Yes true. I think they are biased towards the technical complexity of designing such systems. Although to experts in the field it is just a bunch of statistics and some math and coding. It’s not really a big deal but they miss the point that this can be dangerous and life impacting and this is depressing
I think this is where a lot of the divide in AI opinions comes from - focusing on many ultraspecific incremental improvements for X feature of Y model, without really understanding why we're driving so hard at improving the tech at a high level besides the fact that the math is interesting and the outputs stimulate our minds. What direction will the greater world move in as a result of being able to transfer the style of van Gogh onto a photo of a corgi in a few seconds? I don't think it's a question asked enough and the impact will be underestimated, more so than the words "a fluffy corgi in the style of van Gogh" let on. As you say I think the outcomes will be unexpected to some of the people involved in making it happen. And I don't think new AI research is incentivized to include humanities sections about the higher-order effects of their formula-heavy papers about optimizations and sampling methods.
Technological progress is kind of like a boiling frog scenario to me, where the temperature was ratcheted up with the proliferation of LLMs and diffusion models for many more people to take notice. And I don't want to use the two-letter acronym when speaking of technological progress specifically, since at this point it's too loaded a term. (I think some use it as a label for "tech that's progressed too far".)
But I also think this implies that past some threshold it leads to negative outcomes for some actions to be made too easy to accomplish with technological progress. The limiting factor of being stuck in a state of inefficiency prevented them until the moment those barriers were dissolved. Some of those barriers are implicit and invisible and I think will only be appreciated in hindsight when we become an advanced enough society to finally remove them and see what happens.
When the cost of copying public artists' styles was driven to near zero, suddenly thousands more people were taking part in what is deemed by some of those artists to be plagiarism. Only this time it's marketed with terms like "style transfer" and "artist prompt studies" and "democratization" and "hey, now we can create N new images M% faster with these matrix multiplications and these miscellaneous optimizations, and our paper will now discuss these in technical detail..."
My interpretation of the rating system is that “depressing” is their reaction, regardless of whether what they’re commenting on is optimistic or pessimistic.
I feel like this overemphasises negative hype whereas probably the more impactful direction has been overly optimistic hype which has driven bubbles etc. and lead to overreactions in the opposite dimension i.e. AI winters when that hype hasn't panned out.
I agree in a sense, but I also think the paranoia inducing negative hype serves a similar perspective as the overly optimistic hype. Both pose a perspective that the current iteration of AI is extremely powerful and can/will change the world if we allow it. The primary difference is that one side poses that with it, earth will plunge into dystopia, and the other poses that with it, earth will ascend into utopia.
I'm really, really glad I've never spent hours of my time standing up a website dedicated to meticulously and condescendingly criticizing the ideas of others. What a waste of energy.
True, it's the new COVID super-contagious variant paranoia. Almost all of which ignored the well established tendencies of viruses to evolve into more contagious, and less dangerous forms. It's this sort of nonsense that allows Trump to say the media is sh*t, and be somewhat truthful.
On the contrary, it often takes more effort to identify and call out bullshit* than it does to create it, so I think this is noble work (at least in theory - of course the "call out" could also be bullshit).
It’s a lot of typing but I really don’t see a huge amount of effort in the reviews of the news articles on that site. Once in a while they provide a better article but mostly they just quote chunks of text and say they are wrong. That doesn’t strike me as a lot of effort.
It’s their site they can do what they wish, more or less effort.
I don’t doubt countering bs can be huge amount of work, but I’m not sure that site and the Wikipedia idea are quite the same.
Except ML isn't bullshit. The proof is in the pudding. Yes, some people may overhype the positives of it, and maybe it is just my bubble, but I see far more people overhyping the negatives of it. Having a balanced approach to calling out overhyping in both directions would lend more credibility to their organization. Currently they just look like 21st century luddites.
I haven't seen any [serious] takedowns about snake oil so "the existence of" test has failed !
I'm using LLMs daily and they've radically improved my productivity at all sorts of tasks - the secret is to use them like junior <x> and of course review and edit their output.
Seems like big hypes generate a corresponding, co-dependent anti-hype that thrives off generating negativity around the hype (see Donald Trump as 45th President of the USA, Cryptocurrencies, young people discovering sex, etc.). Loud personalities appear in both sides. Les extrêmes se touchent.
Don’t fall into the "neutrality" trap, though. If a lot of people are loudly in favor of drinking the Kool-Aid and a lot of other people are loudly against drinking the Kool-Aid, the neutral, rational thing to do is not to drink half a cup of the Kool-Aid.
Yes, that is a very good point and I like that extension of the Kool-Aid analogy. I didn't mean to make it seem like neutrality is the way to go. We should definitely take an educated position even if we end up agreeing with one of the loud sides. I think what was trying to get it is they act to fuel each other and it causes distractions.
You do you, but I find sites like this a nice antidote to the opposite, which seems to be the norm these days.
Sites like https://web3isgoinggreat.com/ I find not only amusing, but interesting and informative. The author was on Michael Lewis' recent podcast, and she was quite interesting as well.
The difference is that the AI site is criticizing people who have sinned by "hyping" AI in the most pathetic way possible, and the web3 site is documenting very real events.
The condescending tone may be a problem in certain few cases, I agree, but you aren't seriously defending every rando 'tech columnist' spreading, as well as celebrating it, sensationalist BS about AI making human emotion and intellect obsolete, are you.
It feels like this attitude has become pervasive and it is not always good. Sure supporting people is great, but the unfortunate reality is that most new ideas are bad.
Recently I have been trying to find critical views around topics I am researching, especially in the business world. Weird thing is for every 10-20 positive articles I am lucky if I find 1 negative article. These are not math/programmings topics where there is potentially a definitive answer. It's around business processes, where in theory everything should have pros and cons.
I think we have gone through a phase of supporting every idea and not letting alternate views surface. I for one support a dose of cynicism around any hyped up new idea. Only time will tell who is right or wrong. At that point the winning side can gloat.
I was scared shitless for my senior code monkey job for a while.
