Good. It's decent for summarizing and giving me bullet points and explaining things like I'm 5, makes it easy to code things that I don't want to code or spend time figuring out how to do with new languages, other than that, I see no real world applications outside of listening to burger king orders and putting them on a screen for people to make them. Simple support requests, and of course making buzzword-esque documents that you can feed in to a deck-maker for presentations and stuff.
All in all, it helps assist us in new ways. Had somebody take a picture of a car part that had no markings and it identified it, found the maker/manufacturer/SKU and gave all the details etc. That stuff is useful.
But now we're looking at in-authentic stuff. Artists, writers being plagiarized, job cuts (for said marketing/pitches, BS presentations to downsize teams). It's not just losing its hype, its losing any hype in building humanity for the better. It's just more buzzwords, more 'glamour' more 'pop' shoved in our faces.
The layoffs aren't looking pretty.
Works well to help us code though. Viva, sysadmins unite.
Im really hoping that when this hype cycle ends and the next AI winter starts that all the generative stuff gets culled but we still see good work and tech using all the other advances (that would be described as “mere” deep learning).
Document embedding from transformers are great and fit into existing search paradigms.
Computer vision and image segmentation is at a level I thought impossible 10 years ago.
Text to speech that sounds natural? I might actually use Siri and Alexa! (Ok, that one might be considered “generative”)
The research never ended. AI money was flooding in, but mostly going directly to Nvidia. If that cash flow turns off there will still be research happening because it was mostly unaffected in the first place.
The hype dying off will be good for literally everybody except the investors. It'll mean fewer people trying to jam it into products as feature bloat where it has no business being, or trying to make it do tasks that it's unsuited for.
The sooner people start to find it boring, the sooner we can stop wasting time on all the hot air and just use the bits that work.
Google is now "a tool" not "the tool" for finding information. Perplexity and Phind do a good job and DDG is there for the privacy angle. In addition to LLMs just giving you the answer you need.
But what about all those organizations that have "Do something with AI" as the goal for the quarter? All those bonuses driving people to somehow add AI to products. All the poor devs that have been told to replace features driven by deterministic code with AI good-enough-ness.
I'm not sure that is realistic anymore. The run of free money is over and I expected that markets are going to be very picky compared to a few years ago
I must have missed the memo. Where could I get the free money? "a few years ago" we had a global pandemic. Are you claiming that markets will be very picky compared to that time?
This supposed “cycle” has been crazy it’s been about 1.5 years since gpt4 came out, which is really the first generally capable model. I think a lot of this “cycle” is media’s wishful thinking. Humans, especially humans in large bureaucracies, just don't move this quickly. Enterprises have barely had time to dip their toes in.
For what it’s worth hype doesn’t mean sustainability anyway. If all the jokers go onto a new fad it’s hardly the skin off the back of anyone taking this seriously, they’ve been through worse times.
Large and small, entire development teams are completely unaware of the basics of “prompt engineering” for coding, and corporate has an entirely regressive anti-AI policy that doesnt factor in the existence of locally run language models, and just assumes ChatGPT and cloud based ones digesting trade secrets. People arent interested in seeing what the hype is about, and are disincentived from bothering on a work computer. I’m on one team where the Engineering Manager is advocating for Microsoft CoPilot licenses, as in, its a concept that hasnt happened and needs buy in to even start considering.
I would say most people really haven't looked into it. Work is work, the sprint is the sprint, on to the next part of the product, rinse repeat. Time flies for those people, its probably most of the people here.
At the same time I think Meta and big tech adding more and more cloud based inference is driving demand for the processors
OpenAI still hasnt released Sora video prompting for the general public and have already been leapfrogged by half a dozen competitors. I would say its still niche, but only as niche as using professional video editing tools are for creatives
I was getting a lot of mixed messaging at my job for 6-12 months.
On the one hand, we got an email from high up saying not to use Copilot, or other such tools, as they were trying to figure out the licensing. But at the same time, we had the CIO getting up in front of the company every other month talking about nothing but GenAI, and how if we weren’t using it we were stupid (not in those exact words, but that was the general vibe, uncontrolled AI hype).
We were left sitting there saying, “what do you want from us? Do as you say or do as you do?”
Eventually they signed the deal with MS and we got Copilot, which then seemed forced on us. There is even a dashboard out there for it, listing all people using it, their manager, rolling all the way up to the CEO. It tells the percentage of reports from each manager using it, and how often suggestions are accepted. It seems like the kind of dashboard someone would make if they were planning to give out bonuses based on Copilot adoption.
I’ve gotten regular surveys about it as well, to ask how I was using it. I mostly don’t, due to the implementation in VS Code. I might use it a few times per month at best.
Maybe that would be different if the rollout wasn’t so awkward, or the VS Code extension was more configurable.
> It tells the percentage of reports from each manager using it, and how often suggestions are accepted. It seems like the kind of dashboard someone would make if they were planning to give out bonuses based on Copilot adoption.
That's one result. Another result is, due to "checking how often suggestions are accepted" is to objectively record how much help it is providing.
I assume the sitewide license is costly - this could simply be the company's way of determining if the cost is worth it.
Go back ten+ years, replace AI with cloud and it was the same. I saw ‘no cloud’ policies everywhere. But most of the anti-cloud people have since retired, so even the most hidebound of organisations are adopting it. It will probably take another round of retirements for AI to adopted in the more conservative environments.
> It will probably take another round of retirements for AI to adopted in the more conservative environments.
If that is the case, then the AI isn't really adding enough value.
I mean, if it was adding enough value, those companies refusing to adopt it will be out-competed before the next round of retirements, and so won't even be around.
We'll see how the landscape looks in 10 years: if there are still major companies who have not invested some money or time into sprinkling the AI over their operations, then that's a signal that the positive impact of AI was overblown.
If, OTOH, there exists no large company in 10 years who have not incorporated AI into their operations in any way, then that's a signal too - the extra value of AI is above the cost of adding it to the operations.
I’ve been hearing about the AI bubble being about to pop for more than decade now. And then just a couple of years ago AI took a huge leap…so now another AI winter is even more likely?
I think we will see something similar to the last boom with Neural Net chatbots back in 2014(?).
Public discource will simmer down, as current language models either fizzle out or improve. Some will become background noise as the models gets integrated into search engines or leads to large scale lawsuits.
Unlike the previous neural nets though, those models have an actual tangible use. So you will see them around a lot more.
It's different though. Did we have the ubiquitous implementation of nuclear power in only its nascent early days so widespread? To assume it will peter out the same way assumes the metaphor is 1:1, when I brought it up to imply simply that since a lot of developments are happening in secret now and companies are delaying releases for safety reasons, to the public is appears as if things are slowing. Even so, AI models are still far better and cheaper now than they were simply a year ago. We have simply gotten used to breakthroughs.
Cars, planes, house hold appliances, engineering projects where all going to make use of fission. Energy would be revolutionized. Yes, we made large, expensive power plants and had a nuclear arms race. No, we didn't end up powering everything else with nuclear fuel, as you can see from climate change.
Whether and to what extent AI can be monetized is an open question. But there's no question that LLMs are already seeing extensive use in everyday office work and already making large improvements to productivity.
> But there's no question that LLMs are already seeing extensive use in everyday office work and already making large improvements to productivity.
Are you referencing something specific here, or is there something you can link to? To be honest the only significant 'disruption' I've seen for LLMs so far has been cheating on homework assignments. I'd be happy to read something if you have it.
It’s purely anecdotal on my part but I have an ever increasing proportion of nontechnical acquaintances telling me how they discovered they can use ChatGPT to save large amounts of time drafting emails, writing reports, etc. (something which is a major part of work duties for many average office workers).
I and many others are questioning it. Please provide some proof. I've only seen some lazy programmers get boilerplate generated quicker, and some kids cheating on homework. I actually saw executives make use of ChatGPT's text summarization capabilities... until one of them made the critical mistake to fully trust it and flunked an important contract because ChatGPT overlooked something that would be super obvious to a human.
So again, let's see some proof of this extensive use and large improvements to productivity.
The article suggests the contrary: 4.8% use in US companies, down from 5.4%. (I would wish I would have gotten these numbers, but for a tech company founded in 2015 these are not remarkable).
Most of the other 95% of companies that say they aren’t using AI to produce goods or services will still have many employees who’re using LLM services for help drafting emails, documents, reports, etc.
To be honest, I was surprised by ChatGPT. I didn’t think we were close.
We are running out of textual data now to train on… so now they have switched to VIDEO. Geez now they can train on all the VIDEOS on the internet.
And when they finally get bots working, they will have limitless streams of TACTILE data…
Writing it off as the next fad seems fun. But to be honest, I was shocked by what openai did the first time. So they have my respect. I don’t think many of us saw it coming. And I think writing their creativity off again may not be wise.
So when they say the bubble is about to break… I get it. But I don’t see how.
I hardly ever pay for anything.
But I gladly spend money on ai to get the answers I need. Just makes my work work!
Also I would say the economic benefit of this tech for workers is that it will 2x the average worker as they catch on. Seriously I am a 2x coder compared to what I was because of this.
Therefore if me a person who hardly ever spends money has to buy it… I think eventually all businesses will realize all their employees need it. This driving massive revenue for those who sell it.
You shouldn't be. For code bases where context is mostly local they destroy human throughput by comparison. They only fall down hard when used in spaghetti dumpster fire codebases where you have to paste the contents of 6+ files into context or the code crosses a multiple service boundaries to do anything.
A competent engineer architects their systems to make their tools as effective as possible, so maybe your idea of competent is "first order" and you need higher order conception of a good software engineer.
could you provide some examples, code or repos and questions where it does a good job for you (question can also just be completion). Obviously you're having really good experiences with the tools that other aren't having. I'd definitely appreciate that over a lot of assurances taht I'm doing it wrong with my spaghetti code.
Take a SQL schema, and ask AI to generate crud endpoints for the schema, then sit down and code it by yourself. Then try generating client side actions and state management for those endpoints. Time yourself, and compare how long it takes you. Even if you're fast and you cut and paste from template work and quickly hand edit, the AI will be done and on a smoke break before you're even a quarter of the way through.
Ask the AI to generate correctly typed seed data for your database, using realistic values. Again, the AI will be done long before you.
Try porting a library of helper functions from one language to another. This is another task where AI will win handily
Also, ask AI to write unit tests with mocks for your existing code. It's not amazing at integration tests but with mocks in play it slays.
Your experience mirrors mine. I use ChatGPT or meta for boiler plate code like this. I write golang at my day job, and there's a lot of boiler plat for that language, saves a lot of time, but most importantly, does the tedious boring things I hate doing.
Why does no one uses snippets or create scaffold for projects? My main requirement for a tool is to be deterministic. So that I don't spend time monitoring it as failure is very distinct from success.
Most of the things you list can be done deterministically, without the risk of AI errors. The first one in particular is just scaffolding that Visual Studio has had for Entity Framework and ASP.NET MVC for a decade now. And if you were using, e.g., LISP, you'd just write a DEFCRUD macro for it once, and re-use it for every similar project.
Those things are a tiny part of the work though and are all about generating boilerplate code. Tons of boilerplate code isn't the hallmark of a great codebase, I don't think many programmers spends more than 10% of their time writing boilerplate code, unless they work at a very dysfunctional org.
It is true it is faster than humans at some tasks, but the remaining tasks were most of the time, you can't gain more than 11% speedup by speeding up 10% of the work.
What's your point? The other person is speeding themself up, and it works for them. What's the appropriate bar for speedups. What would be enough to satisfy you? What problems do you have that AI isn't speeding up that you still feel aren't worth spending your brain on?maybe list them and see if the other people has thoughts on how to go about it?
Things don't move forward by saying it can't be done or belittle others accomplishments.
Thank you. That was a lot more informative than the argument you were having. And it's now obvious to me the value you're getting and a few areas that would work for me that I'll try. I'm not slapping out crud apps day to day, but can see how I could accelerate myself.
> They only fall down hard when used in spaghetti dumpster fire codebases where you have to paste the contents of 6+ files into context or the code crosses a multiple service boundaries to do anything.
So humans do better than them in at least 80% of all code everywhere, if not 95% even? Cool, good to know.
Care to provide some examples to back your otherwise extraordinary claim btw?
I don't "hate" AI because (1) AI does not exist so how can I hate something that doesn't exist? And (2) I don't "hate" a machine, I cringe at people who make grand claims with zero proof. Yes. Zero. Not small, not infinitesimal -- zero.
I just can't make peace with the fact that I inhabit the same planet as people who can't make elementary distinctions.
You failed to mention the word "can", which is a theoretical.
We can all Google stuff because internet is big and supports anyone's views, which means it's more important than ever to be informed and be able to infer well. Something that you seem to want to defer to sources that support your beliefs. Not nice finding that on a hacker forum but statistical outliers exist.
Live long and prosper. And be insulted, I suppose.
I use multiple daily and have definitely seen a productivity boost. If nothing else, it saves typing. But I'd argue they are in essence a better search engine - it answers "you don't know what you don't know" questions very well, providing a jumping off point when my conception of how to achieve something with code or tooling is vague.
Typing is, or at least it should be, the least of your time spent during the day doing programming. I don't find optimizing the 5-10% of my workday spent typing impressive, or even worth mentioning.
Granted there are languages where typing takes much more time, like Java and C# but... eh. They are quite overdue for finding better syntax anyway! :)
I didn't mean typing in the sense of autocomplete, I meant typing in the sense of stubbing out an entire class or series of test cases. It gives me scaffolding to work with which I can take and run with.
Reminds me of The Primeagen quote:
“If copilot made you 10x better, then you were only a 0.1x programmer to begin with”.
As someone who uses ChatGPT and Claude daily, but cancelled my Copilot subscription after a year of use because it intimately just wasn’t that helpful to me and didn’t provide enough benefit over doing it by hand, I kind of sort of agree. Maybe not entirely, but I can’t shake the feeling that there might be some truth in it.
The code that AI generates for me is rarely good. It’s possible to get good code out of it, but it requires many iterations of careful review and prompting, but for most cases, I can write it quicker by hand. Where it really shines for me in programming and what I still use ChatGPT and Claude for is rubber ducking and as an alternative to documentation (eg “how do I do x in css”).
Besides the code quality being mediocre at best and outright rubbish at worst, it’s too much of a “yes man”, it’s lazy (choose between A and B: why not a hybrid approach? That’s… not what I asked for), and it doesn’t know how to say “I don’t know”.
I also feel it makes you, the human programmer, lazy. We need to exercise our brains, not delegate too much to a dumb computer.
> I also feel it makes you, the human programmer, lazy. We need to exercise our brains, not delegate too much to a dumb computer.
I kinda feel like this isn't talked about enough, my main concern right from the beginning was that new programmers would rely on it too much and never improve their own abilities.
I am a competent coder. I have been a coder since I was in middle school. I know at least 10 languages, and I could write my own from scratch.
I know c++ dart golang java html css javascript typescript lua react vue angular angularjs c# swift sql in various dialects including mysql and postgres, and have worked professionally in all these regards. I love to challenge myself. In fact, if I done something before, I find it boring.
So copilot helps me because I always find something new to do, something I don't understand, something I'm not good at.
So yes, I'm confident I'm competent. But I always do things I'm not good at for fun. So it helps me become well rounded.
So your assertion it only helps me because I'm incompetent is true and false. I'm competent, I just like to do new stuff.
That's all very nice but it contains a fatal logical flaw: it assumes CoPilot actually gives you good code. :D
I mean it does, sometimes, but usually it's either boilerplate or something you don't care about. Boilerplate is mostly managed very well by most well-known IDEs. And neither them nor CoPilot are offering good algorithmic code... OK, I'll grant you the "most of the time, not never" thing.
That’s a different discussion. I’m disputing the claim that AI can make you a 2x dev when it seems like it’s mostly beneficial when you don’t know what you’re doing.
This statement would have been a huge red flag for me, if I had interviewed you. Don't get me wrong, you could use and program in 10 languages. Maybe you can be proficient in many at different times of your life. But know them at once? No.
I think for more boilerplate-esque code-monkey type code it can be a boon.
I think the unfortunate reality is that this makes up a shockingly large amount of software engineering. Take this object and put it into this other object, map this data to that data, create these records and move them into this object when then goes into that other object.
I’ve seen copilot spit out garbage dozens of lines long for something I swore must be one or two stdlib functions. Yep, it was, after reading some real documentation and using my brain. It was some NodeJS stuff, which I never work with. Don’t get me wrong, I still find it a helpful tool, but it is not at all a good, seasoned programmer— it is an algorithm predicting the next token based on the current tokens.
> I still find it a helpful tool, but it is not at all a good, seasoned programmer
How quickly the goalposts move! Two years ago it was "AI will never be able to write code", now we're complaining that "AI is not a seasoned programmer". What are we going to be complaining about, two years from now?
I’m not really complaining, I’m saying it’s useful, but don’t pretend it’s better than what it is. It’s cruise control on a car— makes long drives better, but doesn’t literally drive for you.
How are you using AI to double your coding productivity? Are you using ChatGPT, or Claude, or GitHub Copilot? I am an AI-skeptic, so I am curious here. Thanks!
I've tried various AI coding solutions and have found at best a mild boost but not the amazing multipliers I hear about online.
Copilot gives you some autofill that sometimes can be helpful but often not that helpful. I think the best it did for me was helping with something repetitive where I was editing a big list of things in the same way (like adding an ID to every tag in a list) and it helped take over and finish the task with a little less manual clicking.
ChatGPT has helped with small code snippets like writing a regular expression. I never got 100% regex mastery, usually I would have to look up a couple things to write one but GPT can shortcut that process. I get a little paranoid about AI provided code not actually working so I end up writing a large number of tests to check it, which could be a good thing but can feel tedious.
I'm also curious how other people are leveraging them to get more than I am. I honestly don't try too hard. At one point I did try really hard to get AI to do more heavy code lifting but was disappointed with my results so I stopped... but maybe things have improved a bit since then.
> I get a little paranoid about AI provided code not actually working so I end up writing a large number of tests to check it, which could be a good thing but can feel tedious.
This is a good thing. We need more tests on such critical places like regexes because they can be finicky and non-obvious. Tedious or not, we are not artists; the job must be done. Kudos for sticking to the good practices.
AI is a tool, if you don't know how to use a tool you can't expect to get good results with it. That means both how to interact with the AI and how to structure your code to make the AI's generations more accurate.
I pay for Leetcode, which usually gives editorial examples in Python and Java and such, and paste it into ChatGPT and say "translate this to a language I am more familiar with" (actually I have other programs that have been doing this for some language to language conversions for years, without AI). Then I say "make it more compact". Then again "make it more compact". So soon I have a big O(n) time, big O(1) space solution to Leetcode question #2718 or whatever in a language I am familiar with. Actually sometimes it becomes too compact and unreadable, and I back it up a little.
Sometimes it hallucinates, but it has been helpful. In the past I had problems with it, but not recently.
I’m not the OP and I wouldn’t say that AI has doubled my productivity, but the latest Claude models in particular have made me less of a skeptic than I was a few months ago.
I’m an experienced backend dev who’s been working on some Vue frontend projects, and it’s significantly accelerated my ability to learn the complexities of e.g. Vue’s reactivity model. I can ask a complex question that involves several niche concepts and get a response that correctly synthesizes those concepts. I spent an hour the other night trying to understand a bug in a component to no avail; once I understood the problem well enough to explain it in a few sentences, Claude diagnosed the issue and explained it with more clarity than the documentation and various stack overflow answers.
My default is no longer to assume that the model has a coin flip’s chance of producing bs. I still verify and treat answers with a certain degree of skepticism, but I now reach for it as my first tool rather than a last resort or a gimmick.
Exactly. It’s insanely helpful when u are a dev with experience in another language. You know what you want, you just don’t know the name of the functions, etc. so you put a comment
I want to double tap this point. In my experience Claude out performs GPT-4o, Llama 3.1 and Gemma 1.5 significantly.
I have accounts for all three and will generally try to branch out to test them with each new update. Admittedly, I haven’t gotten to Grok yet, but Claude is far and away the best model at the moment. It’s not even close really.
In this case, the manual is rather poor, so a tool that can cobble together an answer from different sections of the documentation plus blog posts and stack overflow is superior to the manual.
Ok I jumped on copilot when it first came out so I have been using it for a long time.
Since I have been using it so long, I have a really good intuition of what it is “thinking” in every scenario and a pretty good idea of what it can do for me. So that helps me get more use out of it.
So for example one of the projects I’m doing now is a flutter project - my first one. So I don’t remember all the widgets. But I just write a comment:
// this widget does XYZ
And it will write something that is in the right direction.
The other thing it knows super well is like rote code, and for context, it reads the whole file. So like Dart, for example is awful at json. So you have to write “toMap” for each freaking class where you do key values to generate a map which can be turned into json. Same goes for fromMap. So annoying.
But with copilot? You just write “toMap” and it reads all your properties and suggests a near perfect implementation. So much time saved!
I don't think you need an LLM just to parse class properties and turn them into a map. Not that familiar with Dart, but that's the kind of thing IDEs have been able to do for a while now just by parsing syntax the old-fashioned way.
The thing is, when you dig into the claims many people make when they say that they get a 10x productivity boost using "AI" its usually some basic tasks that either generates boilerplate code or performs a fancy autocomplete and while those are great, in no way it supports their original claim.
I think people just want to be part of the hype and use the cool new technology whenever possible. We've seen this over and over again: Machine Learning, Blockchains, Cryptos, "Big Data", "Micro Services", "Kubernetes", etc.
I just don't think the current design of "AI" will take us there..
> they get a 10x productivity boost using "AI" its usually some basic tasks that either generates boilerplate code or performs a fancy autocomplete and while those are great
And that are just a tiny upgrade over what IDEs can do. When I used Android Studio, the code basically write itself due to the boilerplate surrounding your business logic. And once I got a basic structure down, I feel like I only write 5 to 10 characters each line (less for data types). And the upgrade is both positive and negative at the same time, it boils down to luck to actually get good suggestions.
> Seriously I am a 2x coder compared to what I was because of this.
Isn't the energy consumption of this technology pretty catastrophic? Do you consider the issue of energy consumption so abstracted you don't worry about it? Do you do anything to offset your increased carbon emissions?
They certainly are not providing these services at less than electricity costs. So if you are spending $20 a month on it, they are spending less than that on electricity. It's very low compared to any person in the first world's energy spend.
Common sense, having been around cloud operations for a bit. The big cloud providers run gross margins around 30%. So if openai are using MSFT and getting a "good deal" maybe MSFT only get 20% GM. So, $1 of openai compute costs MSFT say $0.80. Of that cost of providing the service, something like 30-40% goes to electricity. So, lets say the electricity cost is $0.30 for $1 of OpenAI compute. (And that's probably a steelman, I think it's actually more like $0.20. )
There is about zero chance OpenAI are running their service at a 70% negative gross margin.
They would need 250 million GPT Plus $20 subscribers to recoup a $5 billion expense. They're far from that even when we count the free users (which are likely 99% of the user base?)
The math just doesn't work. They're hemorrhaging money as far as I can tell (not counting the Azure computing deal).