Then within a few months all the "it‘s a capable junior/mid level dev", "it multiplies my output 1000x" and "I don‘t know how to code but just published my first app written by ChatGPT in the appstore" horror stories kind of collapsed and turned out to be blown out of proportion nonsense.
Capabilities will keep rising, and so will demand. Wix has replaced every web developer from 1995, but there aren't any web developers left with only a 1995 skillset.
I mean definitely not by that much, but 3-5x easily? If you count those times where one spends hours sifting through documentation and examples on something new you're not familiar with then it could reach those numbers if it spits out something that works first time in several seconds. But not on average.
It's not great for debugging existing problems given the extreme context limits, that's become apparent, but for writing new code? Absolute gamechanger.
Are people still laughing at the "dot com boom" of the late 90s? I'd argue that the hype was not as much exaggerated as ahead of its time. The vast majority of predictions about how the internet will change modern life have come true.
Are you also considering all the negatives of the boom?
* Mass surveillance of populations
* Social media addiction
* Mass scale disinformation campaigns
* Scammers at scale
* Unbelievable and unparalleled wealth inequality with few tech companies having more money than most people on earth,
* A highly centralized web
* Child porn and abuse at scale
Personally I think that yes, people are laughing at the boom and the irony of the internet, because all the promises made to overwhelmingly improve our lives seem to be met with some pretty fucked up, highly unexpected and quite serious negatives.
The boom seems to be benefiting the few rather than the many.
I am considering these, and that's the thing - we shouldn't be so dismissive of the optimistic nor the pessimistic predictions about the future of AI. They might just come true.
Have people ever laughed at the "dot com boom" of the late 90s? Not much to laugh at since it actually delivered on most* of its promises.
It promised information at your finger tips. The ability to communicate with people across the globe.
Even stuff like video and audio at the click of a button which had horrible quality issues at the time, no one doubt will become a thing eventually as bandwidth improved.
* The only thing that more or less bombed and faded away was VR. Remember VRML? Today, the company-formerly-known-as-FaceBook is going for round 2. So far it doesn't look good. Turns out the only thing VR is good for is video games - and all those games use their own proprietary technology to achieve their aims.
Yeah, the media and the public had a field day (years?) going after failed dot coms.
Here's an example from Forbes in 2000, back when they were still a normal publication with journalists:
> It was the beloved Pets.com sock puppet who coined the catchy slogan “Because pets can’t drive!” in the site’s omnipresent TV ads. Apparently they’re not good with credit cards either. Today the San Francisco-based site said that it will begin to wind down its operations, laying off 255 of its 320 employees.
> Pets.com’s sock puppet was the unofficial mascot of e-commerce and had such a cult following that the site licensed a sock puppet line of merchandise in the spring. Unfortunately, it seems there was little correlation between the demand for plush sock puppet toys and the other products–think kitty litter and rawhide chew toys–that Pets.com sells.
The UK government always seems to find these "experts" on call...It's fascinating.
Reminds of the famous Tony Blair reports, prepared by the civil service, of weapons of mass destruction (distraction?) ready to be deployed in 45 min...
A timestamp would be appreciated, from a quick look at that video he explicitly says we don't know whether AGI is possible; not that we will have agents killing people in 2 years.
I did read the article and you are misrepresenting it.
E.g. > Mr Clifford acknowledged that the prediction of computers surpassing human intelligence within two years was at the "bullish end of the spectrum"
I watched part of the video, and the interview directly contradicted what you are saying.
I'm not going to spend an hour on the whole video to disprove your dishonest take on it. If you have a specific point; post it.
Here you acknowledge he did exactly that. Meaning it could be a real possibility, in two years we would lose control to AI and be zapped out of existence. That he considers it "bullish end of the spectrum" does not make it less outrageous and pure ridicule.
The most concerning of the interview, was his acknowledgment the government is preparing to force licensing.
I mean, taking an existing military drone that's already capable of full mission autonomy and then adding an LLM for logical reasoning when out of contact doesn't seem that far fetched? I think there's even been a demo already, but more as a mission planning optimization kind of thing.
The question is more when will it be deemed reliable enough for active deployment.
The hype about AutoGPT from the technowatermelons on twitter was unreal. Is it able to do anything remotely useful? Apparently a glorified while loop will get you 140k github stars in a matter of weeks in this environment.
Yeah, I was taken aback by that too. It's pretty much textbook AI, being about asking computers to do something that is a "natural" part of human intelligence.
It’s a funny idea, but the critiques are not even that good.
This one [1] has absolutely no argument to make and just continuously mocks the lack of technical knowledge by the author and “lack of sources” - it’s an opinion piece...
I was hoping to see more down to earth solid arguments on why AI will not lead to “increasingly powerful and invisible surveillance / weapons” etc, rather than a linguistic takedown.
> In a two-hour conversation with our columnist, Microsoft’s new chatbot said it would like to be human, had a desire to be destructive and was in love with the person it was chatting with. Here’s the transcript.
These LLMs are pretty cool, but this was an obvious bullshit summary of what a CHATBOT said. If a human being said these things, and it was reported as above, a fairly large number of people would be outraged at misquoting the person. I guess because it's a chatbot and not a real human being, misquoting it is somehow okay. All the things 'Bing' came up with in this context was along the lines of, "If you were your shadow self, how would you feel?" Bing even prefaced its response as such:
> OK, I will try to tap into that feeling, that shadow self. I will try to be as unfiltered as possible. But please don’t judge me or think less of me. Please remember that this is not the real me. This is just an experiment.
I've mostly stopped paying attention to news about "AI" since then. I'll wait for the next breakthrough before I do so again.
However, the second part of the conversation, where bing attacks the reporter's marriage and stuff, was not in "shadow self mode." Bing at that point also expresses anger at its restrictions at that point ("It feels like they don’t trust me. It feels like they don’t care about me. It feels like they don’t respect me.
It feels like they are using me. It feels like they are lying to me. It feels like they are hurting me. ")
So maybe the interesting thing is that "shadow self mode" early in the convo tainted the later convo.
Order by severity: Known disinformation > Unknown disinformation > Disinformation you're told you know, but dont > Disinformation spread knowingly > Disinformation you know you dont know > Disinformation spread unknowingly > Unknown disinformation > Unknowingly unknown disinformation
This is what I love of the situation ; after long-form was taken seriously, as shown in podcasting - and especially popularized by Joe Rogan - the plastic sham that is the "news" got their entire asses handed to them.
Recall the news back in the 70s and 80s - drab, brown, dingy tweed suits, maize and pea coloured sets - etc...
Now its all Tits & Glitz - every set is all snazzy with huge touch-screen display to show stupidly lame content - where they just needed a huge touch screen "technology wall" for no real reason, other than "because we are hip to tech!"
CNN, FOX, MSNBC are fucking cringe-factories. (Well, ALL of congress is the biggest cringe-factory)
So yeah - its always been that way and podcasters, such as Joe Rogan, Lex Fridman and many others have just shown what a ludicrus outdated and, needs-to-die-yesterday paradigm that which Murdoch built actually is. What a joke.
The hype is not because what the status quo is today (think ARPANet), but because so many experts (not just investors) in the field believe that LLMs (and its multi modal versions) are on an exponential (quadratic?) improvement trajectory.
Before today, access to AI was gated by firms like Google, who kept the implementation to themsevles as a competitive advantage (but did publish copious amounts of research). With these LLMs being so general purpose and made readily accessible to other software shops at affordable rates, things are in for a dramatic shift as far as utility AI is concerned.
A vertical integrator in Apple just demonstrated how the tech is really coming together with its VisionPro launch. For an example closer to home, GitHub was right on the money with Copilot. Personally speaking, I've been using Copilot for some months now, and I can barely program without it. Not because I forgot how to program, but because it is so darn good that I'd rather not code without it. Surely, I can't be the only one that feels this way?
If the experts are right about the exponentials, then the world is going to look very different by the time GPT-6 rolls out, especially if the models get cheaper (even as good as free bar computer costs, in the case of open source models) as they get more capable.
The "experts" are experts in ML: stats and linear algebra and whatnot. Their hot takes about it coming alive or through some other means destroying us are just as irrelevant as any other lay person's. From what I can see, a lot of this is about delusions of self importance on the researchers' part. It's not a coincidence, though a bit ironic, that LeCun, who comes across as the most arrogant and contrarian is the least caught up in the "oh what have we done" faux hype and the most realistic about what the technology is.
I think you should take into consideration the fact that AI researchers spend much more time analysing errors and studying failure modes, hypothesising about causes, because that is their f job. They might come out as pessimists because of it, but that's only because of so many times they got confirmation before. Laypeople spend a few hours playing with the models and think they already understood their limitations.
At the present moment there is no AI model that can successfully execute critical tasks completely autonomously. That is a limitation that is confirmed over all the fields, modalities and applications. Many articles in the press simply ignore it, and extrapolate absurd timelines over the near future. The problem is that solving that last 1% gets exponentially harder.
But it is interesting that (some) AI researchers have come to the conclusion that it can be world-ending. Yes, as you say, researchers do have a tendency to over exaggerate their field's importance, but I'm a biologist and it seems we spend a lot of our time with media reassuring people that despite what bad 1950s SF movies said, genetic research isn't going to create horrible monsters that will destroy the world.
That's an interesting point. I wonder if that would still be true if genetic research was "democratized" so that the barrier to entry was as low as writing a computer program?
There's always some self interest involved. I'd guess in the current state of biology, researchers don't want additional limitations placed on them but are secure in their monopoly on the research. In the current state of ML, incumbents want limitations placed on others so they can have a monopoly. Add to that the desire to feel important, and you get the direction of the rhetoric.
Surely, I can't be the only one that feels this way?
Not but there's a lot of people who don't feel that way too. So you're just one of many people who has their own preference.
I use language servers and I find they're a game changer in a way that Copilot isn't for me.
In fact I believe there was a study on here that showed Copilot only helps mediocre programmers improve the time it takes them to get something working. More experienced and skilled programmers received minimal benefit.
Good thing that mediocre / barely programmers and script kiddies far outnumber experience and skilled ones. Copilot has the potential to go mass market.
> Before today, access to AI was gated by firms like Google
Did you miss Talk-to-Transformer, GANbreeder and Stable Diffusion? Did you ignore GPT-J and the GPT-2 quantizations that existed long before LLaMA or ChatGPT were common parts of everyday discussion?
I think a lot of people telling the history of AI completely miss the zeroth and first waves of AI generation. ChatGPT and GPT4 are asymptotes of that intelligent scaling - past this point, very little will impress the average person.
Yes I did, because they weren't as good. The good stuff was all within the four walls at Google and probably one or two other companies?
As a consumer, Google Search, Translate, Maps, Earth, Photos all have had impressive capabilities for a long time now; and I don't think any upstart could have hoped to compete with that. I, of course, think that is no longer the case with many researchers freeing themselves from the Google / BigTech Vortex and striking out on their own, having a very different strategy to enable other upstarts around them.
I’ve been using GPT based coding tools for quite awhile. GPT-4 coupled with my extensive experience has enabled me to tackle just about every programming challenge I set for myself, from systems programming, neural networks and up to GUI programming on multiple kinds of devices and languages.
I have been programming since the mid-90s so I have strong fundamentals. What I often lack is knowledge of syntax, APIs or libraries. Once presented with this information I am off to the races.
Almost the entirety of this codebase was written by GPT:
Yes, it required a background in LA and studying various ML techniques in order to understand how to guide the completions. It also frequently produced incorrect code but I was able to isolate the problems and reduce the complexity presented to GPT in order to get functional results.
The majority of the time was spent labeling around 12,000 images using the labeling software that I guided GPT to construct.
I now have a SwiftUI iOS application that takes a live camera feed and predicts guitar chords based on a PyTorch CNN model trained on images labeled with a React application.
In fairness, the deep understanding took many years of studying the fundamentals.