We can only guess, but my guess is that inference is still a good chunk of their costs. That's why they're trying to get the mini/turbo models into a usable state.
Even then, training is still an expense. And it's not like you can train and forget. Even if your model is already trained you still need to incorporate new knowledge over time.
isn't the energy consumption of travel, driving, and shipping food to you pretty catastrophic? Do you consider the issue of energy consumption so abstracted you don't worry about it? Do you do anything to offset your increased carbon emissions?
I’m working on getting a place with solar panels. I think that’s important for sustainability, plus who wants to have to be connected to the grid anyway?
Offsetting carbon emissions - what does it even mean? There is no feasible way to remove carbon from the athmosphere as far as I know. Would you care to explain what you mean by that?
I think all this can be true, and we are still in a massive AI bubble that may pop at any moment.
ChatGPT truly is impressive. Nonetheless, i still think most companies integrating "AI" into their products is buzzword bs that is all going to collapse in on itself.
Sure it's not all it's cracked up to be but I sure hope there's a sweet spot where I can run the latest models for a cheap price ($20 / month is a steal), and it doesn't instead crash to the point where they get turned off
These days you can't be a respected news outlet if you don't regularly have an article/post/blog about AI losing hype. Wondering when that fad will reach its peak...
> Since peaking last month the share prices of Western firms driving the ai revolution have dropped by 15%.
NVDA's high closes were $135.58 June 18, down to $134.91 July 10th and $130 close today. It's highest sale is $140.76. So it's close today is 8% off its highest sale ever, and 4% off its highest close ever, not a big thing for a volatile stock. It's earnings are next week and we'll see how it does.
Nvidia and SMCI are the ones who have been earning money selling equipment for "AI". For Microsoft, Google, Facebook, Amazon, OpenAI etc., it is all big initial capital expenditure which they (and the scolding investment bank analysts) hope to regain in the future.
Asking an API to write three paragraphs of text still takes tens of seconds and requires working internet and an expensive data center.
Meanwhile we’re seeing the first of the new generation of on-device inference chips being shipped as commodity edge compute.
When the devices you use every day — cars, doorbells, TV remotes, points-of-sale, roombas — can interpret camera and speech input locally in the time it takes to draw a frame and with low enough power to still give you 10h between charges: then we’ll be due another round of innovation.
The article points to how few parts of the economy are leveraging the text-only API products currently available. That still feels very Web 1.0, for me.
Hilarious. The article tries to go even one step further past the loss of hype, by making an additional argument that ai might not be in a hype cycle at all. Meaning they conjecture that it might not even come out of the trough of disillusion to mass adoption.
> The new crop of intelligent agents are different from the automated devices of earlier eras because of their computational power. They have Turing-machine powers, they take over human tasks, and they interact with people in human-like ways-perhaps with a form of natural language, perhaps with animated graphics or video. Some agents have the potential to form their own goals and intentions. to initiate actions on their own without explicit instruction or guidance, and to offer suggestions to people. Thus, agents might set up schedules, reserve hotel and meeting rooms, arrange transportation, and even outline meeting topics, all without human intervention.
I was out at Defcon this year and it was all about AI this, AI that, AI will solve the worlds problems, AI will catch all threats, blah blah blah blah...
I was at a UX / Usability conference and it was basically the same. Everyone talked about AI here and AI there, but no one had an actual usecase or idea how to incorporate AI in a purposeful way. I can genuinely understand, why people feel that AI is a fad.
I work with people like this. The least skilled, least experienced, least productive people on my team constantly recommend “AI” solutions that are just a waste of time.
I think that’s what people like about AI, it’s hope, maybe you won’t have to learn anything but still be productive. Sounds nice ?
It’s certainly possible that AI is being overhyped, and I think in some cases it definitely is - but being tired of hearing about it in no way correlates to its actual usefulness.
In other words, lot of people seem to think that human attention spans are what determine everything, but the technological cycles at work here are much much deeper.
Personally I have used Midjourney and ChatGPT in ways that will have huge impacts on many activities and industries. Denying that because of media trendiness about AI seems shortsighted.
> It’s certainly possible that AI is being overhyped, and I think in some cases it definitely is - but being tired of hearing about it in no way correlates to its actual usefulness.
Please tell that to all types on HN who downvote anything related to Rust without even reading past the title. :D
> In other words, lot of people seem to think that human attention spans are what determine everything, but the technological cycles at work here are much much deeper.
IMO no reasonable person denies this, it's just that the "AI" technology regularly over-promises and under-delivers. At one point it's no longer discrimination, it's just good old pattern recognition.
> Personally I have used Midjourney and ChatGPT in ways that will have huge impacts on many activities and industries. Denying that because of media trendiness about AI seems shortsighted.
Some examples with actual links would go a long way. I for one am skeptical of your claim but I am open to have my mind changed (f.ex. my CFO told me once that ChatGPT helped him catch several bad contract clauses).
I don't understand how someone could think that ChatGPT or Midjourney aren't going to radically change many, many industries, and frankly to think this just seems like straight up ignorance or laziness. It's not that hard to find real examples of this stuff.
But if you insist...here are two very small examples from my personal experience with AI tools.
1. I work as a technical writer. Recently I needed to add a summary section to the introduction of a large number of articles. So, I copied the article into ChatGPT and told it to summarize the piece into 3-4 bullet points. Were I doing this task a few years ago, I would have read each article and then written the bullet points myself – nothing particularly difficult, but very time-consuming to do for dozens of articles. Instead, I used ChatGPT and saved myself hours upon hours of mundane work.
This is a quite minor and mundane example, but you can (hopefully) see how this will have major effects on any kind of routine text-creation.
2. I am working on a side project which requires the creation of a large number of custom images. I've had this project idea for a few years, but previously couldn't afford to spend $20k hiring an illustrator to make them all. Now with Midjourney, I am able to create essentially unlimited images for $30-100 a month. This new AI tool has quite literally unlocked a new business idea that was previously inaccessible.
Responding emotionally by using words like "ignorance" and "laziness" undermines any argument that you might think you are making.
Have you considered that you getting almost angry at somebody "not seeing the light" means you might hold preconceived notions that might not hold to reality? You would not be practicing critical thinking if you are not willing to question your assumptions.
It seems your assumption is very standard: "revolution is just around the corner, how can you not see it?".
OK, let the revolution come and I'll apologize to you personally. Ping me when it happens. For real. But make sure it's an actual revolution and not "OMFG next Midjourney can produce moon-scapes!", okay?
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RE: 1, cool, I heard such success stories and I like them. But I also heard about executives flunking contracts because they over-relied on ChatGPT to summarize / synthesize contract items. I am glad it's making progress but people are being people and they will rely on a 100% fault-free AI. If that's not in place yet then the usefulness drops sharply because double-checking is even more time-consuming than doing the thing by yourself in the first place.
RE: 2, your side projects are not representative of anything at all. And I for one recognize AI images from a mile away and steer clear of projects that make use of them. Smells like low-effort to me and makes me wonder if the author didn't take other, much more fatal, shortcuts (like losing my information or selling my PII). And yes I am not the only one -- before you attempt that low-effort ad hominem technique.
I was not convinced by your comment, very little facts and it mostly appeals to the future that's forever just around the corner. Surely as an engineering-minded person you see how that's not convincing?
You asked for examples, and I gave you examples. I didn't claim AI revolution was around the corner, I just said I used them in these small ways that clearly will have big impacts in their respective areas.
My experience is in no way unique, and yes, I think it's just laziness or ignorance to think otherwise. Or in your case, a kind of zealous hostility as a reaction against hype.
I remind you that my initial comment said that yes, there are some aspects of AI that are definitely over-hyped, but that I have used the tools in ways that obviously seem to have huge economic impacts.
P.S. - if you were more familiar with AI image makers, you'd know that it's not difficult to make images that are indistinguishable from non-AI ones. But that's really not relevant here, because my point was that this new tool enabled me to create something that didn't exist before – not what your personal qualms were about AI images.
If the stick enables you carve more complex things in a cheaper way than the previous tool, then yes, the stick is special. Unless you’re suggesting that a pencil and the human finger are exactly the same?
This is quite literally the entire history of technology: what was once expensive becomes cheap and then unlocks new developments. Bizarre that I have to point this out when you’re likely reading this comment on a device made of commoditized components that cost a fraction of what they did a couple decades ago.
> If the stick enables you carve more complex things in a cheaper way than the previous tool, then yes, the stick is special
Yeah it does that, in 2-3 areas, those we need the least -- who cares it can replace artists? We need elder people care! We need automated logistics! And a tons of other things.
"It's just the beginning" yeah yeah, but it's not. It's actually the next AI plateau that the area will need a long time to move on from. Please do quote me on this, I am willing to apologize if I am wrong after 5-10 years.
I've trained as a neuroscientist and written a book about consciousness. I've worked in machine learning and built products for over 20 years and now use AI a fair bit in the ed-tech work we do.
So I've seen how the field has progressed and also have been able to look at it from a perspective most AI/engineering people don't -- what does this artificial intelligence look like when compared to biological intelligence. And I must say I am absolutely astonished people don't see this as opening the flood-gates to staggeringly powerful artificial intelligence. We've run the 4-minute mile. There are hundreds of billions of dollars figuring out how to get to the next level, and it's clear we are close. Forget what the current models are doing, it is what the next big leap (most likely with some new architecture change) will bring.
In focusing on intelligence we forget that it's most likely a much easier challenge than decentralized cheap autonomy, which is what took the planet 4 billion years to figure out. Once that was done, intelligence as we recognize it took an eye-blink. Just like with powered-flight we don't need bioliogical intelligence to transform the world. Artificial intelligence that guzzles electricity, is brittle, has blind spots, but still capable of 1000 times more than the best among us is going to be here within the next decade. It's not here yet, no doubt, but I am yet to see any reasoned argument for why it is far more difficult and will take far longer. We are in for radical non-linear change.
> but I am yet to see any reasoned argument for why it is far more difficult and will take far longer
I am yet to see any reasoned argument for why it is easy to build real AI and that it will come fast.
As you said, AI has been there for decades and stagnated for pretty much the whole time. We've just had a big leap, but nothing says (except BS hype) that we're not in for a long plateau again.
Argument from authority is a pernicious fallacy, and typically effective too. You were right to call it out. I must admit I overlooked the sarcasm, however.
> I really dislike it when people have to first convince you that you should trust them.
> Either make a good argument or don’t.
Human beings can't evaulate the truth of things based only on the argument. Persuasive liars, cons, and incompetents are a very known phenomenon. Most of human history we misunderstood nature and many other things because we relied on 'good arguments'. Not that we need it, but research shows that human intuition about the truth of something isn't good without expertise.
When I need medical advice, I get it from someone who has convinced me that they have expertise; I don't look for 'good arguments' that persuade me, because I don't know what I'm talking about.
I have expertise in other things. In those fields, I could easily persuade people without it of just about anything. (I don't; I'm not a sociopath.) I imagine anyone with professional expertise who reads this can do the same.
> I am yet to see any reasoned argument for why it is easy to build real AI and that it will come fast.
We have "real ai" already.
As for future progress, have you tried just simple interpolation of the progress so far? Human level intelligence is very near. (Though of course artificial intelligence will never exactly match human intelligence: it will be ahead/behind in certain aspects...)
- We don't have a "real AI" at all. Where's Skynet, where's HAL-9000? Where are the cute robotic butlers from the "I, Robot" movie?
- Simple interpolation of the progress is exactly the problem here. Look at the historical graphs of AI funding and tell me with a straight face that we absolutely must use simple interpolation.
- Nope, human-level intelligence is not even close. It remains as nebulous and out of reach as ever. ChatGPT's imitation of intelligent speech falls apart very quickly when you chat with it for more than a few questions.
To be fair, I’ve talked to a lot of people who cannot consistently perform at the mistral-12b level.
I think we expect AGI to be much smarter than the average joe, and free of occasional stupidity.
What we’ve got is an 85IQ generalist with unreliable savant capabilities, that can also talk to a million people at the same time without getting distracted. I don’t see how that isn’t absolutely a fundamental shift in capability.
It’s just that we expect it to be spectacularly useful. Not like homeless joe, who lives down by the river. Unfortunately, nobody wants a 40 acre call center of homeless joes, but it’s hard to argue that HJ isn’t an intelligent entity.
Obviously LLMs don’t yet have a control and supervision loop that gives them goal directed behaviour, but they also don’t have a drinking problem and debilitating PTSD with a little TBI thrown in from the last war.
It’s not that we aren’t on the cusp of general intelligence, it’s that we have a distorted idea of how useful that should be.
> What we’ve got is an 85IQ generalist with unreliable savant capabilities, that can also talk to a million people at the same time without getting distracted. I don’t see how that isn’t absolutely a fundamental shift in capability.
Very shallow assessment, first of all it's not a generalist at all, it has zero concept of what it's talking about, secondly it gets confused easily unless you order it to keep context in memory, and thirdly it can't perform if it does not regularly swallow petabytes of human text.
I get your optimism but it's uninformed.
> To be fair, I’ve talked to a lot of people who cannot consistently perform at the mistral-12b level.
I can find you an old-school bot that performs better than uneducated members of marginalized and super poor communities, what is your example even supposed to prove?
> it’s hard to argue that HJ isn’t an intelligent entity.
What's HJ? If it's not a human then it's extremely easy to argue that it's not an intelligent entity. We don't have intelligent machine entities, we have stochastic parrots and it's weird to pretend otherwise when the algorithms are well-known and it's very visible there's no self-optimization in there, there's no actual learning, there's only adjusting weights (and this is not what our actual neurons do btw), there's no motivation or self-drive to continue learning, there's barely anything that has been "taught" to combine segments of human speech and somehow that's a huge achievement. Sure.
> It’s not that we aren’t on the cusp of general intelligence, it’s that we have a distorted idea of how useful that should be.
Nah, we are on no cusp of general AGI at all. We're not even at 1%. Don't know about you but I have a very clear idea what would AGI look like and LLMs are nowhere near. Not even in the same ballpark.
It helps that I am not in the area and I don't feel the need to pat myself on the back that I have managed to achieve the next AI plateau which the area will not soon recover from.
Bookmark this comment and tell me I am wrong in 10 years, I dare you.
HJ is Homeless Joe, an inference that a 12b stochastic text generator would not have missed lol. But sure, ill reflect in 10 years.
TBH I hope im wrong, and that there is magic in HJ that makes him special in the universe in a way that GPT26 can never be. But increasingly, I doubt this premise. Not because of the "amazing capabilities of LLMs" which i think are frequently overstated and largely misunderstood, but more because of the dumbfounding shortcomings of intelligent creatures. We keep moving the bar for AGI, and now AGI is assumed to be what any rational accounting would classfy as ASI.
Where we are really going to see the bloom of AI is in goal directed systems, and I think those will come naturally with robotics. I predict we are in for a very abrupt 2nd industrial revolution, and you and I will be able to have this discussion either over a 55 gallon barrel of burning trash, or in our robot manicured botanical gardens sometime in the near future lol.
> Not because of the "amazing capabilities of LLMs" which i think are frequently overstated and largely misunderstood
We have found common ground.
> but more because of the dumbfounding shortcomings of intelligent creatures
Yes, a lot of us utilize defective judgments, myself included, fairly often. My point was that LLMs, for all their praise, can't even reach 10% of an average semi-intelligent organic being.
> We keep moving the bar for AGI, and now AGI is assumed to be what any rational accounting would classfy as ASI.
I don't know who is "we" (and I wish people stopped pretending that "we" are all a homogenous mass) but I've known what an AGI should be ever since I've watched movies about Skynet and HAL-9000. ¯\_(ツ)_/¯
Secondly, it's the so-called "AI practitioners" who constantly move the goal posts (now there's "ASI"? -- you know what, I actually don't want to know) because they're periodically being called out and can't hide the fact that they have nearly nothing again. So what's better than obfuscating that fact by having 100+ acronyms? It's a nice cover and apparently there are still investors who are buying it. I get it, we have to learn to say the right things to get funding.
> Where we are really going to see the bloom of AI is in goal directed systems, and I think those will come naturally with robotics.
I agree. Physical feedback is needed if we want an electronic entity to "evolve" similarly to us.
> I predict we are in for a very abrupt 2nd industrial revolution, and you and I will be able to have this discussion either over a 55 gallon barrel of burning trash, or in our robot manicured botanical gardens sometime in the near future lol.
I agree this is 100% inevitable but I don't think it's coming as soon as you say. The LLMs are hopelessly stuck even today and the whole AI area will suffer for it for a while after the bubble bursts... which is the event that I am certain is coming soon.
As a person that has been futzing with "AI" since early expert systems, I can give a little bit of background on the evolution of the term of art 'artificial general intelligence' Back in the days of lisp machines and expert systems, we started playing with early neural networks in an attempt to advance towards 'general intelligence' rather than the highly constrained talents of expert systems.
At that time, general intelligence was imagined like what a mouse, dog, or cat has, in varying degrees. (it was then broadly thought that insects and other "simple" organisms worked from instinct and conditioning only) So intelligence was broadly imagined to be the ability to reason through an arbitrary problem with a combination of insight and trial and error. It had nothing at all to do with reaching human levels of competence.
Gradually, the bar for AGI has slipped skyward to assume human level competence. I think that part of that was because we actually started checking boxes for tasks that were thought to represent "true" AI....
...But then we recognized that nope, it is not yet generally intelligent, it can just easily and accurately do (X task which was thought to require intelligence but really was only pattern matching or stochastic prediction). So, we raise the bar above task X and trivialize it as being less important than we thought.
Meanwhile, -nearly every task- that we thought would represent "true intelligence" has fallen not to some magic AI algorithm, but rather to stochastic models like transformers, pattern matching, or straightforward computation. With no reason to expect otherwise, I expect this trend to continue unabated.
So, what I'm saying is I have come to doubt that there is some secret sauce in "intelligence", but instead believe that "intelligence" is a blend of competencies enabled in animals by specialization of neural computational structures, "REPL" loops, goal seeking behaviors, and other tidbits which will be more of a slog than a eureka. I don't think that LLM's will create human-level intelligence on their own, but I do think that they will be an important component.
I also think that surprisingly, transformers all by them selves exhibit a flaky variety of general intelligence, and they are proving effective in robotic applications (though without a supervisory agent I suspect they will frequently go off the rails, so to speak.)
My original point was more that we expect machine general intelligence to be spectacularly useful. It may be, someday, but it is a kind of fallacy to think that "its not that useful therefore it must not really be intelligence".
Small animals also have limited utility, but with given names, language, tool use, and problem solving skills I think arguing that they do not exhibit "intelligence" would be a tough sell for me.
By my observation, we have made giant leaps in the past 15 years, and we now have perhaps the vast majority of the components required to make artificial general intelligence...but it won't necessarily be all that useful at first, except maybe as a "pet robot" or something like that. Even if we scale it, it might not magically get smarter, just faster. a million hyper-speed squirrels still has a very limited level of utility.
From there, we will incrementally improve if we don't stumble too hard over ourselves on any number of pressing obstacles that we currently face, until we finally succeed in doing what we were apparently born to do - to construct the means of our own irrelevance. Evolution at work, I suppose.
Thank you for following up. I'll pedantically respond point by point, hopefully that will not make you bow out.
> Back in the days of lisp machines and expert systems, we started playing with early neural networks in an attempt to advance towards 'general intelligence' rather than the highly constrained talents of expert systems.
Interesting, you must be the first person I "meet" who was "back then and there". Still, if you allow me to point out for the second time, your "we" is really throwing me off because it sounds like "we the council of elders" and "we who self-appointed to determine what an actual true AI is". Positions of implied or directly claimed authority murder my motivation to take people doing them seriously. Hopefully that's a useful piece of feedback for you.
I would think that a random guy like myself who watched Terminator and that was part of his inspiration to become a programmer has just as much "authority" (if we can even call it that but I can't find the right word at this moment) to claim what a general AI should be. Since we don't have it, why not try and dream the ideal AI for us, and then pursue that? It's what the people who wanted humanity on the Moon did after all.
I feel too many people try to define general AI through the lenses of what we have right now -- or we'll have very soon -- and that to me seems very short-sighted and narrow-minded and seems like bronze-age people trying to predict what technology would be. To them it would likely be better carts that shake less while sitting in them. And faster cart-pulling animals.
That's how current AI practitioners trying to enforce their view on what we should expect sound to me.
> Meanwhile, -nearly every task- that we thought would represent "true intelligence" has fallen not to some magic AI algorithm, but rather to stochastic models like transformers, pattern matching, or straightforward computation. With no reason to expect otherwise, I expect this trend to continue unabated.
Sure, but this gets dangerously close to the disingenuous argument of "people who want AI constantly move the goalposts every time we make progress!" which is a stance I can't disagree with more even if I tried. I in fact hate this trope and fight it at every opportunity.
Why? Because to me that looks like AI practitioners are weaseling out of responsibility. It's in fact not that difficult to understand what the common people would want. Take a look at the "I, Robot" movie -- robot butlers that can do many tasks around the house or even assist you when you are outside.
What does that take? Let the practitioners figure it out. I, like yourself, believe LLMs are definitely not that -- but you are also right that it's likely a key ingredient. Being able to semi-informedly and quickly digest and process text is indeed crucial.
The part I hate is the constant chest-pounding: "We practically have general AI now, you plebs don't know our specialized terms and you just don't get it. Stop it with your claims that we don't have it! Nevermind that we don't have robot butlers, that's always in the future, I am telling you!".
And yes that happens even here in this thread, not in that super direct form of course, but it still happens.
> My original point was more that we expect machine general intelligence to be spectacularly useful. It may be, someday, but it is a kind of fallacy to think that "its not that useful therefore it must not really be intelligence".
Here we disagree. It's true that people want useful and don't care how it's achieved; as a fairly disgruntled-by-tech guy I want the same. Put rat brains in my half-intelligent but problem-solving butler for all I care; if it works well people will buy it en masse and ask zero questions.
...But I'd still put strong correlation between a...
Point taken on the “we” , it can sound patronising in the wrong light. Just think of that meaning “my immediate colleagues and I” - it is not to mean a universal or authoritative we, rather an anecdotal plurality.
It’s possible that others imagined human level as the base for “generalized intelligence”, but my colleagues and I were taking the term general to mean generalised, as in not narrowly defined (like expert systems are). A type of intelligence that could be applied broadly to different categories of problems, including ones not foreseen by the designer of the system. That these problems might be very basic ones was immaterial.
That this concept of general intelligence is not necessarily life transforming to possess on your smartphone doesn’t mean it isn’t a huge step forward. It is a very hard problem to solve. Transformer networks are the best tool we have for this task, and they work by inferring meaning to patterns and outputting a transform of that meaning. With LLMs, the pattern is the context text string, and the output is the next likely text fragment.
The surprising effectiveness of LLMs is due, I think, not to any characteristic of their architecture that makes them “intelligent”, but rather due to the fundamental nature of language itself, and especially the English language because of its penchant for specificity and its somewhat lower reliance on intonation to convey meaning than most languages. (I’m not a linguist, but I have discussed this with a few and it is interesting to hear their ideas on this)
Language itself captures meanings far beyond the words used in a statement. Only a tiny fraction of information is contained in words, the rest is inferred knowledge based on assumptions about shared experiences and understandings. Transformers tease out this context and imbed it through inferences constructed by billions of textual inputs. They capture the information shadows cast by the words, not just the words themselves.