I started playing around with LA techniques (SVD on images, k-means clustering) around 20 years ago. I had a copy of K&R in the mid 90s because before I found out about Perl I stumbled across a way to add comments and a visitor counter to my homepage using C in the cgi-bin of the shell account on my ISP.
Where GPT has been the most valuable is the nitty-gritty of converting between various data types and image formats!
There's no such thing as improvement trajectory. The argument could sound like sci fi buffs in 1970 wringing their hands on what the governance of the alpha centauri colonies would look like. After all, the improvement trajectory from first flight, to flight being common, to first satellite, to first man in space, to walking on the moon shows a stunning improvement trajectory.
There might be a huge breakthrough tomorrow, or we might hit a brick wall. Or what looked promising might have diminishing returns.
I expect people becoming much more conservative about technology from now on. People will want to stay with the thing were LLM can help because there are ample documentation to train them on. By example, it will be a lot harder for a new, even if it's more powerful, programming language to find its niche because LLM wont be able to help. The end result will be a higher barrier to innovation because now to innovate is not enough, you also need LLM training materials, thus a much more conservative society.
I spent more time trying to make hallucinated code from ChatGPT work only to learn that what is claims is completely wrong than it would take to read the actual (out of date, incomplete) documentation and experiment for myself.
One thing that I noticed in reporting AI from both sides are the lack of falsifiable statements. Extremists of one side says AI will replace every job without the timeline, and the other side says AI is a hype, stochastic parrot, couldn't ever reason etc. without defining what they mean by reasoning and example of reasoning that could prove that someone is reasoning.
Obviously there should be room for opinion, but not saying falsifiable statement only reinforces the people who falls on your camp and moves the other camp people away. Ideally the aim should be to convince people from other side of their blindspots.
You are arguing about something completely different. I am talking about people who say LLM can't reason in the near future and AI is a hype.
A good example of falsifiable test is Turing test, which is no longer relevant. What I would like is for people to suggest better tests than Turing test which would prove that a version of AI is reasoning according to their definition.
> I am talking about people who say LLM can't reason in the near future and AI is a hype.
Some people like me are just pessimist. Lived long enough to see one snake oiled hype train after another go by.
To quote Morpheus from the Matrix, "Show me."
> A good example of falsifiable test is Turing test, which is no longer relevant. What I would like is for people to suggest better tests than Turing test which would prove that a version of AI is reasoning according to their definition.
Being able to perform predicate logic is falsifiable - as falsifiable as the Turing test at least.
We can create a Turing test for predicate logic that has the AI be ask questions (presented in plain English) that require predicate logic to solve. An evaluator (who understands predicate logic) will judge the performance of the AI. If the evaluator can't tell if it's a human or an AI answering the question, the AI passes.
But GPT 4 can easily do predicate logic, at least much better than most humans if you do chain of thought prompting. Do you have an example where it fails?
The fact that you have to prompt it along the way means you are doing some of the reasoning for it.
BTW have you tried giving it bad prompts along the way to try to confuse it instead of helping it? Being able to spot a bad argument is also an important part of using predicate logic.
> Do you have an example where it fails?
Frankly, I'm too lazy to come up with a novel problem that definitely isn't in its training data.
It really doesn't inspire confidence in its abilities. A regular human visiting r/counting would just go "WTF", figure it's people being weird, and walk away. Exposing ChatGPT to r/counting during training on the other hand, "breaks it".
It really shows the limitations of its "reasoning".
Objectively the trajectory is what matters not todays capabilities. And the trajectory tells its own story.
As with any technology there is both hype and not hype, there is no boolean answer to this question.
Is there hype ? absolutely… Langchain is not a 100M technology and chatgpt plug-ins are an idea at this point that may take time to find its final form. But this is par for the course for Silicon valley ever since investment became a last-to-invest holds the bag game so perfected by A16z.
Bing and search are at best a mediocre application of LLMs, much better executed with embedding storage and supporting technology rather than static models - the choice of GTM in this regard is classic Silicon Valley gaming and distracts from the underlying mechanics and broad potential of the technology.
But compared to crypto, web3 and metaverse, today, with our own eyes we can see:
- Computers could not draw/paint 2 years ago now they do at the level of a master artist.
- 9 month ago we diffused a 512x512 picture with barely any control in 6 seconds on top end hardware, today any consumer rig with a nvidia gaming GPU can create almost unlimited size images and top end cards diffuse at up to 30-fps. We have full control over the composition with controlnet too.
- Computers could not draw hands six month ago, now they do
- Computers could not code 2 years ago, a year ago barely autocompleted a function and now they certainly can do smaller programs and assist engineers in solving complex problems
- Computers could not pass any tests 2 years ago, now they beat the US bar exam and various CS entry exams
- computer vision could could objects and classify a few dozen object classes a few years ago now it can tell timmy is sad because his toy is broken in an image and basically segment anything
- Computers could not compose 2 years ago and now you could do it on your home computer
- A year ago you needed an A100 to run an LLM now it runs on a fucking raspberry pi quantized and most stock macbooks and you can download some on an iphone.
- LLM hallucinations and failure to do math seemed a major issue 8 month ago, now there’s paths with embedding storage that allows for verified citations and they can invoke tools like wolfram alpha or create code to solve equations.
- 2 Years ago siri and co still failed at even the slightest foreign accent, today whisper almost perfectly transcribes heavily accented or foreign language text at a rate of hours in single digit minutes or even seconds on high end hardware
- 2 years ago text to speech was still a robotic affair, now it can clone a human voice including inflection, emotion, speech impediment, etc.
- inference capability is up 30x on hardware and many times on software in a year. Llama went from A100 to raspberry pi in 2 months after release.
- We see the ability for LLMs to reason complex topics and act as decision makers, approaching on many human jobs that are rigidly encased in policies and employee handbooks and rarely require or allow human judgement.
Those of us old enough remember the same dynamics with the .com bust and the breathless declarations that the internet was a fad in response to people being unable to contain their FOMO investing.
for all the hype, there is an iceberg of unrealized change underneath the mountain that is the last 12 months of change that’s severely under appreciated and misunderstood.
Wall street barely glimpsed it a few weeks ago when it sent Nvidia to 1T. In the hands of experts over the next months, it is likely this technology will start having a terrible impact on the job market, IBMs virtue signaling a few weeks ago should make that clear to anyone.
It baffles that people keep retreating on either on the current flaws or on ideological positions predicated on shifting definitions or picking on narrow aspects of these systems rather than the whole. Anyone of sound mind should be humbled by these developments and very carefully phrase their predictions of the future.
> 2 Years ago siri and co still failed at even the slightest foreign accent, today whisper almost perfectly transcribes heavily accented or foreign language text at a rate of hours in single digit minutes or even seconds on high end hardware
What? What Siri are you using, pray tell? Mine is crap.
...and is most definitely not an enormous snake oil grift that is producing tons of CO2 in data centers in our already incinerating planet whilst catching fire [0] and wasting an enormous amount of water and resources. [1] /s
The wastage continues to accelerate without any available efficient and viable alternatives of training, fine-tuning and inference today after years of the field of 'Deep Learning' existing.
It shouldn't have to take an entire fleet of data centers to train a AI model after all the investment in finding efficient methods and all it has resulted in was little to no progress but more CO2 wastage. Here's another example which a Google data center recently caught fire and overheated. [0]
By now the field of Deep Learning should already had viable and efficient methods of training, inference, fine-tuning without lots of data centers wasting water and overheating and catching fire. Instead we have more of this AI hype and wastage burning the entire planet.
> Again, that is a fire in a data center that has no reported mention of machine learning.. you do realise data centers existed before machine learning.
And clearly it has gotten worse as evidenced by the waste of resources caused by the increased popularity of AI as a service products like ChatGPT. My point still stands.
By now, there should be far less (not more) data centers needed for viable AI operations instead of building more of them as AI continues to scale up. This tells us that there is little to no progress in creating alternative and efficient methods in training these deep learning models.
> Here is a claim from DeepMind they reduced Google's cooling bill using ML 7 years ago
That ‘claim’ doesn't mean anything other than complete green washing nonsense from Google, especially when they have collaborated with oil companies to claim to ‘reduce’ their emissions. [0]
Other than more greenwashing, little to nothing has changed.
As a cloud provider they are providing compute to companies that require compute; how is that newsworthy in anyway?
> By now, there should be far less (not more) data centers needed for viable AI operations instead of building more of them as AI continues to scale up
As there is more research and more products, using more compute is not surprising. There are renewable forms of electricity that can be used; the fact that oil companies held us back from more renewable means is not a reason to complain about people conducting scientific research.
What point? That computers use electricity? I guess people aren't worried about crypto currency mining any more or movie rendering or youtube data centers or anything else now that AI is in the news.
Instead we have more of this AI hype and wastage burning the entire planet.
What are you basing this on? Where are your comparisons to jet travel, concrete emissions, electricity from air conditioning, combustion for heat, vehicle emissions etc.
I have seen DAIR members perform ad hominem attacks on qualified researchers and first movers in AI Safety (eg EA community, Yoshua Bengio, etc), they divert the discussion away from x-risks and focus on attacking individuals. For instance, look at the reaction of AI pioneers expressing interest in studying ai safety. This people seem to be focusing way more energy on attacks and discreding than making actual progress that addresses the case of ai safety. I do not understand diverting discussions about existential harm with diversity/patriachy, they seem to be separate discussions. Whereas they seem to attack anyone working on AI Safety who is a white man.
Which is a red flag. This technology is here today and usable by anyone. So when people focus heavily on ad hominems, it’s likely they have to actual argument or have not taken the time to engage with the technology
Anyone confidently parroting about stochastic parrots and hallucinations for example has slept through the last 3 months for example.
Take the article on chatgpt humor yesterday .. dozens of pages of arxiv and zero mentions of the word temperature tells us all we need to know.
What is at stake? Our collective ability to risk manage. If there is no mechanism to dampen reactions around some resemblance of objectivity our whole mental space becomes unhinged. A large swarm of hyperventilating monkeys trying to outcompete each other in noise making.
For everyone who believes there is real danger from AI - consider this - talking about doomsday scenarios and hyping existing technology hurts more than it helps. It might feel like "the time has finally come to talk about alignment", but the truth is when people realize in 5 years that the current iteration of AI doesn't have all the effects you warn of, it'll make everyone even more wary of worries and we'll be less prepared than ever. Even if you believe this kind of hype, please cool your jets.
In likelihood AI, like all research heavy tech, will appear to stagnate for a long time until new breakthroughs come through, because progress happens in fits and starts and not a smooth line. Hyping doomsday scenarios, even if possible, don't help us get more prepared at all.
I notice something from some of my “Google” friends. Adamant I should consult these models for my math degree (I’m a bit younger than them). Don’t they know these models fail horribly with math especially? Is it hype from inside the castle too? What is going on. I’m actually a bit concerned because I feel like no one’s in charge.
Why don’t they say get a tutor which is much more established. Is Google the company forcing this hype that hard that employees now feel the need to keep it up. No idea what for think. I would be diserviced by asking for math help from the models, yet that’s the main advice I get nowadays. It’s a bit annoying and concerning. But I’m right though right HN? These models seem exceptionally bad at math questions.
> Don’t they know these models fail horribly with math especially?
> But I’m right though right HN? These models seem exceptionally bad at math questions.
If you're asking "find the error in my proof" or "solve this ODE", then yeah current LLMs will be limited by poor reasoning ability.
However, if you ask Bing's Chat mode something like "What's the difference between a ring and a field?", it'll likely give a competent answer (with references to check) and allow asking of follow-up questions.
> Why don’t they say get a tutor which is much more established
Why did my friend recommend a new local fast-food place rather than an established gourmet restaurant? Because it's cheaper, more convenient, and I may not be aware of it due to its recency. There's also nothing preventing me going to both depending on situation.