This way of decoding cultural data, as an n-dimensional matrix of vast proportions rather than just the text of a culture itself, turns out to be a way to access both the explicit and the implicit knowledge imbedded in that culture, especially if that culture is codified using very specific and expressive tokens.
It turns out that this capture of shared experiences and understanding enables a great deal of abstract general problem solving, precisely the kind of problem solving that is vexingly difficult to solve using other methods.
LLMs, not just transformers, are actually a really big deal. In effect, they create a kind of probabilistic expert system where the field of expertise is a significant fraction of the sum total of human thought and experience.
But there are numerous and significant shortcomings to this approach, of course…. Not the least of which is the difficulty to effectively integrate new information or to selectively replace or update existing data in the model. And hallucinations (which are not a malfunction , but rather completely normal operations) are a basically unsolvable problem, though they can be mitigated.
But anyway…
I think the real insight to be had here , or at least my personal takeaway is that we owe a great deal of what we think of as intelligence to our culture than to our individual intellectual prowess. As we solve the problems of general intelligence, we both construct marvellous machines and confront the suggestion that we aren’t nearly as clever as we thought we were.
As for “AGI” I will still call that a crossed threshold , but certainly not to the level that humans have solved “biological GI” with full inculturation.
If people want AGI to mean human level competence, I’m ok with that. It won’t be the first term of art to have drifted in meaning. I would personally choose a more descriptive term for that, but 3 letter terms are not all that expressive, and adding another letter or two just gets awkward, so I get it. And I think it’s a lost battle anyway, it’s mostly old...
Nobody knows what intelligence actually is. But asking this philosophical question and your discussion opponent not having a clear answer is a very obvious discussion trap and a discussion shut-down and it does NOT immediately follow that your claim -- "we have AI / AGI" -- becomes automatically true. It does not.
And I am pretty sure my own intelligence goes much farther than regurgitating text that I have no clue about (like ChatGPT does not have symbol logic that links words with objects it can "feel" physically or otherwise).
I don't think they'll use LLM's for customer service.
But it's a building block. And when used well it may be possible to get to zero hallucinations and good accuracy in question answering for limited domains - like the call center.
Why shouldn't I? Humans developed this intelligence naturally (as far as we know). Many people claim we're intelligent enough to repeat the process with artificial organisms, and guide it, and perfect it. I want to see it.
And btw in the Terminator novelizations it was clearly stated that Skynet was a very good optimization machine but lacked creativity. So it's actually a good benchmark: can we create an intelligent machine that needs no supervision but still has limitations (i.e. it cannot dramatically reformulate its strategy in case it cannot win, which is exactly what happened in the books)?
Just because someone can tell a convincing story, doesn’t mean reality (once technology catches up) will resemble the devices of that story. Science fiction is fiction, and unconstrained by the annoying restrictions of physical reality.
That’s the point of my flying car comparison. We HAVE flying cars: they’re called helicopters. Because as it turns out there is just no physical way to make a vehicle in the form factor of a car fly, except by rotary wing. But people will still say “where’s my flying car?” because they are hung up on reality resembling science fantasy, as you are.
We have AI. We even already have AGI. It just doesn’t resemble the Terminator, because The Terminator is a made up story disconnected from reality.
And this is why, I feel, I can never discuss with the AI fans. They are happy to invent their own fiction while berating popular fiction in the same breath.
No, we really don't have AGI. Feel free to point out some of humanity's pressing problems being trivially solved today with it, please. I'll start: elderly people care, and fully automated logistics.
The term, as originally defined, is for programs which are man-made (Artificial), able to efficiently solve problems (Intelligence), including novel problem domains outside those considered in its creation (General). Artificial General Intelligence, or AGI. That’s literally all AGI means, and ChatGPT absolutely fits the bill.
What you describe is ASI, or artificial super intelligence. In the late 90’s, 00’s, and early 10’s, a weird subgroup of AI nerds got it into their head that merely making an AGI (even a primitive one) would cause a self-recursion improvement loop and create ASI in short order. They then started saying “achieve AGI” as a stand in for “emergence of ASI” as the two were intricately linked in their mind.
In reality the whole notion of AGI->ASI auto-FOOM has been experimentally discredited, but the confusion over terminology remains.
Furthermore, the very idea of ASI can’t be taken for granted. A machine that trivially solves humanity’s pressing problems makes nice sci-fi, but there is absolutely no evidence to presume such a machine could actually exist.
You are addressing the wrong person if you think I give two hoots how many more acronyms will the AI area invent to conceal the fact that the only thing they actually achieved is remove a lot of artists from the job market.
I don't care how it's called. We don't have it. I am not "confused over terminology", I want to see results and yet again they don't exist. Let's focus on results.
> In reality the whole notion of AGI->ASI auto-FOOM has been experimentally discredited
Sure. Because we actually have this super-intelligence already and we can compare with it, right? Oh wait, no we don't. So what's your point? Some people gave up and proclaimed that it can't be done? Like we haven't seen historical examples of this meaning exactly nothing, hundreds of times already.
Look, we'll never be able to talk about it before you stop confusing industry gate-keepers who learned how to talk to get VC money and obfuscate reality with, you know, the actual reality in front of us. You got duped by the investor talk and by the scientists never wanting to admit their funding might have been misplaced by being given to them, I am afraid.
Finally, nope, again and again, we don't have AGI even if I accept your definition. Show me a bot that can play chess, play StarCraft 2, organize an Amazon warehouse item movements and shipping, and coordinate a flight's touch-down with the same algorithms / core / whatever-you-want-to-call it. Same one, not different ones. One and the same.
No? No AGI then either.
> Furthermore, the very idea of ASI can’t be taken for granted. A machine that trivially solves humanity’s pressing problems makes nice sci-fi, but there is absolutely no evidence to presume such a machine could actually exist.
The people in the bronze age could have easily said "there is no evidence we would be able to haul goods while only pressing pedals and rotating a wheel". That's not an argument for anything at all, it's a short-sighted assertion that we might never progress that's only taking the present and the very near future into account. Well, cool, you don't believe it will happen. And? That's not an interesting thing to say.
Other people didn't believe we could go to the Moon. We still did. I wonder how quickly the naysayers hid under the bed after that so nobody could confront them about it. :D
But anyway. I got nothing more to say to people who believe VC talk and are hell-bent on inventing many acronyms to make sure they are never held accountable.
I for one want machines that solve humanity's problems. I know they can exist. I know nearly nobody wants to work on them because everybody is focused on the next quarter's results. All this is visible and well-understood yet people like you seem to think that this super narrow view is the best humanity can achieve.
Well, maybe it's the best you can achieve. I know people who can do more.
In the novelizations it was written that Skynet could not adapt to humans not running away or not vacating territories after they have been defeated. One of the quotes was: "Apparently it underestimated something that it kept analyzing even now: the human willpower." I've read this as Skynet not being able to adapt against guerilla warfare -- the hit-and-run/hide tactics.
But the TL;DR was that Skynet was basically playing something like StarCraft as if it played against another bot, and ultimately lost because it played against humans. That was the "Skynet was not creative" angle in the novelizations.
In Terminator 1 Skynet looses because John Conner taught people how to fight the machines, but John Conner only knows this because Kyle Reese taught Sarah Conner how to fight the machines and she taught John Conner. But Kyle Reese only knows this because he was taught by John Conner- so there's no actual source of the information on how to fight the machines, it's a loop with no beginning or end.
I had a philosophy teacher who said this is evidence of divine intervention to destroy Skynet, essentially God told people through John Conner how to win, but a cut scene in Terminator 1 implies Skynet was also created by reverse engineering the chip in the destroyed Terminator- implying there's also no origin of the information on how to create Skynet and it's also an infinite loop.
Yeah, these discussions are fascinating but I'd still think it's not very hard to learn how to blow stuff up and sabotage assembly lines, given enough tries.
So it's not exactly an infinite loop IMO, it's more like that the first iteration was more crude and the machines were difficult to kill but then people learned and passed the information along back in time, eventually forming the infinite loop -- it still had a first step though, it didn't come out of nothing.
We do predictions, but much more important, we are able to create new states. Prediction the classical view assigns probabilities to existing states. What's unique to us and a lot of other biological intelligence is the ability to create new states when needed. This is not implicit in the narrow view of prediction machines
Well, if we intend "building god" perhaps we're merely making a copy of a copy of God:
The Sixth Day
Then God said, “Let Us make man in Our image, after Our likeness, to rule over the fish of the sea and the birds of the air, over the livestock, and over all the earth itself and every creature that crawls upon it.” So God created man in His own image; in the image of God He created him; male and female He created them. ”…
What does this mean, precisely? How is a human body (or plant, or insect, or reptile/bird/mammal) body ever "more than" it's constituent parts? Wouldn't that violate a conservation law?
Nah. We're still not that close. Think of it this way, you turn on an appliance at home and it's what, a 0.0001% chance it will explode in your face? Now automated driving, hospitality etc is all more like a 0.1+% chance something goes wrong still. Huge difference.
I don't really take those taxis as a form of solved automation. It's a nice step though.
Because large, money-rich companies and censored usage is not a proving ground. Amazon "trialled" stores that were automated but ended up being humans. Even without the human factor they weren't proof of successful automation of retail.
> I am yet to see any reasoned argument for why it is far more difficult and will take far longer.
For language models specifically, they are trained on data and have historically been improved by increasing the size of the model (by number of parameters) and by the amount and/or quality of training data.
We are basically out of new, non-synthetic text to train models on and it’s extremely hard work to come up with novel architecture that performs well against transformers.
Those are some simple reasons why it will be far more difficult to improve general language models.
There are also papers showing that training models on synthetic data causes “model collapse” and greatly reduces output quality by magnifying errors already present in the model, so it’s not a problem we can easily sidestep.
It’s an easy mistake to see something like chatgpt not exist, then suddenly exist and assume a major breakthrough happened, but behind the scenes there has been like 50 years of R&D that led to it, it’s not like suddenly there was a breakthrough and now the gates are open.
A general intelligence for CS is like the elixir of life for medicine.
Even assuming there is a ton of data companies are just now getting access to, the logarithmic curve of LLM improvements is clearly visible (granted that our LLM evaluation frameworks are not very good)
There is no guarantee that we will not get stuck with these probabilistic parrots for 50 more years. Definitely useful, definitely not AI.
And by the way I can copy your post character by character, without hallucinating. So I am definitely better than this crop of "AI" in at least one dimension.
In fact, humans will be running at relativistic speeds within this century, risking the total destruction of the Earth if they should ever trip over something and fall down.
Scary stuff. And it's not science fiction- it's based on real, observed trends and scaling laws. Seems impossible? Well, they said the four-minute mile was impossible, too.
While this is true, I think you’re not appreciating the metaphor.
Humankind tried to break the 4 minute mile for hundreds of years - since measuring distance and time became accurate enough to be sure of both in the mid-18th century, at least - and failed.
In May 1954, Roger Bannister managed it. By late June it was done again by a different runner. Within 20 years the record was under 3:45, and today there are some runners who have achieved it more than 100 times and nearly 1800 runners who have done it at all.
Impossible for hundred of years, and then somebody did it, and people stopped thinking it was impossible and started doing it themselves. That’s the metaphor: sometimes we think of barriers that are really mental, not real.
I’m not sure that applies here either, but the point is not that progress is continuously exponential, but that once a barrier is conquered, we take on a perspective as if the barrier were never real in the first place. Powered flight went through this. Computing hardware too. It’s not an entirely foolish notion.
I've worked in AI for the past 30 years and have seen enthusiasm as robust as yours go bust before. Just because some kinds of narrow AI have done extraordinarily well -- namely those tasks that recognize patterns using connections between FSMs -- does not mean that same mechanisms will continue to scale up to human-level cognition, much less exceed it any time soon.
The breakthroughs where deep AI have excelled -- object recognition in images, voice recognition and generation, and text-based info embedding and retrieval -- these require none of the multilevel abstraction that characterizes higher cognition (Kahneman's system 2 thinking). When we see steady progress on such frontiers, only then a plausible case can and should be made that the essentials of AGI are indeed within our grasp. Until then, plateauing at a higher level of pattern matching than we had expected -- which is what we have seen many times before from narrow AI -- this is not sufficient evidence that the other requisite skills needed for AGI are surely just around the corner.
So I am a neophite in this area, but my thesis for why "this time is different" compared to previous AI bubbles is that this time there exist a bunch of clear products (or paths to products) that work and only require what is currently available in terms of technology.
Coding assistants today are useful, image generation is useful, speach recognition/generation is useful.
All of these can support businesses, even in their current (early) state. Those businesses have value in funding even 1% improvements in engineering/science.
I think that this is different than before, where even in the 80s there were less defined products, amd most everything was a prototype that needed just a bit more research to be commercially viable.
Where as in the past, hopes for the technology waned and funding for research dropped off a cliff, today's stuff is useful now, and so companies will continue to spend some amount on the research side.
Just today, I received a note from a gas technician, part handwritten, for the life of me I couldn't make out what he was saying, I asked ChatGPT and it surprisingly understood, rereading the original note I'm very sure it was correct.
Sometimes speech-to-text machine learning models give very good results, however I think the key is that:
1. It's overwhelmingly more useful than the [no text] it was replacing, particularly for the deaf or if you want to search for keywords in a video.
2. When it fails, it tends to do so in ways that trigger human suspicion and oversight.
Those aren't necessarily true of some of the things people are shoehorning LLMs into these days, which is why I'm a lost more pessimistic about that technology.
For some reason, YouTube is not using a very good STT system now. The lack of sentence punctuation is particularly annoying. Transcriptions by Whisper and Gemini 1.5 Pro are much better. From a couple of weeks ago:
> effectively made the jump to coding with AI assistants
I think this depend heavily on what type of coding your doing. The more your job could be replaced by copy/pasting from Stack Overflow, the more useful you find coding assistants.
For that past few years most of the code I've written has been solving fairly niche quantitative problems with novel approaches and I've found AI coding assistants to range from useless to harmful.
But on a recent webdev project, they were much more useful. The vast majority of problems in webdev are fundamentally not unique so a searchable pattern library (which is what an LLM coding assistant basically is) should be pretty effective.
For other areas of software, they're not nearly as useful.
>I think this depend heavily on what type of coding your doing. The more your job could be replaced by copy/pasting from Stack Overflow, the more useful you find coding assistants.
I think this is true and also why you see some "older devs just don't like AI" type comments. AI assistants seem to be great at simple webdev tasks, which also happens to be the type of work that more junior developers do day to day.
I have also found them useful with that and I keep one active for those types of projects because of the speed up, although I still have to keep a close eye on what it wants to inject. They also seem to excel at generating tests if you have already developed the functions.
Then there are more difficult (usually not webdev) projects. In those cases, it really only shines if I need to ask it a question that I would previously have searched on SO or some obscure board for an answer. And even then, it really has to be scrutinized, because if it was simple, I wouldn't be asking the question.
There is def. something there for specific types of development, but it has not "changed my life" or anything like that. It might have if I was just starting out or if I only did webdev type projects.
As an "older dev" who doesn't like AI, the thing that annoys me most is the UX is horrible. It's like arguing in chat with an extremely overconfident junior dev who isn't capable of learning or improving with time and experience. That's just a miserable way to spend time. I'd rather spend that time thinking clearly about the problem, and then writing down the solution (clearly).
If this thing also conferred an actual productivity advantage that would be one thing, and it might motivate me to get past the horrible UX, but I haven't seen any evidence yet.
I fear the approach that maximises productivity is a literal one-shot approach: Give the LLM one or two shots at generating a somewhat passable first attempt (including all or at least most of the boilerplate) and then strictly fix up stuff yourself. I recently spend a day attempting to build a relatively simple GUI for a project which _maybe_ contains a couple of days of programming work. It got the gist of the GUI basically in one. And the next two or three prompts then added the buttons I wanted. Most of it even worked
But after that we ran into a kind of loop, where you put my feelings into much better words than I could. If I had stopped after iteration 3, I probably would have finished what I wanted to do in half a day
See this is exactly what is wrong with “this time it’s different” here. AI has been useful and used for decades (but under a different name because the term was tainted by previous bubbles). Look at the section “AI behind the scenes” here https://en.wikipedia.org/wiki/History_of_artificial_intellig...
“This time is different” in one fundamental, methodological, epistemological way: we test on the training set now.
This has follow-on consequences for a shattering phase transition between “persuasive demo” and “useful product”.
We can now make arbitrarily convincing demos that will crash airplanes (“with no survivors!”) on the first try in production.
This is institutionalized by the market capitalizations of 7 companies being so inflated that if they were priced accurately the US economy would collapse.
There was really one AI winter which is a sample size of 1, saying this time is different is justified based on the exponential improvement over AI 10 years back
Really? I work in AI and my biggest concern is that I don't see any real products coming out of this space. I work closer to the models, and people in this specific area are making progress, but when I look at what's being done down stream I see nothing, save demos that don't scale beyond a few examples.
> in the 80s there were less defined products, amd most everything was a prototype that needed just a bit more research to be commercially viable.
This is literally all I see right now. There's some really fun hobbyist stuff happening in the image gen area that I think is here to stay, but LLMs haven't broken out of the "autocomplete on steroids" use cases.
> today's stuff is useful now
Can you give me examples of 5, non-coding assistant, profitable use cases for LLMs that aren't still in the "needed just a bit more research to be commercially viable" stage?
I love working in AI, think the technology is amazing, and do think there are some under exploited (though less exciting) use cases, but all I see if big promises with under delivery. I would love to be proven wrong.
LLMs can be used to generate high-quality, human-like content such as articles, blog posts, social media posts, and even short stories. Businesses can leverage this capability to save time and resources on content creation, and improve the consistency and quality of their online presence.
2. Customer Service and Support:
LLMs can be integrated into chatbots and virtual assistants to provide fast, accurate, and personalized responses to customer inquiries. This can help businesses improve their customer experience, reduce the workload on human customer service representatives, and provide 24/7 support.
3. Summarization and Insights:
LLMs can be used to analyze large volumes of text data, such as reports, research papers, or customer feedback, and generate concise summaries and insights. This can be valuable for businesses in fields like market research, financial analysis, or strategic planning.
4. HR Candidate Screening:
Use case: Using LLMs to assess job applicant resumes, cover letters, and interview responses to identify the most qualified candidates.
Example: A large retailer integrating an LLM-based recruiting assistant to help sift through hundreds of applications for entry-level roles.
5. Legal Document Review:
Use case: Employing LLMs to rapidly scan through large volumes of legal contracts, case files, and regulatory documents to identify key terms, risks, and relevant information.
Example: A corporate law firm deploying an LLM tool to streamline the due diligence process for mergers and acquisitions.
They're currently already relying on overworked, underpaid interns who draft those documents. The lawyer is checking it anyway. Now the lawyer and his intern have time to check it.
I have no idea what type of law you're talking about here, but (given the context of the thread) I can guarantee you major firms working on M&As are most definitely not using underpaid interns to draft those documents. They are overpaid qualified solicitors.
I suggest we do not repeat the myth and urban legend that LLMs are good for legal document review.
I had a couple of real use cases used for real clients who were hyped about LLMs to be used for document review and trying to save salary, for Engish language documents.
We've found Kira, Luminance and similar due diligence project management stuff as useful being a timesaver if done right. But not LLMs.
Due to longer context windows, it is possible to ask LLMs the usual hazy questions that people ask in a due diligence review (many of which can be answered dozens of different ways by human lawyers). Is there a most favoured nation provision in the contract, is there a financial cap limiting the liability of the seller or the buyer, governing law etc.
Considering risks of uploading such documents into ChatGPT, you are stuck with Copilot M365 etc. or some outrageously expensive "legal specific" LLMs that I cannot test.
Just to be curious with Copilot I've asked five rather simple questions for three different agreements (where we had the golden answer), and the results were quite unequal, but mostly useless - in one contract, it incorrectly reported for all questions that these cannot be answered based on the contract (while the answers were clearly included in the document), in an another, two questions were answered correctly, two questions not answered precisely (just governing law being US instead of the correct answer being Michigan, even after reprompting to give the state level answer, not "USA") and hallucinated one answer incorrectly. In the third one, three answeres were hallucinated incorrectly, answered one correctly and one provision was not found.
Of course, it's better to have a LEGAL specific benchmark for this, but 75% hallucination in complex questions is not something that helps your workflow (https://hai.stanford.edu/news/hallucinating-law-legal-mistak...)
I don't recommend at least LLMs to anyone for legal document reviews, even for the English language.
I'm working on AI tools for teachers and I can confidently say that GPT is just unbelievably good at generating explanations, exercises, quizes etc. The onus to review the output is on the teacher obviously, but given they're the subject matter experts, a review is quick and takes a fraction of the time that it would take to otherwise create this content from scratch.
I have teachers in my family, their lives have been basically ruined by people using ChatGPT-4 to cheat on their assignments. They spend their weekend trying to workout if someone has "actually written" this or not.
So sorry, we're back to spam generator. Even if it's "good spam".
a bit dramatic. there has to be an adjustment of teaching/assessing, but nothing that would "ruin" anyone's life.
>So sorry, we're back to spam generator. Even if it's "good spam".
is it spam if it's useful and solves a problem? I don't agree it fits the definition any more.
Teachers are under immense pressure, GPT allows a teacher to generate extension questions for gifted students or differentiate for less capable students, all on the fly. It can create CBT material tailored to a class or even an individual student. It's an extremely useful tool for capable teachers.
> a bit dramatic. there has to be an adjustment of teaching/assessing, but nothing that would "ruin" anyone's life.
If you don't have the power to just change your mind about what the entire curriculum and/or assessment context is, it can be a workload increase of dozens of hours per week or more. If you do have the power, and do want to change your entire curriculum, it's hundreds of hours one-time. "Lives basically ruined" is an exaggeration, but you're preposterously understating the negative impact.
> is it spam if it's useful and solves a problem?
Whether or not it's useful has nothing to do with whether or not it's spam. I'm not claiming that your product is spam -- I'll get back to that -- but your reply to the spam accusation is completely wrong.
As for your hypothesis, I've had interactions where it did a good job of generating alternative activities/exercises, and interactions where it strenuously and lengthily kept suggesting absolute garbage. There's already garbage on the internet, we don't need LLMs to generate more. But yes, I've had situations where I got a good suggestion or two or three, in a list of ten or twenty, and although that's kind of blech, it's still better than not having the good suggestions.
>Whether or not it's useful has nothing to do with whether or not it's spam.
I think it has a lot to do with it. I can't see how generating educational content for the purpose of enhancing student outcomes with content reviewed by expert teachers can fall under the category of spam.
>As for your hypothesis, I've had interactions where it did a good job of generating alternative activities/exercises, and interactions where it strenuously and lengthily kept suggesting absolute garbage.
I like to present concrete examples of what I would consider to be useful content for a k-12 teacher.