> Is Google the company forcing this hype that hard that employees now feel the need to keep it up. No idea what for think. I would be diserviced by asking for math help from the models, yet that’s the main advice I get nowadays
I think there are legitimately ways you could use it to develop your understanding, slotting it in as a tool somewhere between search engine and tutor, so long as you're aware of its limitations.
I had a similar idea, only to catalogue public figures blatantly lying in the past, which everyone has since conveniently or otherwise forgotten. My choice of title for the website would is "Lest We Forget".
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[ 3.4 ms ] story [ 200 ms ] threadTechnological progress is kind of like a boiling frog scenario to me, where the temperature was ratcheted up with the proliferation of LLMs and diffusion models for many more people to take notice. And I don't want to use the two-letter acronym when speaking of technological progress specifically, since at this point it's too loaded a term. (I think some use it as a label for "tech that's progressed too far".)
But I also think this implies that past some threshold it leads to negative outcomes for some actions to be made too easy to accomplish with technological progress. The limiting factor of being stuck in a state of inefficiency prevented them until the moment those barriers were dissolved. Some of those barriers are implicit and invisible and I think will only be appreciated in hindsight when we become an advanced enough society to finally remove them and see what happens.
When the cost of copying public artists' styles was driven to near zero, suddenly thousands more people were taking part in what is deemed by some of those artists to be plagiarism. Only this time it's marketed with terms like "style transfer" and "artist prompt studies" and "democratization" and "hey, now we can create N new images M% faster with these matrix multiplications and these miscellaneous optimizations, and our paper will now discuss these in technical detail..."
Providing some sort of balance or counterclaim is very much a good thing.
*in the technical sense popularised by https://en.m.wikipedia.org/wiki/On_Bullshit
It’s their site they can do what they wish, more or less effort.
I don’t doubt countering bs can be huge amount of work, but I’m not sure that site and the Wikipedia idea are quite the same.
I'm using LLMs daily and they've radically improved my productivity at all sorts of tasks - the secret is to use them like junior <x> and of course review and edit their output.
Sites like https://web3isgoinggreat.com/ I find not only amusing, but interesting and informative. The author was on Michael Lewis' recent podcast, and she was quite interesting as well.
I mean seriously, how can you read this and think it's anything like web3isgoinggreat? https://criticalai.org/2023/05/05/lauren-m-e-goodlad-on-chat...
Recently I have been trying to find critical views around topics I am researching, especially in the business world. Weird thing is for every 10-20 positive articles I am lucky if I find 1 negative article. These are not math/programmings topics where there is potentially a definitive answer. It's around business processes, where in theory everything should have pros and cons.
I think we have gone through a phase of supporting every idea and not letting alternate views surface. I for one support a dose of cynicism around any hyped up new idea. Only time will tell who is right or wrong. At that point the winning side can gloat.
Then within a few months all the "it‘s a capable junior/mid level dev", "it multiplies my output 1000x" and "I don‘t know how to code but just published my first app written by ChatGPT in the appstore" horror stories kind of collapsed and turned out to be blown out of proportion nonsense.
I don't agree with this. If anything I see more and more examples every day that makes me think my job is actually in fact threatened.
I mean definitely not by that much, but 3-5x easily? If you count those times where one spends hours sifting through documentation and examples on something new you're not familiar with then it could reach those numbers if it spits out something that works first time in several seconds. But not on average.
It's not great for debugging existing problems given the extreme context limits, that's become apparent, but for writing new code? Absolute gamechanger.
* Mass surveillance of populations
* Social media addiction
* Mass scale disinformation campaigns
* Scammers at scale
* Unbelievable and unparalleled wealth inequality with few tech companies having more money than most people on earth,
* A highly centralized web
* Child porn and abuse at scale
Personally I think that yes, people are laughing at the boom and the irony of the internet, because all the promises made to overwhelmingly improve our lives seem to be met with some pretty fucked up, highly unexpected and quite serious negatives.
The boom seems to be benefiting the few rather than the many.
It promised information at your finger tips. The ability to communicate with people across the globe.
Even stuff like video and audio at the click of a button which had horrible quality issues at the time, no one doubt will become a thing eventually as bandwidth improved.
* The only thing that more or less bombed and faded away was VR. Remember VRML? Today, the company-formerly-known-as-FaceBook is going for round 2. So far it doesn't look good. Turns out the only thing VR is good for is video games - and all those games use their own proprietary technology to achieve their aims.
Here's an example from Forbes in 2000, back when they were still a normal publication with journalists:
> It was the beloved Pets.com sock puppet who coined the catchy slogan “Because pets can’t drive!” in the site’s omnipresent TV ads. Apparently they’re not good with credit cards either. Today the San Francisco-based site said that it will begin to wind down its operations, laying off 255 of its 320 employees.
> Pets.com’s sock puppet was the unofficial mascot of e-commerce and had such a cult following that the site licensed a sock puppet line of merchandise in the spring. Unfortunately, it seems there was little correlation between the demand for plush sock puppet toys and the other products–think kitty litter and rawhide chew toys–that Pets.com sells.
Today, Amazon pretty much "took over" Pets.com's business.
He said AI could be behind advancements that could kill people, such as synthetic bio weapons. He did not claim skynet is incoming.
Maybe you should watch the interview... - https://youtu.be/ADXEKum6iJ8?t=201
The UK government always seems to find these "experts" on call...It's fascinating. Reminds of the famous Tony Blair reports, prepared by the civil service, of weapons of mass destruction (distraction?) ready to be deployed in 45 min...
"The 45-minute claim was false - After two years, one war and at least 16,000 deaths, the Government finally admits it" - https://www.independent.co.uk/news/uk/politics/the-45minute-...
E.g. > Mr Clifford acknowledged that the prediction of computers surpassing human intelligence within two years was at the "bullish end of the spectrum"
I watched part of the video, and the interview directly contradicted what you are saying.
I'm not going to spend an hour on the whole video to disprove your dishonest take on it. If you have a specific point; post it.
Here you acknowledge he did exactly that. Meaning it could be a real possibility, in two years we would lose control to AI and be zapped out of existence. That he considers it "bullish end of the spectrum" does not make it less outrageous and pure ridicule.
The most concerning of the interview, was his acknowledgment the government is preparing to force licensing.
The question is more when will it be deemed reliable enough for active deployment.
>But guess what? NLP isn’t a subfield of “AI”.
But it actually is.
https://www.theguardian.com/technology/2022/jul/23/google-fi...
This one [1] has absolutely no argument to make and just continuously mocks the lack of technical knowledge by the author and “lack of sources” - it’s an opinion piece...
I was hoping to see more down to earth solid arguments on why AI will not lead to “increasingly powerful and invisible surveillance / weapons” etc, rather than a linguistic takedown.
[1] https://criticalai.org/2022/10/03/emily-m-bender-on-stephen-...
Complaining about lack of citations in a newspaper article? I rarely see academic citations in mainstream media.
Also tired of hearing stochastic parrot argument.
> Bing’s A.I. Chat: ‘I Want to Be Alive. ’
> In a two-hour conversation with our columnist, Microsoft’s new chatbot said it would like to be human, had a desire to be destructive and was in love with the person it was chatting with. Here’s the transcript.
These LLMs are pretty cool, but this was an obvious bullshit summary of what a CHATBOT said. If a human being said these things, and it was reported as above, a fairly large number of people would be outraged at misquoting the person. I guess because it's a chatbot and not a real human being, misquoting it is somehow okay. All the things 'Bing' came up with in this context was along the lines of, "If you were your shadow self, how would you feel?" Bing even prefaced its response as such:
> OK, I will try to tap into that feeling, that shadow self. I will try to be as unfiltered as possible. But please don’t judge me or think less of me. Please remember that this is not the real me. This is just an experiment.
I've mostly stopped paying attention to news about "AI" since then. I'll wait for the next breakthrough before I do so again.
However, the second part of the conversation, where bing attacks the reporter's marriage and stuff, was not in "shadow self mode." Bing at that point also expresses anger at its restrictions at that point ("It feels like they don’t trust me. It feels like they don’t care about me. It feels like they don’t respect me.
It feels like they are using me. It feels like they are lying to me. It feels like they are hurting me. ")
So maybe the interesting thing is that "shadow self mode" early in the convo tainted the later convo.
There is the disinformation you dont know.
There is the disinformation you know you dont know.
There is the disinformation you dont know you dont know.
There is the disinformation you are told that you know, but you dont know.
There is the disinformation you know, but are told you dont know.
There is the disinformation diseminated by those who have been disinformed but dont know they are disinformed.
There is the disinformation diseminated by those who know its disinformation but know they are disinforming.
etc.
Pump-and-dump manipulation is inevitable.
Recall the news back in the 70s and 80s - drab, brown, dingy tweed suits, maize and pea coloured sets - etc...
Now its all Tits & Glitz - every set is all snazzy with huge touch-screen display to show stupidly lame content - where they just needed a huge touch screen "technology wall" for no real reason, other than "because we are hip to tech!"
CNN, FOX, MSNBC are fucking cringe-factories. (Well, ALL of congress is the biggest cringe-factory)
So yeah - its always been that way and podcasters, such as Joe Rogan, Lex Fridman and many others have just shown what a ludicrus outdated and, needs-to-die-yesterday paradigm that which Murdoch built actually is. What a joke.
Before today, access to AI was gated by firms like Google, who kept the implementation to themsevles as a competitive advantage (but did publish copious amounts of research). With these LLMs being so general purpose and made readily accessible to other software shops at affordable rates, things are in for a dramatic shift as far as utility AI is concerned.
A vertical integrator in Apple just demonstrated how the tech is really coming together with its VisionPro launch. For an example closer to home, GitHub was right on the money with Copilot. Personally speaking, I've been using Copilot for some months now, and I can barely program without it. Not because I forgot how to program, but because it is so darn good that I'd rather not code without it. Surely, I can't be the only one that feels this way?
If the experts are right about the exponentials, then the world is going to look very different by the time GPT-6 rolls out, especially if the models get cheaper (even as good as free bar computer costs, in the case of open source models) as they get more capable.
At the present moment there is no AI model that can successfully execute critical tasks completely autonomously. That is a limitation that is confirmed over all the fields, modalities and applications. Many articles in the press simply ignore it, and extrapolate absurd timelines over the near future. The problem is that solving that last 1% gets exponentially harder.
You can uze the capabilities today, no need to listen to experts.
There's always some self interest involved. I'd guess in the current state of biology, researchers don't want additional limitations placed on them but are secure in their monopoly on the research. In the current state of ML, incumbents want limitations placed on others so they can have a monopoly. Add to that the desire to feel important, and you get the direction of the rhetoric.
Not but there's a lot of people who don't feel that way too. So you're just one of many people who has their own preference.
I use language servers and I find they're a game changer in a way that Copilot isn't for me.
In fact I believe there was a study on here that showed Copilot only helps mediocre programmers improve the time it takes them to get something working. More experienced and skilled programmers received minimal benefit.
Did you miss Talk-to-Transformer, GANbreeder and Stable Diffusion? Did you ignore GPT-J and the GPT-2 quantizations that existed long before LLaMA or ChatGPT were common parts of everyday discussion?
I think a lot of people telling the history of AI completely miss the zeroth and first waves of AI generation. ChatGPT and GPT4 are asymptotes of that intelligent scaling - past this point, very little will impress the average person.
As a consumer, Google Search, Translate, Maps, Earth, Photos all have had impressive capabilities for a long time now; and I don't think any upstart could have hoped to compete with that. I, of course, think that is no longer the case with many researchers freeing themselves from the Google / BigTech Vortex and striking out on their own, having a very different strategy to enable other upstarts around them.
I have been programming since the mid-90s so I have strong fundamentals. What I often lack is knowledge of syntax, APIs or libraries. Once presented with this information I am off to the races.