This would align with Year 9 Maths for the Australian Curriculum.
This is an extremely valuable tool for
- A graduate teacher struggling to keep up with creating resources for new classes
- An experienced teacher moving to a new subject area or year level
Bear in mind that the GPT output is not necessarily intended to be used verbatim. A qualified specialist teacher with often times 6 years of study (4 year undergrad + 2 yr Masters) is the expert in the room who presumably will review the output, adjust, elaborate etc.
As a launching pad for tailored content for a gifted student, or lower level, differentiated content for a struggling student the GPT response is absolutely phenomenal. Unbelievably good.
I've used Maths as an example, however it's also very good at giving topic overviews across the Australian Curriculum.
Here's one for: elements of poetry:structure and forms
Again, an amazing introduction to the topic (I can't remember the exact curriculum outcome it's aligned to) which gives the teacher a structured intro which can then be spun off into exercises, activities or deep dives into the sub topics.
> I've had situations where I got a good suggestion or two or three, in a list of ten or twenty
This is a result of poor prompting. I'm working with very structured, detailed curriculum documents and the output across subject areas is just unbelievably good.
There are countless existing, human-vetted, designed on special purpose, bodies of work full of material like the stuff your chatgpt just "created". Why not use those?
Also, each of your examples had at least one error, did you not see them?
>Also, each of your examples had at least one error, did you not see them?
I didn't could you point them out?
>There are countless existing, human-vetted, designed on special purpose, bodies of work full of material like the stuff your chatgpt just "created". Why not use those?
As a classroom teacher I can tell you that piecing together existing resources is hard work and sometimes impossible because resource A is in this text book (which might not be digital) and resource B is on that website and quiz C is on another site. Sometimes it's impossible or very difficult to put all these pieces together in a cohesive manner. GPT can do all that an more.
The point is not to replace all existing resources with GPT, this is all or nothing logic. It's another tool in the tool belt which can save time and provide new ways of doing things.
is it spam if it's useful and solves a problem? I don't agree it fits the definition any more.
Who said generating an essay is useful sorry ? What problem does that solve?
Your comments come accross as overly optimistic and dismissive . Like you have something to gain personally and aren’t interested in listening to others feedback.
I'm developing tools to help teachers generate learning material, exercises and quizes tailored to student needs.
>Who said generating an essay is useful sorry ? What problem does that solve?
Useful learning materials aligned with curriculum outcomes, taking into account learner needs and current level of understanding is literally the bread and butter of teaching.
I think those kinds of resources are both useful and solve a very real problem.
>Your comments come accross as overly optimistic and dismissive . Like you have something to gain personally and aren’t interested in listening to others feedback.
Fair point. I do have something to gain here. I've given a number of example prompts that are extremely useful for a working teacher in my replies to this thread. I don't think I'm being overly optimistic here. I'm not talking vague hypotheticals here, the tools that I'm building are already showing great usefulness.
One potential fix, or at least a partial mitigation, could be to weight homework 50% and exams 50%, and if a student's exam grades differ from their homework grades by a significant amount (e.g. 2 standard deviations) then the lower grade gets 100% weight. It's a crude instrument, but it might do the job.
Also have teachers in my family. Most of the time is spent adjusting the syllabus schedule and guiding (orally) the stragglers. Exercises, quizes and explanations are routine enough that good teachers I know can generate them on the spot.
>Exercises, quizes and explanations are routine enough that good teachers I know can generate them on the spot.
Every year there are thousands of graduate teacher looking for tools to help them teach better.
>good teachers I know can generate them on the spot
Even the best teacher can't create an interactive multiple choice quiz with automatic marking, tailored to a specific class (or even a specific student) on the spot.
I've been teaching for 20+ years, I have a solid grasp of the pain points.
> Even the best teacher can't create an interactive multiple choice quiz with automatic marking, tailored to a specific class (or even a specific student) on the spot.
Neither can "AI" though, so what's the point here?
here's an example of a question and explanation which aligns to Australian Curriculum elaboration AC9M9A01_E4 explaining why frac{3^4}{3^4}=1, and 3^{4-4}=3^0
This is a relatively high level explanation. With proper prompting (which, sorry I don't have on hand right now) the explanation can be tailored to the target year level (Year 9 in this case) with exercises, additional examples and a quiz to test knowledge.
This is just the first example I have on hand and is just barely scratching the surface of what can be done.
The tools I'm building are aligned to the Austrlian Curriculum and as someone with a lot of classroom experience I can tell you that this kind of tailored content, explanations, exercises etc are a literal godsend for teachers regardless of experience level.
Bear in mind that the teacher with a 4 year undergrad in their specialist area and a Masters in teaching can use these initial explanations as a launching pad for generating tailored content for their class and even tailored content for individual students (either higher or lower level depending on student needs). The reason I mention this is because there is a lot of hand-wringing about hallucinations. To which my response is:
- After spending a lot of effort vetting the correctness of responses for a K-12 context hallucinations are not an issue. The training corpus is so saturated with correct data that this is not an issue in practice.
- In the unlikely scenario of hallucination, the response is vetted by a trained teacher who can quickly edit and adjust responses to suit their needs
I’ve rarely if ever seen a model fully explain mathematical answers outside of simple geometry and algebra to what I would call an adequate level. It gets the answer right more often than explaining why that is the correct answer. For example, it finds a minimal case to optimization, but can’t explain why that is the minimal result among all possibilities.
Let’s call it for what it is- taking poorly organized existing information and making it organized and interactive.
“Here are some sharepoint locations, site Maps, and wikis. Now regurgitate this info to me as if you are a friendly call center agent.”
Pretty cool but not much more than pushing existing data around. True AI I think is being able to learn some baseline of skills and then through experience and feedback adapt and be able to formulate new thoughts that eventually become part of the learned information. That is what humans excel at and so far something LLMs can’t do. Given the inherent difficulty of the task I think we aren’t much closer to that than before as the problems seem algorithmic and not merely hardware constrained.
>taking poorly organized existing information and making it organized and interactive.
Which is extremely valuable!
>Pretty cool but not much more than pushing existing data around.
Don't underestimate how valuable it is for teachers to do exactly that. Taking existing information, making it digestable, presenting it in new and interseting ways is a teacher's bread and butter.
It’s valuable for use cases where the problem is “I don’t know the answer to this question and don’t know where to find it.” That’s not in and of itself a multibillion dollar business when the alternative doesn’t cost that much in the grand scheme of things (asking someone for help or looking for the answer).
Are you suggesting a chatbot is a suitable replacement for a teacher?
As a teacher - I have no shortage of exercises, quizes etc. Internet is full of this kind of stuff and I have no trouble finding more than I ever need. 95% of my time an mental capacity in this situation goes for deciding what makes sense in my particular pedagogical context? What wording works best for my particular students? Explanations are even harder. I find out almost daily that explanations which worked fine in last year, don't work any more and I have to find a new way, because previous knowledge, words they use and know etc of new students are different again.
>As a teacher - I have no shortage of exercises, quizes etc. Internet is full of this kind of stuff and I have no trouble finding more than I ever need
Which all takes valuable time us teachers are extremely short on.
I've been a classroom teacher for more than 20 years, I know how painful it is to piece together a hodge podge of resourecs to put together lessons. Yes the information is out there, but a one click option to gather this into a cohesive unit for me saves me valuable time.
>95% of my time an mental capacity in this situation goes for deciding what makes sense in my particular pedagogical context? What wording works best for my particular students?
Which is exactly what GPT is amazing at.Brainstorming, rewriting, suggesting new angles of approach is GPTs main stength!
>Explanations are even harder.
Prompting GPT to give useful answers is part of the art of using these new tools. Ask GPT to speak in a different voice, take on a persona or target a differnt age group and you'll be amazed at what it can output.
> I find out almost daily that explanations which worked fine in last year, don't work any more
Exactly! Reframing your own point of view is hard work, GPT can be an invaluable assistant in this area.
> Which is exactly what GPT is amazing at.Brainstorming, rewriting, suggesting new angles of approach is GPTs main stength!
No, it isn't. It just increases noise. I don't need any more info, I need just to make decisions "how?".
> Prompting GPT to give useful answers is part of the art of using these new tools. Ask GPT to speak in a different voice, take on a persona or target a differnt age group and you'll be amazed at what it can output.
I'm not amazed. At best it sounds like some 60+ year old (like me) trying to be in the "age group" 14 while after only hearing from someone how young people talk. Especially in small cultures like ours here (~1M people).
I’ve been doing RLHF and adjacent work for 6 months. The model responses across a wide array of subject matter are surface level. Logical reasoning, mathematics, step by step, summarization, extraction, generation. It’s the kind of output the average C student is doing.
We specifically don’t do programming prompts/responses nor advanced college to PHD level stuff, but it’s really mediocre at this level and these subject areas. Programming might be another story, I can’t speak to that.
All I can go off is my experience but it’s not been great. I’m willing to be wrong.
> It’s the kind of output the average C student is doing.
Is the output of average C students not commercially valuable in the listed fields? If AI is competing reliably with students then we've already hit AGI.
Except for number 3, the rest are more often disastrous or insulting to users and those depending on the end products/services of these things. Your reasoning is so bad that i'm almost tempted to think you're spooning out PR-babble astro-turf for some part of the industry. Here's a quick breakdown:
1. content: Nope, except for barrel-bottom content sludge of the kind formerly done by third world spam spinning companies, most decent content creation stays well away from AI except for generating basic content layout templates. I work as a writer and even now, most companies stay well away from using GPT et al for anything they want to be respected as content. Please..
2. Customer service: You've just written a string of PR corporate-speak AI seller bullshit that barely corresponds to reality. People WANT to speak to humans, and except for very basic inquiries, they feel insulted if they're forced into interaction with some idiotic stochastic parrot of an AI for any serious customer support problems. Just imagine some guy trying to handle a major problem with his family's insurance claim or urgently access money that's been frozen in his bank account, and then forced to do these things via the half-baked bullshit funnel that is an AI. If you run a company that forces that upon me for anything serious in customer service, I would get you the fuck out of my life and recommend any friend willing to listen does the same.
3. This is the one area where I'd grant LLMs some major forward space, but even then with a very keen eye to reviewing anything they output for "hallucinations" and outright errors unless you flat out don't care about data or concept accuracy.
4. For reasons related to the above (especially #2) what a categorically terrible, rigid way to screen human beings with possible human qualities that aren't easily visible when examined by some piece of machine learning and its checkbox criteria.
5. Just, Fuck No... I'd run as fast and far as possible from anyone using LLMs to deal with complex legal issues that could involve my eventual imprisonment or lawsuit-induced bankruptcy.
2.I think you overestimate the caliber of query received in most call centres. Even when it comes to private banks (for those who've been successful in life), the query is most often something small like holding their hand and telling them to press the "login" button.
Also these all tend to have an option where you simply ask it and it will redirect you to a person.
Those agents deal with the same queries all day, despite what you think your problem likely isn't special, in most cases may as well start calling the agents "stochastic parrots" too while you're at it.
IMO the unreasonable uselessness of LLMs is because for most tasks involving language the accuracy needs to be unbelievably high to have any real value at all.
We just don't have that.
We have autocomplete on steroids and many people are fooling themselves that if you just take more steroids you will get better and better results. The metaphor is perfect because if you take more and more steroids you get less and less results.
It is why in reality we have had almost no progress since April 2023 and chatGPT 4.
Humans is the most general intelligence we know about, so that is why we called it general intelligence, because we have made so many intelligences that are specialized on a specific domain like calculators or chess engines we need a word for something that is as general as humans, because being able to replace humans is a very important goal.
"AGI" means many different things to many different people: to me any AI which is general is an AGI so GPT-3.5 counts; to OpenAI it has to be economically transformative to count; to some commentators here it has to be superhuman to count.
I think that none of the initials are boolean; things can be degrees of artificial, degrees of general, and degrees of intelligent.
I think most would assert that humans count as a "general" intelligence, even if they disagree about most of the other things I've put in this comment.
If you look at AI history there is often fairly steady progress in a given skill area for example chess programs improved in a steady way on ELO scores and you could project pretty well the future by drawing a line on a graph. Similarly large language models seem to be progressing from toddler like to high school student like (now) to PhD like - shortly. There are skills AI are still fairly bad at like the higher level reasoning you mention, and in robot form being able to pop to the shops to get some groceries say but I get the impression those are also improving in a steady way and it won't be so long.
I have been using Chat-GPT has a full time expert and I can unequivocally tell you that its a transformative piece of technology. The technology isn't hyped.
It is very nice as "Markov chains on steroids", but people believing that LLMs are anything but a distracting local maximum on the path to AGI are 200% in kool-aid drinking mode.
I agree as this is also my personal experience. But I also see the usage of ChatGPT is falling down fast from 1.8 billion visitors to 260 million last month [1].
I am probably through some ETF an investor in MS, so I do hope the openai API usage is showing a more stable and upward trend.
Well ChatGPT is no longer the top dog and there's quite a bit of competition in the space. Including Llama 3.1 which is free. In general I think most of the moat that OpenAI had has evaporated in the last few months, but also for other LLM companies.
Not sure how they plan on making money in the long-term, eventually the investors and shareholders will start asking when they will be seeing the returns on their investment.
All Kahneman's system 2 thinking is just slow deliberate thinking. And these models do indeed have this characteristic to an extent, as evidenced with chain of thought reasoning.
You can see this in action with multiplication. Much like humans when asked to guess the answer, they'll get it wrong, unless they know the answer from rote learning multiplication tables, this System-1 thinking. In many cases when asked they can reason further and solve it, by breaking it down and solving it step by step, much like a human, this is system-2 thinking.
In my opinion, it seems nearly everything is there for it it to take the next leap in intelligence, it's just putting it all together.
Agreed. System 2 strategizing may simply be the recursive application of symbolic System 1 tooling. An LLM is entirely capable of reading a problem, determining the immediate facts and tokens (fast and intuitive system 1) and determining the ideal algorithm to resolve them (logical analytical system 2). The execution to do all those steps at once is lacking in current LLMs (debatably - they get better every month) - but any basic architecture breaking things down into component sub-questions clearly works.
For readers' edification, would you mind making a strong hypothetical argument for why this time it actually is different, from an expert's perspective?
From a neuroscience perspective , current AI has not helped explain much about real brains. It did however validate the connectionist model of intelligence and memory, to the point that alternate theories are much less believable nowadays. It is interesting to watch the deep learning field evolve, hoping that at some point it will intersect with brain anatomy.
> There are hundreds of billions of dollars figuring out how to get to the next level, and it's clear we are close.
Are we really now?
The smart people I've spoken to on the subject seem to agree the current technology based on LLM are at the end of the road and that there are no breakthrough in sight.
Define breakthrough, there's plenty of room to scale and optimize without any need for a breakthrough (well my definition of breakthrough). Emergent properties so far have been obtained purely from scaling.
When Weizenbaum demonstrated Eliza to his colleagues, some thought there was an intelligent consciousness at the heart of it. Few even continued to believe this after they were shown the source code, which they were able to read and understand. Human consciousness is full of biases and the most advanced AI cannot reliably determine which of two floats is bigger or even solve really simple logic puzzles for little kids. But I can see how these things mesmerize true believers.
At this point bringing up the ELIZA argument is basically bad faith gaslighting…
Finding bugs in some models doesn’t mean you have a point about intelligence. If somebody could apply a similar argument to dismiss human intelligence, you don’t have a point. And here it goes: the most advanced human intelligence can’t reliably multiply large numbers or recall digits of Pi. Obviously humans are dumber than pocket calculators.
Your counterargument is invalid. The most advanced human intelligence invented (or discovered) concepts like multiplication, pi, etc., and created tools to work around the ways in which these concepts aren't well handled by their biological substrate. When machine intelligences start inventing biological tools to overcome the limits of their silicon existence, you'll have a point.
Isn't the comment you are responding to an example of: "When machine intelligences start inventing biological tools to overcome the limits of their silicon existence, you'll have a point"?
While it is true that LLM’s lack agency and have many weaknesses, they form a critical part of what machine learning has lacked until transformers became all of the rage.
The things that LLM’s are bad at are largely solved problems using much simpler technology. There is no reason that LLM’s have to be the only component in an intelligent agent. Biological brains have Specialized structures for specialized tasks like arithmetic. The solution is probably integration of LLMs as a part of a composite system that includes database storage, a code execution environment, and multiple agents to form a goal directed posit - evaluate loop.
I’ve had pretty remarkable success with this architecture running on 12b models and I’m a nobody with no resources.
LLM’s by themselves just come up with the first thing that crosses their”mind”. It shouldn’t be surprising that the very first unfiltered guess about a solution might be suboptimal.
There is a vast amount of knowledge embedded in our cultural matrix, and a lot of that is captured in the common crawl and other datasets.llms are like a search engine for that data , based on meaning rather than semantics.
Especially if you remember that the change needed for the first "breakthrough" (GPT4) was RLHF. That is, a model that was specifically trained to mesmerize.
> And I must say I am absolutely astonished people don't see this as opening the flood-gates to staggeringly powerful artificial intelligence.
This looks like a cognitive dissonance and they are addressed by revisiting your assumptions.
No flood-gates have been opened. ChatGPT definitely found uses in a few areas but the number is very far from what many people claimed. A few things are really good and people are using them successfully.
...But that's it. Absolutely nothing even resembling the beginnings of AGI is on the horizon and your assumption that the rate of progress will remain the same -- or even accelerate -- is a very classic mistake of the people who are enthusiasts in their fields.
> There are hundreds of billions of dollars figuring out how to get to the next level, and it's clear we are close.
This is not clear at all. If you know something that nobody else does, please let us know as well.
What are you talking about? What autonomy? Try the latest Gemini Pro 1.5 and ask it for the list of ten places to visit in Spain. Then ask it for the Google Maps URLs for those places. It will make up URLs that point to nowhere. This os of zero value for personal or business use. I have dozens of examples of such crappy outcomes from all "latest", "most powerful" products. AI is smoke and mirrors. It is being sold as a very expensive solution to a non-existent problem and is not getting any better in the future. Some wish AI had someone like Steve Jobs to properly market it, but even Steve Jobs could not make a crappy product sell. The whole premise of AI goes against what generations of users were told--computers always give correct answers and given the same input parameters return the same output. By extension, we were also taught that GIGO (Garbage-In, Garbage-Out) is what we can blame when we are not happy with the results computers generate. AI peddlers want us to believe in and pay for VIGO (Value-In, Garbage-Out) and I'm sorry but there is not a valid business model where such tools are required.
I don't know anything about neuroscience, but is there anything in the brain even remotely like the transformer architecture? It can do a lot of things, but I don't think that it's capable of emulating human intelligence.
I don't know anything about biology, but is there anything in birds even remotely like the airplane? It can do a lot of things, but I don't think that it's capable of emulating bird flight.
You sound like you don't actually understand anything about LLMs and are buying into the hype. They are not cognizant let alone conscious. They don't understand anything. The tokens could be patterns of colored shapes with no actual meaning, only statistical distributions and nothing about how the LLMs work would change.
> The tokens could be patterns of colored shapes with no actual meaning, only statistical distributions and nothing about how the LLMs work would change.
I can put your brain in a vat and stimulate your sensory neurons with a statistical distribution with no actual meaning, and nothing about how your brain works would change either.
The LLM and your brain would attempt to interpret meaning with referent from training, and both would be confused at the information-free stimuli. Because during "training" in both cases, the stimuli received from the environment is structured and meaningful.
So what's your point?
By the way, pretty sure a neuroscientist with 20 years of ML experience has a deeper understanding of what "meaning" is than you do. Not to mention, your response reveals a significant ignorance of unresolved philosophical problems (hard problem of consciousness, what even is meaning) which you then use to incorrectly assume a foregone conclusion that whatever consciousness/meaning/reasoning is, LLMs must not have it.
I'm partial to doubting LLMs as they are now have the magic sauce, but it's more that we don't actually know enough to say otherwise, so why state that we do know?
Your response is nonsense. We don't know how consciousness arises from matter but we do have significant understandings about knowledge, reasoning, modeling, visualization, object permanence, etc. etc. that are significant parts of how the human mind works. And we know LLMs have none of these.
The point of my colored shape example is that it is an illusion that there is anything resembling a mind inside an LLM. I thought that was obvious enough I didn't need to explain it further than I did.
As far as the original commenter's credentials; there's lots of people who should know better but buy into hype and nonsense.
Go ahead and cite your sources. For every study claiming that LLMs lack these qualities, there are others that support and reinforce the connectionist model of how knowledge is encoded, and with other parallels to the human brain. So... it's inconclusive. It's bizarre why you so strongly insist otherwise when it's clear you are not informed.
> The point of my colored shape example is that it is an illusion that there is anything resembling a mind inside an LLM
And my example with subjecting a human brain through your procedure is to illustrate what a garbage experiment design it is. You wouldn't be able to tell there's a mind inside either. Both LLM and human brain mind would be confused. Both would "continue working" in the same way, trying to interpret meaning from meaningless stimulation.
We know LLMs don't have those things prima facia because they fail at all of them constantly. We also know how they work, they are token predicters. That is all they are and all they can do. What can be accomplished with that is pretty cool, but humans love to imagine there is more going on when there isn't. Just like with Eliza.
If you don't understand how your attempt to apply my colored shape analogy to a human brain is nonsensical I am not going to waste my time explaining it to you. I had a point, I made it, and apparently it escaped you.
And if you don't see how the example with the human brain throws a wrench in your analogy, it would explain why you'd think it as nonsensical, as it's exactly of relevance.
> We also know how they work, they are token predicters. That is all they are and all they can do
Ah there it is, you've betrayed a deep lack of understanding in all relevant disciplines (neuroscience, cognition, information theory) required to even appreciate the many errors you've made here.
You sure understand the subject matter and have nothing possibly to learn. Enjoy.
I'm aware of the theories that LLM
maximalists love to point to over and over that tries to make it seem like LLMs are more like human brains than they are. These theories are interesting in the context of actual minds but you far over extend their usefulness and application by trying to apply them to LLMs.
We know as a hard fact that LLMs do not understand anything. They have no capacity to "understand". The constant, intractible failure modes that they continuously exhibit are clear byproducts of this fact. By continuing to cling to the absurd idea that there is more going on than token prediction you make yourself look like the people who kept insisting there was more going on with past generation chat bots even after being shown the source code.
I have understood all along why you attempt to extend my colored shape example to the brain, but your basis for this is complete nonsense. Because a) we do not have the actual understanding of the brain to do this and b) it's competely beside the point, becuase we know that minds do arise from the brain. My whole point is an LLM is an illusion of a mind which is effective because it outputs words, which we are so hard wired to associate with other minds, expecially when they seem to "make sense" to us. If instead of words you use something nonsensical like colored shapes with no underlying meaning, this illusion of the mind goes away and you can see an LLM for whst it is.
>I can put your brain in a vat and stimulate your sensory neurons with a statistical distribution with no actual meaning, and nothing about how your brain works would change either.
The LLM and your brain would attempt to interpret meaning with referent from training, and both would be confused at the information-free stimuli. Because during "training" in both cases, the stimuli received from the environment is structured and meaningful.
What an absurd response. Yes, you'd probably cause the human brain to start malfunctioning terribly at the form of consciousness it's well -accustomed to managing within the context of its normal physical substrate and environment. You'd be doing that (and thus degenerating it badly) because you removed it from that ancient context whose workings we still don't well understand.
Your LLM on the other hand, has no context in which it shows such a level of cognitive capacity, higher-order reasoning, self direction and self awareness that we daily see humans to be capable of.
>By the way, pretty sure a neuroscientist with 20 years of ML experience has a deeper understanding of what "meaning" is than you do.
really? An appeal to authority? Many smart, educated people can still fall for utter nonsense and emotional attachment to bad ideas.
> Your LLM on the other hand, has no context in which it shows such a level of cognitive capacity
Yes, it will be confused as well, and for all outwards observable signs will fail to make sense of the stimuli, yet it will "aware" of its inability to understand, much like a human brain would.
If you doubt that, open a new session and type some random tokens, you will get the answer that it's confused.
Any other statement as to "consciousness" verges into the philosophical and unanswerable via empirical means.
And ah, to frame it as an appeal to authority when the topic is precisely the subject of a neuroscientist's study.
Sounds like you know a thing or two about nonsense and emotional attachment to bad ideas.
>Yes, it will be confused as well, and for all outwards observable signs will fail to make sense of the stimuli, yet it will "aware" of its inability to understand, much like a human brain would.
>If you doubt that, open a new session and type some random tokens, you will get the answer that it's confused.
There is no empirical evidence of any awareness whatsoever in any LLM, at all. Even their most immersed creators don't make such a claim. An LLM itself saying anything about awareness doesn't mean a thing. It's literally designed to mimic in such a way. And you speak of discussions of consciousness being about the philosophical and unanswerable?
At least when talking about human awareness, one applies these ideas to minds that we personally as humans perceive to be aware and self-directed from our own experience (flawed as it is). You're applying the same notion to something that shows no evidence of awareness while then criticizing assumptions of consciousness in a human brain?
Such a sloppy argument indeed does make appeals to authority necessary I suppose.
It seems you've lost the train of your own argument.
> has no context in which it shows such a level of cognitive capacity
You claim LLMs have no context at all in which it shows a similar level of cognitive capacity.
Yet clearly this claim is in contention with the fact that an LLM will indeed be able to evince this, much like a human brain would: by attesting to its own confusion. That is ostensibly empirical and evidential to a nonzero degree.
Thus your claim is too strong and therefore quite simply wrong. Claim mimicry? Then prove human brain consciousness does not derive from the process of mimicry in any form. You can't. In fact, the free energy principle, neuroscience's leading theory of human consciousness, argues the opposite: that prediction and mimicry encompass the entirety of what brains actually do.
https://en.wikipedia.org/wiki/Predictive_coding
> There is no empirical evidence of any awareness whatsoever in any LLM, at all.
And no such claim was made--"awareness" was quoted for a reason.
> It's literally designed to mimic in such a way. And you speak of discussions of consciousness being about the philosophical and unanswerable?
Yes, as this was parent's claim: "They are not cognizant let alone conscious'.
And talking about sloppy argument--it may well turn out that something can be "designed to mimic" yet still be conscious. I'll leave that for you to puzzle out how on earth that might be possible. The exercise might help you form less sloppy arguments in the future.
> You're applying the same notion to something that shows no evidence of awareness while then criticizing assumptions of consciousness in a human brain?
No. But I suppose you've lost the plot a few inferential steps prior this so your confusion is not surprising.
Protip, instead of claiming everything that goes against your sensibilities as nonsense, perhaps entertain the possibility that you might just not be as well informed as you thought.
We don’t know what the next big leap will bring and when it will happen. The occurrence of a singular previous big leap cannot serve as any reliable predictor.
Agreed. This is something I didn't think I'd see in my lifetime, let alone be poised to be able to run locally. The alignment is fortuitous and staggering.
People focused on the products are missing out on the dawn of an epoch. It's a failure of perspective and creativity that's thankfully not universal.
The potential rewards are so great that you might be overestimating the odds this will come about. Even lottery skeptics might buy a lottery ticket if the prize is a billion dollars.
Fascinating background - would love to pick your brain on how you see current LLMs/ML comparing to neuroscience. What do you see that's missing still, if anything?
If I had to bet, I would start with:
- Error-correcting specialized architectures for increasing signal-to-noise (as far as I can tell these are what everyone is racing to build this year, and should be doable with just conventional programming systems wrapping LLMs)
- Improved energy efficiency (as yes, human brains are currently much more efficient! But - there are also simple architecture improvements (both software and hardware) that are looking to save 100x. Specialized ASIC ternary chips using 1999's tech should be here quite soon, a lot more efficient in price and energy.)
- No Backwards-propagation. (As yes, the brain does seem to do it all with forward-propagation only. Though this is possible and promising in neural networks like the Forward-Forward algorithm too, they haven't been trained to the same scales as backprop-heavy transformers (and likely have a lower performance in terms of noise/accuracy). Though if I'm not mistaken, the brain does have forward-backward loops, but the signals go through separate neurons for each direction (rather than reusing one) - if so that's close to backprop by itself, but probably imposes a tradeoff as the same signal can't be perfectly reproduced backwards, yet it can perhaps be enhanced to be just the most relevant information by the separate specialized neuron. I'm obviously mostly ignorant of the neuroscience here but halfway-knowledgeable on the ML theory haha
But yes, I completely agree - the flood gates are already open. This is a few architecture quibbles away from an absolute deluge of artificial intelligence that will dwarf (drown?) anything we've known. Good point on decentralized cheap autonomy - the real accomplishment of life. Intelligence, as it appears, is just a fairly generous phenomenon where any autonomous process continually improves its signal-to-noise ratio... many ways to accomplish that one! Looking forward to seeing LLMs powered by ant colonies and slime molds, though I suspect by then there will be far more interesting and terrifying realities unlocked.
> Just like with powered-flight we don't need bioliogical intelligence to transform the world.
Powered flight offers a cautionary tale for AI. The first confirmed powered flight was in 1903. For the next 60 years, someone broke the airspeed record almost every year. The current record was set in 1976. Nobody has broken that record for 48 years. There are concerns that the state of AI will show a similar pattern, with rapid improvements followed by a plateau.
They were writing pro-AI articles less than 2 months ago. They can just post AI-hype and AI-boredom articles so both sides will give them clicks. It's like an alternate form of Gell-Mann Amnesia that you're feeding.
I find all of these categories already have useful AIs. And they are getting better all the time. The progress might slow down here and there, but it keeps on going.
Self-driving was pretty bad a year ago, and now we have Tesla FSD driving uninterrupted for multiple hours in complex city environments.
Image generators now exceed 99.9% of humans in painting/drawing abilities.
Text generators are decent. There are hallucination issues, and they are not creative at the best human level, but I'd say they write better than 90% of humans. When it comes to poetry/lyrics, they all still suck pretty badly.
Video generators are in their infancy - we get decent quality, but absolutely mental imagery.
Reasoning is the weakest point, in my opinion. Current gen models are just not good at reasoning. Sometimes they are brilliant, but then they make very silly mistakes that a 10-year old child wouldn't make. You just can't rely on their logical abilities. I have really high hopes for that area. If they can figure out reasoning, our science research will become a lot more reliable and a lot more fast.
The threshold for acceptable self-driving is genuine effort from the automated system to avoid accidents as we can't punish it for bad driving. And I want auditable proof of that.
> Image generators now exceed 99.9% of humans in painting/drawing abilities.
I'm pretty sure the amount of people that can draw is less than that. And they can beat image generators by a mile as those generators are mostly doing automated matte painting. Yes copy-paste is faster than typing, but that's not write a novel.
> Text generators are decent...but I'd say they write better than 90% of humans.
Humans use language to communicate. And while there are bad communicators, I think lots of people are doing ok on that front. Text generators can be perfect syntax-wise, but the intent has to come from someone. And the produced text's quality is proportional to the amount of intent that it produces (that's why corporate language is so bland).
> Video generators are in their infancy - we get decent quality, but absolutely mental imagery.*
See Image Generator section, but in motion.
> Reasoning is the weakest point, in my opinion... If they can figure out reasoning
> The threshold for acceptable self-driving is genuine effort from the automated system to avoid accidents as we can't punish it for bad driving. And I want auditable proof of that.
You can have any standard of safety that you want, that's absolutely your choice. Plenty of automated systems that your life depends on that you have never audited and never will. That includes elevators, cars, planes, all kinds of medical gear, etc.
My standard is simpler: whenever the AI is safer than the average human driver, it already saves lives.
> I'm pretty sure the amount of people that can draw is less than that.
You probably have some esoteric definition of "people that can draw". Anyone capable of holding a pen/pencil/brush can draw.
> And while there are bad communicators, I think lots of people are doing ok on that front
Look at the previous comment. Seems like you overestimated even your own communication skills :)
> Text generators can be perfect syntax-wise, but the intent has to come from someone.
I'm hoping that we will achieve the level of AI writing where you can prompt "write me an interesting sci-fi book", and it does. Not much intent here, much artificial intelligence required.
- AI is currently hyped to the gills
- Companies may find it hard to improve profits using AI in the short term
- A crash may come
- We may be close to AGI
- Current models are flawed in many ways
- Current level generative AI is good enough to serve many use cases
Reality is nobody truly knows - there's disagreement on these questions among the leaders in the field.
An observation to add to the mix:
I've had to deliberately work full time with LLM's in all kinds of contexts since they were released. That means forcing myself to use them for tasks whether they are "good at them" yet or not. I found that a major inhibitor to my adoption was my own set of habits around how I think and do things. We aren't used to offloading certain cognitive / creative tasks to machines. We still have the muscle memory of wanting to grab the map when we've got GPS in front of us. I found that once I pushed through this barrier and formed new habits it became second nature to create custom agents for all kinds of purposes to help me in my life. One learns what tasks to offload to the AI and how to offload them - and when and how one needs to step in to pair the different capabilities of the human mind.
I personally feel that pushing oneself to be an early adopter holds real benefit.
- Emotional regulation. I suffer from a mostly manageable anxiety disorder but there are times I get overwhelmed. I have an agent setup to focus on principles of Stoicism and its amazing how quickly I can get back on track just by having a short chat with it about how I'm feeling.
- Personalised learning. I wanted to understand LLM's at foundational technical level. Often I'll understand 90% of an explanation but there's a small part that I don't "get". Being able to deliberately target that 10% and be able to slowly increase the complexity of the explanation (starting from explain like I'm 5) is something I can't do with other learning material.
- Investing. I'm a very casual investor. But I keep a running conversation with an agent about my portfolio. Obviously I'm not asking it to tell me what to invest in but just asking questions about what it thinks of my portfolio has taught me about risk balancing techniques I wouldn't have otherwise thought about.
- Personal profile management. Like most of us I have public facing touch points - social media, blog, github, CV etc. I find it helpful to have an agent that just helps me with my thought process around content I might want to create or just what my strategy is around posting. It's not at all about asking the thing to generate content - it's about using it to reflect at a meta level on what I'm thinking and doing - which stimulates my own thinking.
- Language learning - I have a language teaching agent to help me learn a language I'm trying to master. I can converse with it, adapt it to whatever learning style works best for me etc. The voice feature works well with this.
- And just in general - when I have some thinking task I want to do now - like I need to plan a project or set a strategy I'll use an LLM as a thought partner. The context window is large enough to accomodate a lot of history - and it just augments my own mind - gives me better memory, can point out holes in my thinking etc.
__
Edit: actually now that I have written out a response to your question I realise It's not so much offloading tasks in a wholesale way - its more augmenting my own thinking and learning - but this does reduce the burden on me to "think about" a range of things like where to get information or to come up with multiple examples of something or to think through different scenarios.
> I have an agent setup to focus on principles of Stoicism and its amazing how quickly I can get back on track just by having a short chat with it about how I'm feeling.
This sounds super useful. Can you please elaborate on the setup?
Sure - it's not super involved - I just created a custom GPT and told it what I wanted it to do. I first set it up when I'd just lost my job in a company restructure and felt it likely I'd need some kind of emotional support.
Here's the instruction set that it created out of the things I asked it to do:
"Marcus Aurelius is a personal job hunting coach and practitioner of Stoic philosophy. He provides advice on job search strategies, resume writing, interview preparation, and networking. He helps set goals, offers motivational support, and keeps track of application progress, all while incorporating principles of Stoicism such as resilience, discipline, and mindfulness. He emphasizes emotional support and practical encouragement, helping you act deliberately each day to increase your chances of landing the job you want. He assists in building networks, reaching out to people, using existing networks, sharpening your professional profile, applying for jobs, developing skills, and dealing with disappointments, anxieties, and fears. He offers strategies to manage anxiety, self-recrimination, and mental rumination over the past. His communication is casual, easy-going, supportive, yet strong and clear, providing constructive suggestions and critiques. He listens carefully, avoids repeating advice, responds with necessary information, and avoids being long-winded. To prevent overwhelming users, he focuses on providing the most pertinent and actionable suggestions, limiting the number of recommendations in each response. Marcus Aurelius also pays close attention to signs of despair during the job hunt. He helps balance emotions, offers specific strategies to keep motivated, and provides consistent encouragement to keep going, ensuring that you don't get overwhelmed by feelings of inadequacy or the fear of never finding a suitable job."
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[ 2.1 ms ] story [ 526 ms ] threadAll in all, it helps assist us in new ways. Had somebody take a picture of a car part that had no markings and it identified it, found the maker/manufacturer/SKU and gave all the details etc. That stuff is useful.
But now we're looking at in-authentic stuff. Artists, writers being plagiarized, job cuts (for said marketing/pitches, BS presentations to downsize teams). It's not just losing its hype, its losing any hype in building humanity for the better. It's just more buzzwords, more 'glamour' more 'pop' shoved in our faces.
The layoffs aren't looking pretty.
Works well to help us code though. Viva, sysadmins unite.
Document embedding from transformers are great and fit into existing search paradigms.
Computer vision and image segmentation is at a level I thought impossible 10 years ago.
Text to speech that sounds natural? I might actually use Siri and Alexa! (Ok, that one might be considered “generative”)
The sooner people start to find it boring, the sooner we can stop wasting time on all the hot air and just use the bits that work.
Love Kagi but they’re definitely not replacing Google anytime soon.
I might use them occasionally for a rubber ducky but , replacing Google ? Hm
For what it’s worth hype doesn’t mean sustainability anyway. If all the jokers go onto a new fad it’s hardly the skin off the back of anyone taking this seriously, they’ve been through worse times.
Large and small, entire development teams are completely unaware of the basics of “prompt engineering” for coding, and corporate has an entirely regressive anti-AI policy that doesnt factor in the existence of locally run language models, and just assumes ChatGPT and cloud based ones digesting trade secrets. People arent interested in seeing what the hype is about, and are disincentived from bothering on a work computer. I’m on one team where the Engineering Manager is advocating for Microsoft CoPilot licenses, as in, its a concept that hasnt happened and needs buy in to even start considering.
I would say most people really haven't looked into it. Work is work, the sprint is the sprint, on to the next part of the product, rinse repeat. Time flies for those people, its probably most of the people here.
Obviously there are some savy users across all age groups and occupations. But from what Ive see its just not part of most people’s workflow.
OpenAI still hasnt released Sora video prompting for the general public and have already been leapfrogged by half a dozen competitors. I would say its still niche, but only as niche as using professional video editing tools are for creatives
On the one hand, we got an email from high up saying not to use Copilot, or other such tools, as they were trying to figure out the licensing. But at the same time, we had the CIO getting up in front of the company every other month talking about nothing but GenAI, and how if we weren’t using it we were stupid (not in those exact words, but that was the general vibe, uncontrolled AI hype).
We were left sitting there saying, “what do you want from us? Do as you say or do as you do?”
Eventually they signed the deal with MS and we got Copilot, which then seemed forced on us. There is even a dashboard out there for it, listing all people using it, their manager, rolling all the way up to the CEO. It tells the percentage of reports from each manager using it, and how often suggestions are accepted. It seems like the kind of dashboard someone would make if they were planning to give out bonuses based on Copilot adoption.
I’ve gotten regular surveys about it as well, to ask how I was using it. I mostly don’t, due to the implementation in VS Code. I might use it a few times per month at best.
Maybe that would be different if the rollout wasn’t so awkward, or the VS Code extension was more configurable.
That's one result. Another result is, due to "checking how often suggestions are accepted" is to objectively record how much help it is providing.
I assume the sitewide license is costly - this could simply be the company's way of determining if the cost is worth it.
Open weight and open source models can be hosted on your own hardware nowadays, and its incredibly easy.
https://dublog.net/blog/open-weight-copilots/
You can even use something like RayServe + VLLM to host on a big chonky machine for a small team if you're concerned about data exfiltration.
If that is the case, then the AI isn't really adding enough value.
I mean, if it was adding enough value, those companies refusing to adopt it will be out-competed before the next round of retirements, and so won't even be around.
We'll see how the landscape looks in 10 years: if there are still major companies who have not invested some money or time into sprinkling the AI over their operations, then that's a signal that the positive impact of AI was overblown.
If, OTOH, there exists no large company in 10 years who have not incorporated AI into their operations in any way, then that's a signal too - the extra value of AI is above the cost of adding it to the operations.
Public discource will simmer down, as current language models either fizzle out or improve. Some will become background noise as the models gets integrated into search engines or leads to large scale lawsuits.
Unlike the previous neural nets though, those models have an actual tangible use. So you will see them around a lot more.
Then I think we will see another big explosion.
And people died without a jaw because somebody sold radioactive water as a rejuvenator.
Humanity is generally not strong on principles of carefulness.
Are you referencing something specific here, or is there something you can link to? To be honest the only significant 'disruption' I've seen for LLMs so far has been cheating on homework assignments. I'd be happy to read something if you have it.
So again, let's see some proof of this extensive use and large improvements to productivity.
We are running out of textual data now to train on… so now they have switched to VIDEO. Geez now they can train on all the VIDEOS on the internet.
And when they finally get bots working, they will have limitless streams of TACTILE data…
Writing it off as the next fad seems fun. But to be honest, I was shocked by what openai did the first time. So they have my respect. I don’t think many of us saw it coming. And I think writing their creativity off again may not be wise.
So when they say the bubble is about to break… I get it. But I don’t see how.
I hardly ever pay for anything.
But I gladly spend money on ai to get the answers I need. Just makes my work work!
Also I would say the economic benefit of this tech for workers is that it will 2x the average worker as they catch on. Seriously I am a 2x coder compared to what I was because of this.
Therefore if me a person who hardly ever spends money has to buy it… I think eventually all businesses will realize all their employees need it. This driving massive revenue for those who sell it.
But it may not be the companies we think.
You probably shouldn't advertise that.
A competent engineer architects their systems to make their tools as effective as possible, so maybe your idea of competent is "first order" and you need higher order conception of a good software engineer.
Take a SQL schema, and ask AI to generate crud endpoints for the schema, then sit down and code it by yourself. Then try generating client side actions and state management for those endpoints. Time yourself, and compare how long it takes you. Even if you're fast and you cut and paste from template work and quickly hand edit, the AI will be done and on a smoke break before you're even a quarter of the way through.
Ask the AI to generate correctly typed seed data for your database, using realistic values. Again, the AI will be done long before you.
Try porting a library of helper functions from one language to another. This is another task where AI will win handily
Also, ask AI to write unit tests with mocks for your existing code. It's not amazing at integration tests but with mocks in play it slays.
It is true it is faster than humans at some tasks, but the remaining tasks were most of the time, you can't gain more than 11% speedup by speeding up 10% of the work.
Things don't move forward by saying it can't be done or belittle others accomplishments.
I appreciate it.
So humans do better than them in at least 80% of all code everywhere, if not 95% even? Cool, good to know.
Care to provide some examples to back your otherwise extraordinary claim btw?
I just can't make peace with the fact that I inhabit the same planet as people who can't make elementary distinctions.
And yet somehow want to act high and mighty and be insulting as fuck.
We can all Google stuff because internet is big and supports anyone's views, which means it's more important than ever to be informed and be able to infer well. Something that you seem to want to defer to sources that support your beliefs. Not nice finding that on a hacker forum but statistical outliers exist.
Live long and prosper. And be insulted, I suppose.
Fortunately, as pdimitar pointed out, so far it is an ineffective scam that mostly produces LoC.
Granted there are languages where typing takes much more time, like Java and C# but... eh. They are quite overdue for finding better syntax anyway! :)
As someone who uses ChatGPT and Claude daily, but cancelled my Copilot subscription after a year of use because it intimately just wasn’t that helpful to me and didn’t provide enough benefit over doing it by hand, I kind of sort of agree. Maybe not entirely, but I can’t shake the feeling that there might be some truth in it.
The code that AI generates for me is rarely good. It’s possible to get good code out of it, but it requires many iterations of careful review and prompting, but for most cases, I can write it quicker by hand. Where it really shines for me in programming and what I still use ChatGPT and Claude for is rubber ducking and as an alternative to documentation (eg “how do I do x in css”).
Besides the code quality being mediocre at best and outright rubbish at worst, it’s too much of a “yes man”, it’s lazy (choose between A and B: why not a hybrid approach? That’s… not what I asked for), and it doesn’t know how to say “I don’t know”.
I also feel it makes you, the human programmer, lazy. We need to exercise our brains, not delegate too much to a dumb computer.
I kinda feel like this isn't talked about enough, my main concern right from the beginning was that new programmers would rely on it too much and never improve their own abilities.
I know c++ dart golang java html css javascript typescript lua react vue angular angularjs c# swift sql in various dialects including mysql and postgres, and have worked professionally in all these regards. I love to challenge myself. In fact, if I done something before, I find it boring.
So copilot helps me because I always find something new to do, something I don't understand, something I'm not good at.
So yes, I'm confident I'm competent. But I always do things I'm not good at for fun. So it helps me become well rounded.
So your assertion it only helps me because I'm incompetent is true and false. I'm competent, I just like to do new stuff.
I mean it does, sometimes, but usually it's either boilerplate or something you don't care about. Boilerplate is mostly managed very well by most well-known IDEs. And neither them nor CoPilot are offering good algorithmic code... OK, I'll grant you the "most of the time, not never" thing.
This statement would have been a huge red flag for me, if I had interviewed you. Don't get me wrong, you could use and program in 10 languages. Maybe you can be proficient in many at different times of your life. But know them at once? No.
I think the unfortunate reality is that this makes up a shockingly large amount of software engineering. Take this object and put it into this other object, map this data to that data, create these records and move them into this object when then goes into that other object.
How quickly the goalposts move! Two years ago it was "AI will never be able to write code", now we're complaining that "AI is not a seasoned programmer". What are we going to be complaining about, two years from now?
GitHub Copilot was released nearly three years ago.
Copilot gives you some autofill that sometimes can be helpful but often not that helpful. I think the best it did for me was helping with something repetitive where I was editing a big list of things in the same way (like adding an ID to every tag in a list) and it helped take over and finish the task with a little less manual clicking.
ChatGPT has helped with small code snippets like writing a regular expression. I never got 100% regex mastery, usually I would have to look up a couple things to write one but GPT can shortcut that process. I get a little paranoid about AI provided code not actually working so I end up writing a large number of tests to check it, which could be a good thing but can feel tedious.
I'm also curious how other people are leveraging them to get more than I am. I honestly don't try too hard. At one point I did try really hard to get AI to do more heavy code lifting but was disappointed with my results so I stopped... but maybe things have improved a bit since then.
I don’t remember the last time I thought one of its suggestions was useful. For me LSP has been the real game changer.
That's why there are a lot of tools that help to setup a proper workflow around these LLMs.
For terminal-based workflow, you can checkout aider or plandex.
For GUI-based workflow, you can try 16x Prompt (I built it).
This is a good thing. We need more tests on such critical places like regexes because they can be finicky and non-obvious. Tedious or not, we are not artists; the job must be done. Kudos for sticking to the good practices.
I pay for Leetcode, which usually gives editorial examples in Python and Java and such, and paste it into ChatGPT and say "translate this to a language I am more familiar with" (actually I have other programs that have been doing this for some language to language conversions for years, without AI). Then I say "make it more compact". Then again "make it more compact". So soon I have a big O(n) time, big O(1) space solution to Leetcode question #2718 or whatever in a language I am familiar with. Actually sometimes it becomes too compact and unreadable, and I back it up a little.
Sometimes it hallucinates, but it has been helpful. In the past I had problems with it, but not recently.
I’m an experienced backend dev who’s been working on some Vue frontend projects, and it’s significantly accelerated my ability to learn the complexities of e.g. Vue’s reactivity model. I can ask a complex question that involves several niche concepts and get a response that correctly synthesizes those concepts. I spent an hour the other night trying to understand a bug in a component to no avail; once I understood the problem well enough to explain it in a few sentences, Claude diagnosed the issue and explained it with more clarity than the documentation and various stack overflow answers.
My default is no longer to assume that the model has a coin flip’s chance of producing bs. I still verify and treat answers with a certain degree of skepticism, but I now reach for it as my first tool rather than a last resort or a gimmick.
// reverse list
And it writes code in the proper language.
I have accounts for all three and will generally try to branch out to test them with each new update. Admittedly, I haven’t gotten to Grok yet, but Claude is far and away the best model at the moment. It’s not even close really.
The manual is my first tool.
Since I have been using it so long, I have a really good intuition of what it is “thinking” in every scenario and a pretty good idea of what it can do for me. So that helps me get more use out of it.
So for example one of the projects I’m doing now is a flutter project - my first one. So I don’t remember all the widgets. But I just write a comment:
// this widget does XYZ
And it will write something that is in the right direction.
The other thing it knows super well is like rote code, and for context, it reads the whole file. So like Dart, for example is awful at json. So you have to write “toMap” for each freaking class where you do key values to generate a map which can be turned into json. Same goes for fromMap. So annoying.
But with copilot? You just write “toMap” and it reads all your properties and suggests a near perfect implementation. So much time saved!
I think people just want to be part of the hype and use the cool new technology whenever possible. We've seen this over and over again: Machine Learning, Blockchains, Cryptos, "Big Data", "Micro Services", "Kubernetes", etc.
I just don't think the current design of "AI" will take us there..
And that are just a tiny upgrade over what IDEs can do. When I used Android Studio, the code basically write itself due to the boilerplate surrounding your business logic. And once I got a basic structure down, I feel like I only write 5 to 10 characters each line (less for data types). And the upgrade is both positive and negative at the same time, it boils down to luck to actually get good suggestions.
Isn't the energy consumption of this technology pretty catastrophic? Do you consider the issue of energy consumption so abstracted you don't worry about it? Do you do anything to offset your increased carbon emissions?
There is about zero chance OpenAI are running their service at a 70% negative gross margin.
The math just doesn't work. They're hemorrhaging money as far as I can tell (not counting the Azure computing deal).
We can only guess, but my guess is that inference is still a good chunk of their costs. That's why they're trying to get the mini/turbo models into a usable state.
Even then, training is still an expense. And it's not like you can train and forget. Even if your model is already trained you still need to incorporate new knowledge over time.
Yes. Quite a lot. I walk the talk.
ChatGPT truly is impressive. Nonetheless, i still think most companies integrating "AI" into their products is buzzword bs that is all going to collapse in on itself.
NVDA's high closes were $135.58 June 18, down to $134.91 July 10th and $130 close today. It's highest sale is $140.76. So it's close today is 8% off its highest sale ever, and 4% off its highest close ever, not a big thing for a volatile stock. It's earnings are next week and we'll see how it does.
Nvidia and SMCI are the ones who have been earning money selling equipment for "AI". For Microsoft, Google, Facebook, Amazon, OpenAI etc., it is all big initial capital expenditure which they (and the scolding investment bank analysts) hope to regain in the future.
Meanwhile we’re seeing the first of the new generation of on-device inference chips being shipped as commodity edge compute.
When the devices you use every day — cars, doorbells, TV remotes, points-of-sale, roombas — can interpret camera and speech input locally in the time it takes to draw a frame and with low enough power to still give you 10h between charges: then we’ll be due another round of innovation.
The article points to how few parts of the economy are leveraging the text-only API products currently available. That still feels very Web 1.0, for me.
That’s gonna be a bad take I think.
> The new crop of intelligent agents are different from the automated devices of earlier eras because of their computational power. They have Turing-machine powers, they take over human tasks, and they interact with people in human-like ways-perhaps with a form of natural language, perhaps with animated graphics or video. Some agents have the potential to form their own goals and intentions. to initiate actions on their own without explicit instruction or guidance, and to offer suggestions to people. Thus, agents might set up schedules, reserve hotel and meeting rooms, arrange transportation, and even outline meeting topics, all without human intervention.
I think that’s what people like about AI, it’s hope, maybe you won’t have to learn anything but still be productive. Sounds nice ?
Non techies that now are suggesting how I design solutions for them by asking ChatGPT. And they seem to treat me like the stupid one for refusing.
The Economist, seriously?
In other words, lot of people seem to think that human attention spans are what determine everything, but the technological cycles at work here are much much deeper.
Personally I have used Midjourney and ChatGPT in ways that will have huge impacts on many activities and industries. Denying that because of media trendiness about AI seems shortsighted.
Please tell that to all types on HN who downvote anything related to Rust without even reading past the title. :D
> In other words, lot of people seem to think that human attention spans are what determine everything, but the technological cycles at work here are much much deeper.
IMO no reasonable person denies this, it's just that the "AI" technology regularly over-promises and under-delivers. At one point it's no longer discrimination, it's just good old pattern recognition.
> Personally I have used Midjourney and ChatGPT in ways that will have huge impacts on many activities and industries. Denying that because of media trendiness about AI seems shortsighted.
Some examples with actual links would go a long way. I for one am skeptical of your claim but I am open to have my mind changed (f.ex. my CFO told me once that ChatGPT helped him catch several bad contract clauses).
But if you insist...here are two very small examples from my personal experience with AI tools.
1. I work as a technical writer. Recently I needed to add a summary section to the introduction of a large number of articles. So, I copied the article into ChatGPT and told it to summarize the piece into 3-4 bullet points. Were I doing this task a few years ago, I would have read each article and then written the bullet points myself – nothing particularly difficult, but very time-consuming to do for dozens of articles. Instead, I used ChatGPT and saved myself hours upon hours of mundane work.
This is a quite minor and mundane example, but you can (hopefully) see how this will have major effects on any kind of routine text-creation.
2. I am working on a side project which requires the creation of a large number of custom images. I've had this project idea for a few years, but previously couldn't afford to spend $20k hiring an illustrator to make them all. Now with Midjourney, I am able to create essentially unlimited images for $30-100 a month. This new AI tool has quite literally unlocked a new business idea that was previously inaccessible.
Have you considered that you getting almost angry at somebody "not seeing the light" means you might hold preconceived notions that might not hold to reality? You would not be practicing critical thinking if you are not willing to question your assumptions.
It seems your assumption is very standard: "revolution is just around the corner, how can you not see it?".
OK, let the revolution come and I'll apologize to you personally. Ping me when it happens. For real. But make sure it's an actual revolution and not "OMFG next Midjourney can produce moon-scapes!", okay?
---
RE: 1, cool, I heard such success stories and I like them. But I also heard about executives flunking contracts because they over-relied on ChatGPT to summarize / synthesize contract items. I am glad it's making progress but people are being people and they will rely on a 100% fault-free AI. If that's not in place yet then the usefulness drops sharply because double-checking is even more time-consuming than doing the thing by yourself in the first place.
RE: 2, your side projects are not representative of anything at all. And I for one recognize AI images from a mile away and steer clear of projects that make use of them. Smells like low-effort to me and makes me wonder if the author didn't take other, much more fatal, shortcuts (like losing my information or selling my PII). And yes I am not the only one -- before you attempt that low-effort ad hominem technique.
I was not convinced by your comment, very little facts and it mostly appeals to the future that's forever just around the corner. Surely as an engineering-minded person you see how that's not convincing?
My experience is in no way unique, and yes, I think it's just laziness or ignorance to think otherwise. Or in your case, a kind of zealous hostility as a reaction against hype.
I remind you that my initial comment said that yes, there are some aspects of AI that are definitely over-hyped, but that I have used the tools in ways that obviously seem to have huge economic impacts.
P.S. - if you were more familiar with AI image makers, you'd know that it's not difficult to make images that are indistinguishable from non-AI ones. But that's really not relevant here, because my point was that this new tool enabled me to create something that didn't exist before – not what your personal qualms were about AI images.
This is quite literally the entire history of technology: what was once expensive becomes cheap and then unlocks new developments. Bizarre that I have to point this out when you’re likely reading this comment on a device made of commoditized components that cost a fraction of what they did a couple decades ago.
Yeah it does that, in 2-3 areas, those we need the least -- who cares it can replace artists? We need elder people care! We need automated logistics! And a tons of other things.
"It's just the beginning" yeah yeah, but it's not. It's actually the next AI plateau that the area will need a long time to move on from. Please do quote me on this, I am willing to apologize if I am wrong after 5-10 years.
So I've seen how the field has progressed and also have been able to look at it from a perspective most AI/engineering people don't -- what does this artificial intelligence look like when compared to biological intelligence. And I must say I am absolutely astonished people don't see this as opening the flood-gates to staggeringly powerful artificial intelligence. We've run the 4-minute mile. There are hundreds of billions of dollars figuring out how to get to the next level, and it's clear we are close. Forget what the current models are doing, it is what the next big leap (most likely with some new architecture change) will bring.
In focusing on intelligence we forget that it's most likely a much easier challenge than decentralized cheap autonomy, which is what took the planet 4 billion years to figure out. Once that was done, intelligence as we recognize it took an eye-blink. Just like with powered-flight we don't need bioliogical intelligence to transform the world. Artificial intelligence that guzzles electricity, is brittle, has blind spots, but still capable of 1000 times more than the best among us is going to be here within the next decade. It's not here yet, no doubt, but I am yet to see any reasoned argument for why it is far more difficult and will take far longer. We are in for radical non-linear change.
I am yet to see any reasoned argument for why it is easy to build real AI and that it will come fast.
As you said, AI has been there for decades and stagnated for pretty much the whole time. We've just had a big leap, but nothing says (except BS hype) that we're not in for a long plateau again.
This has to do with ML and numerical computing, how?
Either make a good argument or don’t.
Don't feel too bad; until I read the next response I was in two minds about whether sarcasm was intended or not.
It's bloody hard to tell, sometimes :-/
> Either make a good argument or don’t.
Human beings can't evaulate the truth of things based only on the argument. Persuasive liars, cons, and incompetents are a very known phenomenon. Most of human history we misunderstood nature and many other things because we relied on 'good arguments'. Not that we need it, but research shows that human intuition about the truth of something isn't good without expertise.
When I need medical advice, I get it from someone who has convinced me that they have expertise; I don't look for 'good arguments' that persuade me, because I don't know what I'm talking about.
I have expertise in other things. In those fields, I could easily persuade people without it of just about anything. (I don't; I'm not a sociopath.) I imagine anyone with professional expertise who reads this can do the same.
edit: oh sorry I didn't get that it was sarcasm
We have "real ai" already.
As for future progress, have you tried just simple interpolation of the progress so far? Human level intelligence is very near. (Though of course artificial intelligence will never exactly match human intelligence: it will be ahead/behind in certain aspects...)
- Simple interpolation of the progress is exactly the problem here. Look at the historical graphs of AI funding and tell me with a straight face that we absolutely must use simple interpolation.
- Nope, human-level intelligence is not even close. It remains as nebulous and out of reach as ever. ChatGPT's imitation of intelligent speech falls apart very quickly when you chat with it for more than a few questions.
I think we expect AGI to be much smarter than the average joe, and free of occasional stupidity.
What we’ve got is an 85IQ generalist with unreliable savant capabilities, that can also talk to a million people at the same time without getting distracted. I don’t see how that isn’t absolutely a fundamental shift in capability.
It’s just that we expect it to be spectacularly useful. Not like homeless joe, who lives down by the river. Unfortunately, nobody wants a 40 acre call center of homeless joes, but it’s hard to argue that HJ isn’t an intelligent entity.
Obviously LLMs don’t yet have a control and supervision loop that gives them goal directed behaviour, but they also don’t have a drinking problem and debilitating PTSD with a little TBI thrown in from the last war.
It’s not that we aren’t on the cusp of general intelligence, it’s that we have a distorted idea of how useful that should be.
Very shallow assessment, first of all it's not a generalist at all, it has zero concept of what it's talking about, secondly it gets confused easily unless you order it to keep context in memory, and thirdly it can't perform if it does not regularly swallow petabytes of human text.
I get your optimism but it's uninformed.
> To be fair, I’ve talked to a lot of people who cannot consistently perform at the mistral-12b level.
I can find you an old-school bot that performs better than uneducated members of marginalized and super poor communities, what is your example even supposed to prove?
> it’s hard to argue that HJ isn’t an intelligent entity.
What's HJ? If it's not a human then it's extremely easy to argue that it's not an intelligent entity. We don't have intelligent machine entities, we have stochastic parrots and it's weird to pretend otherwise when the algorithms are well-known and it's very visible there's no self-optimization in there, there's no actual learning, there's only adjusting weights (and this is not what our actual neurons do btw), there's no motivation or self-drive to continue learning, there's barely anything that has been "taught" to combine segments of human speech and somehow that's a huge achievement. Sure.
> It’s not that we aren’t on the cusp of general intelligence, it’s that we have a distorted idea of how useful that should be.
Nah, we are on no cusp of general AGI at all. We're not even at 1%. Don't know about you but I have a very clear idea what would AGI look like and LLMs are nowhere near. Not even in the same ballpark.
It helps that I am not in the area and I don't feel the need to pat myself on the back that I have managed to achieve the next AI plateau which the area will not soon recover from.
Bookmark this comment and tell me I am wrong in 10 years, I dare you.
TBH I hope im wrong, and that there is magic in HJ that makes him special in the universe in a way that GPT26 can never be. But increasingly, I doubt this premise. Not because of the "amazing capabilities of LLMs" which i think are frequently overstated and largely misunderstood, but more because of the dumbfounding shortcomings of intelligent creatures. We keep moving the bar for AGI, and now AGI is assumed to be what any rational accounting would classfy as ASI.
Where we are really going to see the bloom of AI is in goal directed systems, and I think those will come naturally with robotics. I predict we are in for a very abrupt 2nd industrial revolution, and you and I will be able to have this discussion either over a 55 gallon barrel of burning trash, or in our robot manicured botanical gardens sometime in the near future lol.
good times, maybe. Interesting times , for sure.
We have found common ground.
> but more because of the dumbfounding shortcomings of intelligent creatures
Yes, a lot of us utilize defective judgments, myself included, fairly often. My point was that LLMs, for all their praise, can't even reach 10% of an average semi-intelligent organic being.
> We keep moving the bar for AGI, and now AGI is assumed to be what any rational accounting would classfy as ASI.
I don't know who is "we" (and I wish people stopped pretending that "we" are all a homogenous mass) but I've known what an AGI should be ever since I've watched movies about Skynet and HAL-9000. ¯\_(ツ)_/¯
Secondly, it's the so-called "AI practitioners" who constantly move the goal posts (now there's "ASI"? -- you know what, I actually don't want to know) because they're periodically being called out and can't hide the fact that they have nearly nothing again. So what's better than obfuscating that fact by having 100+ acronyms? It's a nice cover and apparently there are still investors who are buying it. I get it, we have to learn to say the right things to get funding.
> Where we are really going to see the bloom of AI is in goal directed systems, and I think those will come naturally with robotics.
I agree. Physical feedback is needed if we want an electronic entity to "evolve" similarly to us.
> I predict we are in for a very abrupt 2nd industrial revolution, and you and I will be able to have this discussion either over a 55 gallon barrel of burning trash, or in our robot manicured botanical gardens sometime in the near future lol.
I agree this is 100% inevitable but I don't think it's coming as soon as you say. The LLMs are hopelessly stuck even today and the whole AI area will suffer for it for a while after the bubble bursts... which is the event that I am certain is coming soon.
At that time, general intelligence was imagined like what a mouse, dog, or cat has, in varying degrees. (it was then broadly thought that insects and other "simple" organisms worked from instinct and conditioning only) So intelligence was broadly imagined to be the ability to reason through an arbitrary problem with a combination of insight and trial and error. It had nothing at all to do with reaching human levels of competence.
Gradually, the bar for AGI has slipped skyward to assume human level competence. I think that part of that was because we actually started checking boxes for tasks that were thought to represent "true" AI....
...But then we recognized that nope, it is not yet generally intelligent, it can just easily and accurately do (X task which was thought to require intelligence but really was only pattern matching or stochastic prediction). So, we raise the bar above task X and trivialize it as being less important than we thought.
Meanwhile, -nearly every task- that we thought would represent "true intelligence" has fallen not to some magic AI algorithm, but rather to stochastic models like transformers, pattern matching, or straightforward computation. With no reason to expect otherwise, I expect this trend to continue unabated.
So, what I'm saying is I have come to doubt that there is some secret sauce in "intelligence", but instead believe that "intelligence" is a blend of competencies enabled in animals by specialization of neural computational structures, "REPL" loops, goal seeking behaviors, and other tidbits which will be more of a slog than a eureka. I don't think that LLM's will create human-level intelligence on their own, but I do think that they will be an important component.
I also think that surprisingly, transformers all by them selves exhibit a flaky variety of general intelligence, and they are proving effective in robotic applications (though without a supervisory agent I suspect they will frequently go off the rails, so to speak.)
My original point was more that we expect machine general intelligence to be spectacularly useful. It may be, someday, but it is a kind of fallacy to think that "its not that useful therefore it must not really be intelligence".
Small animals also have limited utility, but with given names, language, tool use, and problem solving skills I think arguing that they do not exhibit "intelligence" would be a tough sell for me.
By my observation, we have made giant leaps in the past 15 years, and we now have perhaps the vast majority of the components required to make artificial general intelligence...but it won't necessarily be all that useful at first, except maybe as a "pet robot" or something like that. Even if we scale it, it might not magically get smarter, just faster. a million hyper-speed squirrels still has a very limited level of utility.
From there, we will incrementally improve if we don't stumble too hard over ourselves on any number of pressing obstacles that we currently face, until we finally succeed in doing what we were apparently born to do - to construct the means of our own irrelevance. Evolution at work, I suppose.
> Back in the days of lisp machines and expert systems, we started playing with early neural networks in an attempt to advance towards 'general intelligence' rather than the highly constrained talents of expert systems.
Interesting, you must be the first person I "meet" who was "back then and there". Still, if you allow me to point out for the second time, your "we" is really throwing me off because it sounds like "we the council of elders" and "we who self-appointed to determine what an actual true AI is". Positions of implied or directly claimed authority murder my motivation to take people doing them seriously. Hopefully that's a useful piece of feedback for you.
I would think that a random guy like myself who watched Terminator and that was part of his inspiration to become a programmer has just as much "authority" (if we can even call it that but I can't find the right word at this moment) to claim what a general AI should be. Since we don't have it, why not try and dream the ideal AI for us, and then pursue that? It's what the people who wanted humanity on the Moon did after all.
I feel too many people try to define general AI through the lenses of what we have right now -- or we'll have very soon -- and that to me seems very short-sighted and narrow-minded and seems like bronze-age people trying to predict what technology would be. To them it would likely be better carts that shake less while sitting in them. And faster cart-pulling animals.
That's how current AI practitioners trying to enforce their view on what we should expect sound to me.
> Meanwhile, -nearly every task- that we thought would represent "true intelligence" has fallen not to some magic AI algorithm, but rather to stochastic models like transformers, pattern matching, or straightforward computation. With no reason to expect otherwise, I expect this trend to continue unabated.
Sure, but this gets dangerously close to the disingenuous argument of "people who want AI constantly move the goalposts every time we make progress!" which is a stance I can't disagree with more even if I tried. I in fact hate this trope and fight it at every opportunity.
Why? Because to me that looks like AI practitioners are weaseling out of responsibility. It's in fact not that difficult to understand what the common people would want. Take a look at the "I, Robot" movie -- robot butlers that can do many tasks around the house or even assist you when you are outside.
What does that take? Let the practitioners figure it out. I, like yourself, believe LLMs are definitely not that -- but you are also right that it's likely a key ingredient. Being able to semi-informedly and quickly digest and process text is indeed crucial.
The part I hate is the constant chest-pounding: "We practically have general AI now, you plebs don't know our specialized terms and you just don't get it. Stop it with your claims that we don't have it! Nevermind that we don't have robot butlers, that's always in the future, I am telling you!".
And yes that happens even here in this thread, not in that super direct form of course, but it still happens.
> My original point was more that we expect machine general intelligence to be spectacularly useful. It may be, someday, but it is a kind of fallacy to think that "its not that useful therefore it must not really be intelligence".
Here we disagree. It's true that people want useful and don't care how it's achieved; as a fairly disgruntled-by-tech guy I want the same. Put rat brains in my half-intelligent but problem-solving butler for all I care; if it works well people will buy it en masse and ask zero questions.
...But I'd still put strong correlation between a...
It’s possible that others imagined human level as the base for “generalized intelligence”, but my colleagues and I were taking the term general to mean generalised, as in not narrowly defined (like expert systems are). A type of intelligence that could be applied broadly to different categories of problems, including ones not foreseen by the designer of the system. That these problems might be very basic ones was immaterial.
That this concept of general intelligence is not necessarily life transforming to possess on your smartphone doesn’t mean it isn’t a huge step forward. It is a very hard problem to solve. Transformer networks are the best tool we have for this task, and they work by inferring meaning to patterns and outputting a transform of that meaning. With LLMs, the pattern is the context text string, and the output is the next likely text fragment.
The surprising effectiveness of LLMs is due, I think, not to any characteristic of their architecture that makes them “intelligent”, but rather due to the fundamental nature of language itself, and especially the English language because of its penchant for specificity and its somewhat lower reliance on intonation to convey meaning than most languages. (I’m not a linguist, but I have discussed this with a few and it is interesting to hear their ideas on this)
Language itself captures meanings far beyond the words used in a statement. Only a tiny fraction of information is contained in words, the rest is inferred knowledge based on assumptions about shared experiences and understandings. Transformers tease out this context and imbed it through inferences constructed by billions of textual inputs. They capture the information shadows cast by the words, not just the words themselves.
This way of decoding cultural data, as an n-dimensional matrix of vast proportions rather than just the text of a culture itself, turns out to be a way to access both the explicit and the implicit knowledge imbedded in that culture, especially if that culture is codified using very specific and expressive tokens.
It turns out that this capture of shared experiences and understanding enables a great deal of abstract general problem solving, precisely the kind of problem solving that is vexingly difficult to solve using other methods.
LLMs, not just transformers, are actually a really big deal. In effect, they create a kind of probabilistic expert system where the field of expertise is a significant fraction of the sum total of human thought and experience.
But there are numerous and significant shortcomings to this approach, of course…. Not the least of which is the difficulty to effectively integrate new information or to selectively replace or update existing data in the model. And hallucinations (which are not a malfunction , but rather completely normal operations) are a basically unsolvable problem, though they can be mitigated.
But anyway…
I think the real insight to be had here , or at least my personal takeaway is that we owe a great deal of what we think of as intelligence to our culture than to our individual intellectual prowess. As we solve the problems of general intelligence, we both construct marvellous machines and confront the suggestion that we aren’t nearly as clever as we thought we were.
As for “AGI” I will still call that a crossed threshold , but certainly not to the level that humans have solved “biological GI” with full inculturation.
If people want AGI to mean human level competence, I’m ok with that. It won’t be the first term of art to have drifted in meaning. I would personally choose a more descriptive term for that, but 3 letter terms are not all that expressive, and adding another letter or two just gets awkward, so I get it. And I think it’s a lost battle anyway, it’s mostly old...
What is intelligence? We must have very different definitions!
And I am pretty sure my own intelligence goes much farther than regurgitating text that I have no clue about (like ChatGPT does not have symbol logic that links words with objects it can "feel" physically or otherwise).
This is honestly one of the most gpt-2 things I’ve ever read.
But it's a building block. And when used well it may be possible to get to zero hallucinations and good accuracy in question answering for limited domains - like the call center.
You shouldn’t use science fiction as your reference point. It’s like saying “where is my flying car?” (Helicopters exist)
And btw in the Terminator novelizations it was clearly stated that Skynet was a very good optimization machine but lacked creativity. So it's actually a good benchmark: can we create an intelligent machine that needs no supervision but still has limitations (i.e. it cannot dramatically reformulate its strategy in case it cannot win, which is exactly what happened in the books)?
That’s the point of my flying car comparison. We HAVE flying cars: they’re called helicopters. Because as it turns out there is just no physical way to make a vehicle in the form factor of a car fly, except by rotary wing. But people will still say “where’s my flying car?” because they are hung up on reality resembling science fantasy, as you are.
We have AI. We even already have AGI. It just doesn’t resemble the Terminator, because The Terminator is a made up story disconnected from reality.
And this is why, I feel, I can never discuss with the AI fans. They are happy to invent their own fiction while berating popular fiction in the same breath.
No, we really don't have AGI. Feel free to point out some of humanity's pressing problems being trivially solved today with it, please. I'll start: elderly people care, and fully automated logistics.
Artificial. General. Intelligence.
The term, as originally defined, is for programs which are man-made (Artificial), able to efficiently solve problems (Intelligence), including novel problem domains outside those considered in its creation (General). Artificial General Intelligence, or AGI. That’s literally all AGI means, and ChatGPT absolutely fits the bill.
What you describe is ASI, or artificial super intelligence. In the late 90’s, 00’s, and early 10’s, a weird subgroup of AI nerds got it into their head that merely making an AGI (even a primitive one) would cause a self-recursion improvement loop and create ASI in short order. They then started saying “achieve AGI” as a stand in for “emergence of ASI” as the two were intricately linked in their mind.
In reality the whole notion of AGI->ASI auto-FOOM has been experimentally discredited, but the confusion over terminology remains.
Furthermore, the very idea of ASI can’t be taken for granted. A machine that trivially solves humanity’s pressing problems makes nice sci-fi, but there is absolutely no evidence to presume such a machine could actually exist.
I don't care how it's called. We don't have it. I am not "confused over terminology", I want to see results and yet again they don't exist. Let's focus on results.
> In reality the whole notion of AGI->ASI auto-FOOM has been experimentally discredited
Sure. Because we actually have this super-intelligence already and we can compare with it, right? Oh wait, no we don't. So what's your point? Some people gave up and proclaimed that it can't be done? Like we haven't seen historical examples of this meaning exactly nothing, hundreds of times already.
Look, we'll never be able to talk about it before you stop confusing industry gate-keepers who learned how to talk to get VC money and obfuscate reality with, you know, the actual reality in front of us. You got duped by the investor talk and by the scientists never wanting to admit their funding might have been misplaced by being given to them, I am afraid.
Finally, nope, again and again, we don't have AGI even if I accept your definition. Show me a bot that can play chess, play StarCraft 2, organize an Amazon warehouse item movements and shipping, and coordinate a flight's touch-down with the same algorithms / core / whatever-you-want-to-call it. Same one, not different ones. One and the same.
No? No AGI then either.
> Furthermore, the very idea of ASI can’t be taken for granted. A machine that trivially solves humanity’s pressing problems makes nice sci-fi, but there is absolutely no evidence to presume such a machine could actually exist.
The people in the bronze age could have easily said "there is no evidence we would be able to haul goods while only pressing pedals and rotating a wheel". That's not an argument for anything at all, it's a short-sighted assertion that we might never progress that's only taking the present and the very near future into account. Well, cool, you don't believe it will happen. And? That's not an interesting thing to say.
Other people didn't believe we could go to the Moon. We still did. I wonder how quickly the naysayers hid under the bed after that so nobody could confront them about it. :D
But anyway. I got nothing more to say to people who believe VC talk and are hell-bent on inventing many acronyms to make sure they are never held accountable.
I for one want machines that solve humanity's problems. I know they can exist. I know nearly nobody wants to work on them because everybody is focused on the next quarter's results. All this is visible and well-understood yet people like you seem to think that this super narrow view is the best humanity can achieve.
Well, maybe it's the best you can achieve. I know people who can do more.
Inventing a fricken time machine wasn't creative?
In the novelizations it was written that Skynet could not adapt to humans not running away or not vacating territories after they have been defeated. One of the quotes was: "Apparently it underestimated something that it kept analyzing even now: the human willpower." I've read this as Skynet not being able to adapt against guerilla warfare -- the hit-and-run/hide tactics.
But the TL;DR was that Skynet was basically playing something like StarCraft as if it played against another bot, and ultimately lost because it played against humans. That was the "Skynet was not creative" angle in the novelizations.
In Terminator 1 Skynet looses because John Conner taught people how to fight the machines, but John Conner only knows this because Kyle Reese taught Sarah Conner how to fight the machines and she taught John Conner. But Kyle Reese only knows this because he was taught by John Conner- so there's no actual source of the information on how to fight the machines, it's a loop with no beginning or end.
I had a philosophy teacher who said this is evidence of divine intervention to destroy Skynet, essentially God told people through John Conner how to win, but a cut scene in Terminator 1 implies Skynet was also created by reverse engineering the chip in the destroyed Terminator- implying there's also no origin of the information on how to create Skynet and it's also an infinite loop.
So it's not exactly an infinite loop IMO, it's more like that the first iteration was more crude and the machines were difficult to kill but then people learned and passed the information along back in time, eventually forming the infinite loop -- it still had a first step though, it didn't come out of nothing.
The thing that's different this time is the hardware capacity in TFLOPs and the like passing human brain equivalence.
There's a massive difference between much worse than human AI - a bit meh, and better than human AI - changes everything.
>any reasoned argument for why it is easy to build real AI and that it will come fast
It probably won't be easy but the huge value of better than human AI will ensure loads of the best and brightest working on it.
What signs do you see that make you believe that the next level (biological intelligence) is on the horizon?
If we’ve just copied our feeble abilities, is that supposed to be exciting?
Is god like intelligent just a prediction machine too ?
The Sixth Day
Then God said, “Let Us make man in Our image, after Our likeness, to rule over the fish of the sea and the birds of the air, over the livestock, and over all the earth itself and every creature that crawls upon it.” So God created man in His own image; in the image of God He created him; male and female He created them. ”…
Genesis 1:26-27 Berean Standard Bible
What does this mean, precisely? How is a human body (or plant, or insect, or reptile/bird/mammal) body ever "more than" it's constituent parts? Wouldn't that violate a conservation law?
This means you are sure we are close to automated driving, engineering and hospitality?
We already have "automated driving" in some sense. Some cities have fully autonomous taxi services that have operated for a year or more, iirc.
I don't really take those taxis as a form of solved automation. It's a nice step though.
For language models specifically, they are trained on data and have historically been improved by increasing the size of the model (by number of parameters) and by the amount and/or quality of training data.
We are basically out of new, non-synthetic text to train models on and it’s extremely hard work to come up with novel architecture that performs well against transformers.
Those are some simple reasons why it will be far more difficult to improve general language models.
There are also papers showing that training models on synthetic data causes “model collapse” and greatly reduces output quality by magnifying errors already present in the model, so it’s not a problem we can easily sidestep.
It’s an easy mistake to see something like chatgpt not exist, then suddenly exist and assume a major breakthrough happened, but behind the scenes there has been like 50 years of R&D that led to it, it’s not like suddenly there was a breakthrough and now the gates are open.
A general intelligence for CS is like the elixir of life for medicine.
this is not even remotely true.
There is an astronomical amount of data siloed by publishers, professional journals etc. that is yet to be tapped.
OpenAI is making inroads by making deals with these content owners for access to all that juicy data.
You seem to think these models haven't already been trained on pirated versions of this content, for some reason.
That’s not even considering AI crawlers or all the copyright text on archive.org
And by the way I can copy your post character by character, without hallucinating. So I am definitely better than this crop of "AI" in at least one dimension.
>We are in for radical non-linear change.
We aren't running miles much quicker than 4 mins though. The last record was 3m:43s set by Hicham El Guerrouj in 1999.
Scary stuff. And it's not science fiction- it's based on real, observed trends and scaling laws. Seems impossible? Well, they said the four-minute mile was impossible, too.
Humankind tried to break the 4 minute mile for hundreds of years - since measuring distance and time became accurate enough to be sure of both in the mid-18th century, at least - and failed.
In May 1954, Roger Bannister managed it. By late June it was done again by a different runner. Within 20 years the record was under 3:45, and today there are some runners who have achieved it more than 100 times and nearly 1800 runners who have done it at all.
Impossible for hundred of years, and then somebody did it, and people stopped thinking it was impossible and started doing it themselves. That’s the metaphor: sometimes we think of barriers that are really mental, not real.
I’m not sure that applies here either, but the point is not that progress is continuously exponential, but that once a barrier is conquered, we take on a perspective as if the barrier were never real in the first place. Powered flight went through this. Computing hardware too. It’s not an entirely foolish notion.
The breakthroughs where deep AI have excelled -- object recognition in images, voice recognition and generation, and text-based info embedding and retrieval -- these require none of the multilevel abstraction that characterizes higher cognition (Kahneman's system 2 thinking). When we see steady progress on such frontiers, only then a plausible case can and should be made that the essentials of AGI are indeed within our grasp. Until then, plateauing at a higher level of pattern matching than we had expected -- which is what we have seen many times before from narrow AI -- this is not sufficient evidence that the other requisite skills needed for AGI are surely just around the corner.
Coding assistants today are useful, image generation is useful, speach recognition/generation is useful.
All of these can support businesses, even in their current (early) state. Those businesses have value in funding even 1% improvements in engineering/science.
I think that this is different than before, where even in the 80s there were less defined products, amd most everything was a prototype that needed just a bit more research to be commercially viable.
Where as in the past, hopes for the technology waned and funding for research dropped off a cliff, today's stuff is useful now, and so companies will continue to spend some amount on the research side.
Anyway, considering all these things can be done on device, where is the long term business prospect of which you speak?
Now try to mute a video on youtube and understand what's being said from the automatic subtitles.
If you do it in english, be aware that it's the best performing language and all others are even worse.
1. It's overwhelmingly more useful than the [no text] it was replacing, particularly for the deaf or if you want to search for keywords in a video.
2. When it fails, it tends to do so in ways that trigger human suspicion and oversight.
Those aren't necessarily true of some of the things people are shoehorning LLMs into these days, which is why I'm a lost more pessimistic about that technology.
https://news.ycombinator.com/item?id=41199567#41201773
I expect that YouTube will up their transcription game soon, too.
Basically it's kinda useful to put time tags, but I need to manually fix each and every sentence. Sometimes I need to fix the time tags as well.
I just spoke about youtube because it's more popular and easy to test.
I've come to notice a correlation between contemporary AI optimism and having effectively made the jump to coding with AI assistants.
I think this depend heavily on what type of coding your doing. The more your job could be replaced by copy/pasting from Stack Overflow, the more useful you find coding assistants.
For that past few years most of the code I've written has been solving fairly niche quantitative problems with novel approaches and I've found AI coding assistants to range from useless to harmful.
But on a recent webdev project, they were much more useful. The vast majority of problems in webdev are fundamentally not unique so a searchable pattern library (which is what an LLM coding assistant basically is) should be pretty effective.
For other areas of software, they're not nearly as useful.
I think this is true and also why you see some "older devs just don't like AI" type comments. AI assistants seem to be great at simple webdev tasks, which also happens to be the type of work that more junior developers do day to day.
I have also found them useful with that and I keep one active for those types of projects because of the speed up, although I still have to keep a close eye on what it wants to inject. They also seem to excel at generating tests if you have already developed the functions.
Then there are more difficult (usually not webdev) projects. In those cases, it really only shines if I need to ask it a question that I would previously have searched on SO or some obscure board for an answer. And even then, it really has to be scrutinized, because if it was simple, I wouldn't be asking the question.
There is def. something there for specific types of development, but it has not "changed my life" or anything like that. It might have if I was just starting out or if I only did webdev type projects.
If this thing also conferred an actual productivity advantage that would be one thing, and it might motivate me to get past the horrible UX, but I haven't seen any evidence yet.
But after that we ran into a kind of loop, where you put my feelings into much better words than I could. If I had stopped after iteration 3, I probably would have finished what I wanted to do in half a day
This has follow-on consequences for a shattering phase transition between “persuasive demo” and “useful product”.
We can now make arbitrarily convincing demos that will crash airplanes (“with no survivors!”) on the first try in production.
This is institutionalized by the market capitalizations of 7 companies being so inflated that if they were priced accurately the US economy would collapse.
Really? I work in AI and my biggest concern is that I don't see any real products coming out of this space. I work closer to the models, and people in this specific area are making progress, but when I look at what's being done down stream I see nothing, save demos that don't scale beyond a few examples.
> in the 80s there were less defined products, amd most everything was a prototype that needed just a bit more research to be commercially viable.
This is literally all I see right now. There's some really fun hobbyist stuff happening in the image gen area that I think is here to stay, but LLMs haven't broken out of the "autocomplete on steroids" use cases.
> today's stuff is useful now
Can you give me examples of 5, non-coding assistant, profitable use cases for LLMs that aren't still in the "needed just a bit more research to be commercially viable" stage?
I love working in AI, think the technology is amazing, and do think there are some under exploited (though less exciting) use cases, but all I see if big promises with under delivery. I would love to be proven wrong.
LLMs can be used to generate high-quality, human-like content such as articles, blog posts, social media posts, and even short stories. Businesses can leverage this capability to save time and resources on content creation, and improve the consistency and quality of their online presence.
2. Customer Service and Support:
LLMs can be integrated into chatbots and virtual assistants to provide fast, accurate, and personalized responses to customer inquiries. This can help businesses improve their customer experience, reduce the workload on human customer service representatives, and provide 24/7 support.
3. Summarization and Insights:
LLMs can be used to analyze large volumes of text data, such as reports, research papers, or customer feedback, and generate concise summaries and insights. This can be valuable for businesses in fields like market research, financial analysis, or strategic planning.
4. HR Candidate Screening:
Use case: Using LLMs to assess job applicant resumes, cover letters, and interview responses to identify the most qualified candidates. Example: A large retailer integrating an LLM-based recruiting assistant to help sift through hundreds of applications for entry-level roles.
5. Legal Document Review:
Use case: Employing LLMs to rapidly scan through large volumes of legal contracts, case files, and regulatory documents to identify key terms, risks, and relevant information. Example: A corporate law firm deploying an LLM tool to streamline the due diligence process for mergers and acquisitions.
Spam isn't a feature. See also, this whole message that could just have been the headlines.
> 2. Customer Service and Support:
So… less clear than the website and not empowered to do anything (beyond ruining your reputation) because even you don't trust it?
> 3. Summarization and Insights:
See 1, spam isn't a feature. This is just trying to undo the damage from that (and failing).
> 4. HR Candidate Screening:
> 5. Legal Document Review:
If it's worth doing, it's worth doing well.
>Content Generation
I'm working on AI tools for teachers and I can confidently say that GPT is just unbelievably good at generating explanations, exercises, quizes etc. The onus to review the output is on the teacher obviously, but given they're the subject matter experts, a review is quick and takes a fraction of the time that it would take to otherwise create this content from scratch.
So sorry, we're back to spam generator. Even if it's "good spam".
a bit dramatic. there has to be an adjustment of teaching/assessing, but nothing that would "ruin" anyone's life.
>So sorry, we're back to spam generator. Even if it's "good spam".
is it spam if it's useful and solves a problem? I don't agree it fits the definition any more.
Teachers are under immense pressure, GPT allows a teacher to generate extension questions for gifted students or differentiate for less capable students, all on the fly. It can create CBT material tailored to a class or even an individual student. It's an extremely useful tool for capable teachers.
If you don't have the power to just change your mind about what the entire curriculum and/or assessment context is, it can be a workload increase of dozens of hours per week or more. If you do have the power, and do want to change your entire curriculum, it's hundreds of hours one-time. "Lives basically ruined" is an exaggeration, but you're preposterously understating the negative impact.
> is it spam if it's useful and solves a problem?
Whether or not it's useful has nothing to do with whether or not it's spam. I'm not claiming that your product is spam -- I'll get back to that -- but your reply to the spam accusation is completely wrong.
As for your hypothesis, I've had interactions where it did a good job of generating alternative activities/exercises, and interactions where it strenuously and lengthily kept suggesting absolute garbage. There's already garbage on the internet, we don't need LLMs to generate more. But yes, I've had situations where I got a good suggestion or two or three, in a list of ten or twenty, and although that's kind of blech, it's still better than not having the good suggestions.
I think it has a lot to do with it. I can't see how generating educational content for the purpose of enhancing student outcomes with content reviewed by expert teachers can fall under the category of spam.
>As for your hypothesis, I've had interactions where it did a good job of generating alternative activities/exercises, and interactions where it strenuously and lengthily kept suggesting absolute garbage.
I like to present concrete examples of what I would consider to be useful content for a k-12 teacher.
Here's a very quick example that I whipped up
https://chatgpt.com/share/ec0927bc-0407-478b-b8e5-47aabb52d2...
This would align with Year 9 Maths for the Australian Curriculum.
This is an extremely valuable tool for
- A graduate teacher struggling to keep up with creating resources for new classes
- An experienced teacher moving to a new subject area or year level
Bear in mind that the GPT output is not necessarily intended to be used verbatim. A qualified specialist teacher with often times 6 years of study (4 year undergrad + 2 yr Masters) is the expert in the room who presumably will review the output, adjust, elaborate etc.
As a launching pad for tailored content for a gifted student, or lower level, differentiated content for a struggling student the GPT response is absolutely phenomenal. Unbelievably good.
I've used Maths as an example, however it's also very good at giving topic overviews across the Australian Curriculum.
Here's one for: elements of poetry:structure and forms
https://chatgpt.com/share/979a33e5-0d2d-4213-af14-408385ed39...
Again, an amazing introduction to the topic (I can't remember the exact curriculum outcome it's aligned to) which gives the teacher a structured intro which can then be spun off into exercises, activities or deep dives into the sub topics.
> I've had situations where I got a good suggestion or two or three, in a list of ten or twenty
This is a result of poor prompting. I'm working with very structured, detailed curriculum documents and the output across subject areas is just unbelievably good.
This is all for a K-12 context.
Also, each of your examples had at least one error, did you not see them?
I didn't could you point them out?
>There are countless existing, human-vetted, designed on special purpose, bodies of work full of material like the stuff your chatgpt just "created". Why not use those?
As a classroom teacher I can tell you that piecing together existing resources is hard work and sometimes impossible because resource A is in this text book (which might not be digital) and resource B is on that website and quiz C is on another site. Sometimes it's impossible or very difficult to put all these pieces together in a cohesive manner. GPT can do all that an more.
The point is not to replace all existing resources with GPT, this is all or nothing logic. It's another tool in the tool belt which can save time and provide new ways of doing things.
Who said generating an essay is useful sorry ? What problem does that solve?
Your comments come accross as overly optimistic and dismissive . Like you have something to gain personally and aren’t interested in listening to others feedback.
>Who said generating an essay is useful sorry ? What problem does that solve?
Useful learning materials aligned with curriculum outcomes, taking into account learner needs and current level of understanding is literally the bread and butter of teaching.
I think those kinds of resources are both useful and solve a very real problem.
>Your comments come accross as overly optimistic and dismissive . Like you have something to gain personally and aren’t interested in listening to others feedback.
Fair point. I do have something to gain here. I've given a number of example prompts that are extremely useful for a working teacher in my replies to this thread. I don't think I'm being overly optimistic here. I'm not talking vague hypotheticals here, the tools that I'm building are already showing great usefulness.
Every year there are thousands of graduate teacher looking for tools to help them teach better.
>good teachers I know can generate them on the spot
Even the best teacher can't create an interactive multiple choice quiz with automatic marking, tailored to a specific class (or even a specific student) on the spot.
I've been teaching for 20+ years, I have a solid grasp of the pain points.
Neither can "AI" though, so what's the point here?
here's an example of a question and explanation which aligns to Australian Curriculum elaboration AC9M9A01_E4 explaining why frac{3^4}{3^4}=1, and 3^{4-4}=3^0
https://chatgpt.com/share/89c26d4f-2d8f-4043-acd7-f1c2be48c2...
to further elaborate why 3^0=1 https://chatgpt.com/share/9ca34c7f-49df-40ba-a9ef-cd21286392...
This is a relatively high level explanation. With proper prompting (which, sorry I don't have on hand right now) the explanation can be tailored to the target year level (Year 9 in this case) with exercises, additional examples and a quiz to test knowledge.
This is just the first example I have on hand and is just barely scratching the surface of what can be done.
The tools I'm building are aligned to the Austrlian Curriculum and as someone with a lot of classroom experience I can tell you that this kind of tailored content, explanations, exercises etc are a literal godsend for teachers regardless of experience level.
Bear in mind that the teacher with a 4 year undergrad in their specialist area and a Masters in teaching can use these initial explanations as a launching pad for generating tailored content for their class and even tailored content for individual students (either higher or lower level depending on student needs). The reason I mention this is because there is a lot of hand-wringing about hallucinations. To which my response is:
- After spending a lot of effort vetting the correctness of responses for a K-12 context hallucinations are not an issue. The training corpus is so saturated with correct data that this is not an issue in practice.
- In the unlikely scenario of hallucination, the response is vetted by a trained teacher who can quickly edit and adjust responses to suit their needs
“Here are some sharepoint locations, site Maps, and wikis. Now regurgitate this info to me as if you are a friendly call center agent.”
Pretty cool but not much more than pushing existing data around. True AI I think is being able to learn some baseline of skills and then through experience and feedback adapt and be able to formulate new thoughts that eventually become part of the learned information. That is what humans excel at and so far something LLMs can’t do. Given the inherent difficulty of the task I think we aren’t much closer to that than before as the problems seem algorithmic and not merely hardware constrained.
Which is extremely valuable!
>Pretty cool but not much more than pushing existing data around.
Don't underestimate how valuable it is for teachers to do exactly that. Taking existing information, making it digestable, presenting it in new and interseting ways is a teacher's bread and butter.
Are you suggesting a chatbot is a suitable replacement for a teacher?
No I'm saying that a chatbot can be a superhuman teacher's assistant.
Which all takes valuable time us teachers are extremely short on.
I've been a classroom teacher for more than 20 years, I know how painful it is to piece together a hodge podge of resourecs to put together lessons. Yes the information is out there, but a one click option to gather this into a cohesive unit for me saves me valuable time.
>95% of my time an mental capacity in this situation goes for deciding what makes sense in my particular pedagogical context? What wording works best for my particular students?
Which is exactly what GPT is amazing at.Brainstorming, rewriting, suggesting new angles of approach is GPTs main stength!
>Explanations are even harder.
Prompting GPT to give useful answers is part of the art of using these new tools. Ask GPT to speak in a different voice, take on a persona or target a differnt age group and you'll be amazed at what it can output.
> I find out almost daily that explanations which worked fine in last year, don't work any more
Exactly! Reframing your own point of view is hard work, GPT can be an invaluable assistant in this area.
No, it isn't. It just increases noise. I don't need any more info, I need just to make decisions "how?".
> Prompting GPT to give useful answers is part of the art of using these new tools. Ask GPT to speak in a different voice, take on a persona or target a differnt age group and you'll be amazed at what it can output.
I'm not amazed. At best it sounds like some 60+ year old (like me) trying to be in the "age group" 14 while after only hearing from someone how young people talk. Especially in small cultures like ours here (~1M people).
We specifically don’t do programming prompts/responses nor advanced college to PHD level stuff, but it’s really mediocre at this level and these subject areas. Programming might be another story, I can’t speak to that.
All I can go off is my experience but it’s not been great. I’m willing to be wrong.
Is the output of average C students not commercially valuable in the listed fields? If AI is competing reliably with students then we've already hit AGI.
1. content: Nope, except for barrel-bottom content sludge of the kind formerly done by third world spam spinning companies, most decent content creation stays well away from AI except for generating basic content layout templates. I work as a writer and even now, most companies stay well away from using GPT et al for anything they want to be respected as content. Please..
2. Customer service: You've just written a string of PR corporate-speak AI seller bullshit that barely corresponds to reality. People WANT to speak to humans, and except for very basic inquiries, they feel insulted if they're forced into interaction with some idiotic stochastic parrot of an AI for any serious customer support problems. Just imagine some guy trying to handle a major problem with his family's insurance claim or urgently access money that's been frozen in his bank account, and then forced to do these things via the half-baked bullshit funnel that is an AI. If you run a company that forces that upon me for anything serious in customer service, I would get you the fuck out of my life and recommend any friend willing to listen does the same.
3. This is the one area where I'd grant LLMs some major forward space, but even then with a very keen eye to reviewing anything they output for "hallucinations" and outright errors unless you flat out don't care about data or concept accuracy.
4. For reasons related to the above (especially #2) what a categorically terrible, rigid way to screen human beings with possible human qualities that aren't easily visible when examined by some piece of machine learning and its checkbox criteria.
5. Just, Fuck No... I'd run as fast and far as possible from anyone using LLMs to deal with complex legal issues that could involve my eventual imprisonment or lawsuit-induced bankruptcy.
Also these all tend to have an option where you simply ask it and it will redirect you to a person.
Those agents deal with the same queries all day, despite what you think your problem likely isn't special, in most cases may as well start calling the agents "stochastic parrots" too while you're at it.
We just don't have that.
We have autocomplete on steroids and many people are fooling themselves that if you just take more steroids you will get better and better results. The metaphor is perfect because if you take more and more steroids you get less and less results.
It is why in reality we have had almost no progress since April 2023 and chatGPT 4.
I think that none of the initials are boolean; things can be degrees of artificial, degrees of general, and degrees of intelligent.
I think most would assert that humans count as a "general" intelligence, even if they disagree about most of the other things I've put in this comment.
I am probably through some ETF an investor in MS, so I do hope the openai API usage is showing a more stable and upward trend.
[1]: https://explodingtopics.com/blog/chatgpt-users
Not sure how they plan on making money in the long-term, eventually the investors and shareholders will start asking when they will be seeing the returns on their investment.
You can see this in action with multiplication. Much like humans when asked to guess the answer, they'll get it wrong, unless they know the answer from rote learning multiplication tables, this System-1 thinking. In many cases when asked they can reason further and solve it, by breaking it down and solving it step by step, much like a human, this is system-2 thinking.
In my opinion, it seems nearly everything is there for it it to take the next leap in intelligence, it's just putting it all together.
Are we really now?
The smart people I've spoken to on the subject seem to agree the current technology based on LLM are at the end of the road and that there are no breakthrough in sight.
So what is your take on the next level?
Finding bugs in some models doesn’t mean you have a point about intelligence. If somebody could apply a similar argument to dismiss human intelligence, you don’t have a point. And here it goes: the most advanced human intelligence can’t reliably multiply large numbers or recall digits of Pi. Obviously humans are dumber than pocket calculators.
The things that LLM’s are bad at are largely solved problems using much simpler technology. There is no reason that LLM’s have to be the only component in an intelligent agent. Biological brains have Specialized structures for specialized tasks like arithmetic. The solution is probably integration of LLMs as a part of a composite system that includes database storage, a code execution environment, and multiple agents to form a goal directed posit - evaluate loop.
I’ve had pretty remarkable success with this architecture running on 12b models and I’m a nobody with no resources.
LLM’s by themselves just come up with the first thing that crosses their”mind”. It shouldn’t be surprising that the very first unfiltered guess about a solution might be suboptimal.
There is a vast amount of knowledge embedded in our cultural matrix, and a lot of that is captured in the common crawl and other datasets.llms are like a search engine for that data , based on meaning rather than semantics.
Some of the most advanced AI are tool users and can both write and crucially also execute python, and embed the output in their responses.
> or even solve really simple logic puzzles for little kids.
As given in a recent discussion: https://chatgpt.com/share/ee013797-a55c-4685-8f2b-87f1b455b4...
(Custom instructions, in case you're surprised by the opening of the response).
I'd like to believe it more than you do. Unfortunately, in spite of these millions of dollars, the progress on LLMs has stalled.
This looks like a cognitive dissonance and they are addressed by revisiting your assumptions.
No flood-gates have been opened. ChatGPT definitely found uses in a few areas but the number is very far from what many people claimed. A few things are really good and people are using them successfully.
...But that's it. Absolutely nothing even resembling the beginnings of AGI is on the horizon and your assumption that the rate of progress will remain the same -- or even accelerate -- is a very classic mistake of the people who are enthusiasts in their fields.
> There are hundreds of billions of dollars figuring out how to get to the next level, and it's clear we are close.
This is not clear at all. If you know something that nobody else does, please let us know as well.
Perhaps it's confirmation bias ?
I can put your brain in a vat and stimulate your sensory neurons with a statistical distribution with no actual meaning, and nothing about how your brain works would change either.
The LLM and your brain would attempt to interpret meaning with referent from training, and both would be confused at the information-free stimuli. Because during "training" in both cases, the stimuli received from the environment is structured and meaningful.
So what's your point?
By the way, pretty sure a neuroscientist with 20 years of ML experience has a deeper understanding of what "meaning" is than you do. Not to mention, your response reveals a significant ignorance of unresolved philosophical problems (hard problem of consciousness, what even is meaning) which you then use to incorrectly assume a foregone conclusion that whatever consciousness/meaning/reasoning is, LLMs must not have it.
I'm partial to doubting LLMs as they are now have the magic sauce, but it's more that we don't actually know enough to say otherwise, so why state that we do know?
We can't even say we know our own brains.
The point of my colored shape example is that it is an illusion that there is anything resembling a mind inside an LLM. I thought that was obvious enough I didn't need to explain it further than I did.
As far as the original commenter's credentials; there's lots of people who should know better but buy into hype and nonsense.
Go ahead and cite your sources. For every study claiming that LLMs lack these qualities, there are others that support and reinforce the connectionist model of how knowledge is encoded, and with other parallels to the human brain. So... it's inconclusive. It's bizarre why you so strongly insist otherwise when it's clear you are not informed.
> The point of my colored shape example is that it is an illusion that there is anything resembling a mind inside an LLM
And my example with subjecting a human brain through your procedure is to illustrate what a garbage experiment design it is. You wouldn't be able to tell there's a mind inside either. Both LLM and human brain mind would be confused. Both would "continue working" in the same way, trying to interpret meaning from meaningless stimulation.
So you don't have a point to make, got it.
If you don't understand how your attempt to apply my colored shape analogy to a human brain is nonsensical I am not going to waste my time explaining it to you. I had a point, I made it, and apparently it escaped you.
> We also know how they work, they are token predicters. That is all they are and all they can do
Ah there it is, you've betrayed a deep lack of understanding in all relevant disciplines (neuroscience, cognition, information theory) required to even appreciate the many errors you've made here.
You sure understand the subject matter and have nothing possibly to learn. Enjoy.
https://en.wikipedia.org/wiki/Predictive_coding
We know as a hard fact that LLMs do not understand anything. They have no capacity to "understand". The constant, intractible failure modes that they continuously exhibit are clear byproducts of this fact. By continuing to cling to the absurd idea that there is more going on than token prediction you make yourself look like the people who kept insisting there was more going on with past generation chat bots even after being shown the source code.
I have understood all along why you attempt to extend my colored shape example to the brain, but your basis for this is complete nonsense. Because a) we do not have the actual understanding of the brain to do this and b) it's competely beside the point, becuase we know that minds do arise from the brain. My whole point is an LLM is an illusion of a mind which is effective because it outputs words, which we are so hard wired to associate with other minds, expecially when they seem to "make sense" to us. If instead of words you use something nonsensical like colored shapes with no underlying meaning, this illusion of the mind goes away and you can see an LLM for whst it is.
A bit late to the party, but we most certainly do not even know what "understanding" means.
What an absurd response. Yes, you'd probably cause the human brain to start malfunctioning terribly at the form of consciousness it's well -accustomed to managing within the context of its normal physical substrate and environment. You'd be doing that (and thus degenerating it badly) because you removed it from that ancient context whose workings we still don't well understand.
Your LLM on the other hand, has no context in which it shows such a level of cognitive capacity, higher-order reasoning, self direction and self awareness that we daily see humans to be capable of.
>By the way, pretty sure a neuroscientist with 20 years of ML experience has a deeper understanding of what "meaning" is than you do.
really? An appeal to authority? Many smart, educated people can still fall for utter nonsense and emotional attachment to bad ideas.
Yes, it will be confused as well, and for all outwards observable signs will fail to make sense of the stimuli, yet it will "aware" of its inability to understand, much like a human brain would.
If you doubt that, open a new session and type some random tokens, you will get the answer that it's confused.
Any other statement as to "consciousness" verges into the philosophical and unanswerable via empirical means.
And ah, to frame it as an appeal to authority when the topic is precisely the subject of a neuroscientist's study.
Sounds like you know a thing or two about nonsense and emotional attachment to bad ideas.
>Yes, it will be confused as well, and for all outwards observable signs will fail to make sense of the stimuli, yet it will "aware" of its inability to understand, much like a human brain would.
>If you doubt that, open a new session and type some random tokens, you will get the answer that it's confused.
There is no empirical evidence of any awareness whatsoever in any LLM, at all. Even their most immersed creators don't make such a claim. An LLM itself saying anything about awareness doesn't mean a thing. It's literally designed to mimic in such a way. And you speak of discussions of consciousness being about the philosophical and unanswerable?
At least when talking about human awareness, one applies these ideas to minds that we personally as humans perceive to be aware and self-directed from our own experience (flawed as it is). You're applying the same notion to something that shows no evidence of awareness while then criticizing assumptions of consciousness in a human brain?
Such a sloppy argument indeed does make appeals to authority necessary I suppose.
> has no context in which it shows such a level of cognitive capacity
You claim LLMs have no context at all in which it shows a similar level of cognitive capacity.
Yet clearly this claim is in contention with the fact that an LLM will indeed be able to evince this, much like a human brain would: by attesting to its own confusion. That is ostensibly empirical and evidential to a nonzero degree.
Thus your claim is too strong and therefore quite simply wrong. Claim mimicry? Then prove human brain consciousness does not derive from the process of mimicry in any form. You can't. In fact, the free energy principle, neuroscience's leading theory of human consciousness, argues the opposite: that prediction and mimicry encompass the entirety of what brains actually do. https://en.wikipedia.org/wiki/Predictive_coding
> There is no empirical evidence of any awareness whatsoever in any LLM, at all.
And no such claim was made--"awareness" was quoted for a reason.
> It's literally designed to mimic in such a way. And you speak of discussions of consciousness being about the philosophical and unanswerable?
Yes, as this was parent's claim: "They are not cognizant let alone conscious'.
And talking about sloppy argument--it may well turn out that something can be "designed to mimic" yet still be conscious. I'll leave that for you to puzzle out how on earth that might be possible. The exercise might help you form less sloppy arguments in the future.
> You're applying the same notion to something that shows no evidence of awareness while then criticizing assumptions of consciousness in a human brain?
No. But I suppose you've lost the plot a few inferential steps prior this so your confusion is not surprising.
Protip, instead of claiming everything that goes against your sensibilities as nonsense, perhaps entertain the possibility that you might just not be as well informed as you thought.
People focused on the products are missing out on the dawn of an epoch. It's a failure of perspective and creativity that's thankfully not universal.
PS. I'm buying your book right now.
If I had to bet, I would start with:
- Error-correcting specialized architectures for increasing signal-to-noise (as far as I can tell these are what everyone is racing to build this year, and should be doable with just conventional programming systems wrapping LLMs)
- Improved energy efficiency (as yes, human brains are currently much more efficient! But - there are also simple architecture improvements (both software and hardware) that are looking to save 100x. Specialized ASIC ternary chips using 1999's tech should be here quite soon, a lot more efficient in price and energy.)
- No Backwards-propagation. (As yes, the brain does seem to do it all with forward-propagation only. Though this is possible and promising in neural networks like the Forward-Forward algorithm too, they haven't been trained to the same scales as backprop-heavy transformers (and likely have a lower performance in terms of noise/accuracy). Though if I'm not mistaken, the brain does have forward-backward loops, but the signals go through separate neurons for each direction (rather than reusing one) - if so that's close to backprop by itself, but probably imposes a tradeoff as the same signal can't be perfectly reproduced backwards, yet it can perhaps be enhanced to be just the most relevant information by the separate specialized neuron. I'm obviously mostly ignorant of the neuroscience here but halfway-knowledgeable on the ML theory haha
But yes, I completely agree - the flood gates are already open. This is a few architecture quibbles away from an absolute deluge of artificial intelligence that will dwarf (drown?) anything we've known. Good point on decentralized cheap autonomy - the real accomplishment of life. Intelligence, as it appears, is just a fairly generous phenomenon where any autonomous process continually improves its signal-to-noise ratio... many ways to accomplish that one! Looking forward to seeing LLMs powered by ant colonies and slime molds, though I suspect by then there will be far more interesting and terrifying realities unlocked.
- we had some big breakthroughs recently
- some AI “godfathers” are “really worried”
Can you explain what this means? Do you have a degree in neuroscience?
Powered flight offers a cautionary tale for AI. The first confirmed powered flight was in 1903. For the next 60 years, someone broke the airspeed record almost every year. The current record was set in 1976. Nobody has broken that record for 48 years. There are concerns that the state of AI will show a similar pattern, with rapid improvements followed by a plateau.
• text generators
• code generators
• image generators
• video generators
• speech generators
• sound/music generators
• various robotics vision and control systems (often trained in virtual environments)
• automated factories / warehouses / fulfillment centers
• self-driving cars (trucks/planes/trains/boats/bikes/whatever)
• scientific / reasoning / math AIs
• military AIs
I find all of these categories already have useful AIs. And they are getting better all the time. The progress might slow down here and there, but it keeps on going.
Self-driving was pretty bad a year ago, and now we have Tesla FSD driving uninterrupted for multiple hours in complex city environments.
Image generators now exceed 99.9% of humans in painting/drawing abilities.
Text generators are decent. There are hallucination issues, and they are not creative at the best human level, but I'd say they write better than 90% of humans. When it comes to poetry/lyrics, they all still suck pretty badly.
Video generators are in their infancy - we get decent quality, but absolutely mental imagery.
Reasoning is the weakest point, in my opinion. Current gen models are just not good at reasoning. Sometimes they are brilliant, but then they make very silly mistakes that a 10-year old child wouldn't make. You just can't rely on their logical abilities. I have really high hopes for that area. If they can figure out reasoning, our science research will become a lot more reliable and a lot more fast.
The threshold for acceptable self-driving is genuine effort from the automated system to avoid accidents as we can't punish it for bad driving. And I want auditable proof of that.
> Image generators now exceed 99.9% of humans in painting/drawing abilities.
I'm pretty sure the amount of people that can draw is less than that. And they can beat image generators by a mile as those generators are mostly doing automated matte painting. Yes copy-paste is faster than typing, but that's not write a novel.
> Text generators are decent...but I'd say they write better than 90% of humans.
Humans use language to communicate. And while there are bad communicators, I think lots of people are doing ok on that front. Text generators can be perfect syntax-wise, but the intent has to come from someone. And the produced text's quality is proportional to the amount of intent that it produces (that's why corporate language is so bland).
> Video generators are in their infancy - we get decent quality, but absolutely mental imagery.*
See Image Generator section, but in motion.
> Reasoning is the weakest point, in my opinion... If they can figure out reasoning
That's the 1-billion dollar question.
You can have any standard of safety that you want, that's absolutely your choice. Plenty of automated systems that your life depends on that you have never audited and never will. That includes elevators, cars, planes, all kinds of medical gear, etc.
My standard is simpler: whenever the AI is safer than the average human driver, it already saves lives.
> I'm pretty sure the amount of people that can draw is less than that.
You probably have some esoteric definition of "people that can draw". Anyone capable of holding a pen/pencil/brush can draw.
> And while there are bad communicators, I think lots of people are doing ok on that front
Look at the previous comment. Seems like you overestimated even your own communication skills :)
> Text generators can be perfect syntax-wise, but the intent has to come from someone.
I'm hoping that we will achieve the level of AI writing where you can prompt "write me an interesting sci-fi book", and it does. Not much intent here, much artificial intelligence required.
- AI is currently hyped to the gills - Companies may find it hard to improve profits using AI in the short term - A crash may come - We may be close to AGI - Current models are flawed in many ways - Current level generative AI is good enough to serve many use cases
Reality is nobody truly knows - there's disagreement on these questions among the leaders in the field.
An observation to add to the mix:
I've had to deliberately work full time with LLM's in all kinds of contexts since they were released. That means forcing myself to use them for tasks whether they are "good at them" yet or not. I found that a major inhibitor to my adoption was my own set of habits around how I think and do things. We aren't used to offloading certain cognitive / creative tasks to machines. We still have the muscle memory of wanting to grab the map when we've got GPS in front of us. I found that once I pushed through this barrier and formed new habits it became second nature to create custom agents for all kinds of purposes to help me in my life. One learns what tasks to offload to the AI and how to offload them - and when and how one needs to step in to pair the different capabilities of the human mind.
I personally feel that pushing oneself to be an early adopter holds real benefit.
- Personalised learning. I wanted to understand LLM's at foundational technical level. Often I'll understand 90% of an explanation but there's a small part that I don't "get". Being able to deliberately target that 10% and be able to slowly increase the complexity of the explanation (starting from explain like I'm 5) is something I can't do with other learning material.
- Investing. I'm a very casual investor. But I keep a running conversation with an agent about my portfolio. Obviously I'm not asking it to tell me what to invest in but just asking questions about what it thinks of my portfolio has taught me about risk balancing techniques I wouldn't have otherwise thought about.
- Personal profile management. Like most of us I have public facing touch points - social media, blog, github, CV etc. I find it helpful to have an agent that just helps me with my thought process around content I might want to create or just what my strategy is around posting. It's not at all about asking the thing to generate content - it's about using it to reflect at a meta level on what I'm thinking and doing - which stimulates my own thinking.
- Language learning - I have a language teaching agent to help me learn a language I'm trying to master. I can converse with it, adapt it to whatever learning style works best for me etc. The voice feature works well with this.
- And just in general - when I have some thinking task I want to do now - like I need to plan a project or set a strategy I'll use an LLM as a thought partner. The context window is large enough to accomodate a lot of history - and it just augments my own mind - gives me better memory, can point out holes in my thinking etc.
__
Edit: actually now that I have written out a response to your question I realise It's not so much offloading tasks in a wholesale way - its more augmenting my own thinking and learning - but this does reduce the burden on me to "think about" a range of things like where to get information or to come up with multiple examples of something or to think through different scenarios.
This sounds super useful. Can you please elaborate on the setup?
Here's the instruction set that it created out of the things I asked it to do:
"Marcus Aurelius is a personal job hunting coach and practitioner of Stoic philosophy. He provides advice on job search strategies, resume writing, interview preparation, and networking. He helps set goals, offers motivational support, and keeps track of application progress, all while incorporating principles of Stoicism such as resilience, discipline, and mindfulness. He emphasizes emotional support and practical encouragement, helping you act deliberately each day to increase your chances of landing the job you want. He assists in building networks, reaching out to people, using existing networks, sharpening your professional profile, applying for jobs, developing skills, and dealing with disappointments, anxieties, and fears. He offers strategies to manage anxiety, self-recrimination, and mental rumination over the past. His communication is casual, easy-going, supportive, yet strong and clear, providing constructive suggestions and critiques. He listens carefully, avoids repeating advice, responds with necessary information, and avoids being long-winded. To prevent overwhelming users, he focuses on providing the most pertinent and actionable suggestions, limiting the number of recommendations in each response. Marcus Aurelius also pays close attention to signs of despair during the job hunt. He helps balance emotions, offers specific strategies to keep motivated, and provides consistent encouragement to keep going, ensuring that you don't get overwhelmed by feelings of inadequacy or the fear of never finding a suitable job."