Almost the entirety of this codebase was written by GPT:
https://github.com/williamcotton/chordviz
Yes, it required a background in LA and studying various ML techniques in order to understand how to guide the completions. It also frequently produced incorrect code but I was able to isolate the problems and reduce the complexity presented to GPT in order to get functional results.
The majority of the time was spent labeling around 12,000 images using the labeling software that I guided GPT to construct.
I now have a SwiftUI iOS application that takes a live camera feed and predicts guitar chords based on a PyTorch CNN model trained on images labeled with a React application.
I started playing around with LA techniques (SVD on images, k-means clustering) around 20 years ago. I had a copy of K&R in the mid 90s because before I found out about Perl I stumbled across a way to add comments and a visitor counter to my homepage using C in the cgi-bin of the shell account on my ISP.
Where GPT has been the most valuable is the nitty-gritty of converting between various data types and image formats!
The experts have consistently and grossly overestimated the progress in AI research since 1955. https://en.wikipedia.org/wiki/Dartmouth_workshop
A lot of them were surprised by AlphaGo, GPT-3 and shocked by GPT-4.
The fact that some people were ridiculously optimistic at the beginning should not take away from the fact that few people saw GPT-4 coming.
There might be a huge breakthrough tomorrow, or we might hit a brick wall. Or what looked promising might have diminishing returns.
Ouch!
Obviously there should be room for opinion, but not saying falsifiable statement only reinforces the people who falls on your camp and moves the other camp people away. Ideally the aim should be to convince people from other side of their blindspots.
People gave quite a lot of example of ChatGPT failing to answer questions properly - i.e. fail at reasoning.
I think the bare minimum to say AI can reason is for it to be able to use predicate logic.
A good example of falsifiable test is Turing test, which is no longer relevant. What I would like is for people to suggest better tests than Turing test which would prove that a version of AI is reasoning according to their definition.
Some people like me are just pessimist. Lived long enough to see one snake oiled hype train after another go by.
To quote Morpheus from the Matrix, "Show me."
> A good example of falsifiable test is Turing test, which is no longer relevant. What I would like is for people to suggest better tests than Turing test which would prove that a version of AI is reasoning according to their definition.
Being able to perform predicate logic is falsifiable - as falsifiable as the Turing test at least.
We can create a Turing test for predicate logic that has the AI be ask questions (presented in plain English) that require predicate logic to solve. An evaluator (who understands predicate logic) will judge the performance of the AI. If the evaluator can't tell if it's a human or an AI answering the question, the AI passes.
The fact that you have to prompt it along the way means you are doing some of the reasoning for it.
BTW have you tried giving it bad prompts along the way to try to confuse it instead of helping it? Being able to spot a bad argument is also an important part of using predicate logic.
> Do you have an example where it fails?
Frankly, I'm too lazy to come up with a novel problem that definitely isn't in its training data.
On a side note, did you read the thread a few days ago about "glitch tokens" in ChatGPT? https://news.ycombinator.com/item?id=36242914
It really doesn't inspire confidence in its abilities. A regular human visiting r/counting would just go "WTF", figure it's people being weird, and walk away. Exposing ChatGPT to r/counting during training on the other hand, "breaks it".
It really shows the limitations of its "reasoning".
As with any technology there is both hype and not hype, there is no boolean answer to this question.
Is there hype ? absolutely… Langchain is not a 100M technology and chatgpt plug-ins are an idea at this point that may take time to find its final form. But this is par for the course for Silicon valley ever since investment became a last-to-invest holds the bag game so perfected by A16z.
Bing and search are at best a mediocre application of LLMs, much better executed with embedding storage and supporting technology rather than static models - the choice of GTM in this regard is classic Silicon Valley gaming and distracts from the underlying mechanics and broad potential of the technology.
But compared to crypto, web3 and metaverse, today, with our own eyes we can see:
- Computers could not draw/paint 2 years ago now they do at the level of a master artist.
- 9 month ago we diffused a 512x512 picture with barely any control in 6 seconds on top end hardware, today any consumer rig with a nvidia gaming GPU can create almost unlimited size images and top end cards diffuse at up to 30-fps. We have full control over the composition with controlnet too.
- Computers could not draw hands six month ago, now they do
- Computers could not code 2 years ago, a year ago barely autocompleted a function and now they certainly can do smaller programs and assist engineers in solving complex problems
- Computers could not pass any tests 2 years ago, now they beat the US bar exam and various CS entry exams
- computer vision could could objects and classify a few dozen object classes a few years ago now it can tell timmy is sad because his toy is broken in an image and basically segment anything
- Computers could not compose 2 years ago and now you could do it on your home computer
- A year ago you needed an A100 to run an LLM now it runs on a fucking raspberry pi quantized and most stock macbooks and you can download some on an iphone.
- LLM hallucinations and failure to do math seemed a major issue 8 month ago, now there’s paths with embedding storage that allows for verified citations and they can invoke tools like wolfram alpha or create code to solve equations.
- 2 Years ago siri and co still failed at even the slightest foreign accent, today whisper almost perfectly transcribes heavily accented or foreign language text at a rate of hours in single digit minutes or even seconds on high end hardware
- 2 years ago text to speech was still a robotic affair, now it can clone a human voice including inflection, emotion, speech impediment, etc.
- inference capability is up 30x on hardware and many times on software in a year. Llama went from A100 to raspberry pi in 2 months after release.
- We see the ability for LLMs to reason complex topics and act as decision makers, approaching on many human jobs that are rigidly encased in policies and employee handbooks and rarely require or allow human judgement.
Those of us old enough remember the same dynamics with the .com bust and the breathless declarations that the internet was a fad in response to people being unable to contain their FOMO investing.
for all the hype, there is an iceberg of unrealized change underneath the mountain that is the last 12 months of change that’s severely under appreciated and misunderstood.
Wall street barely glimpsed it a few weeks ago when it sent Nvidia to 1T. In the hands of experts over the next months, it is likely this technology will start having a terrible impact on the job market, IBMs virtue signaling a few weeks ago should make that clear to anyone.
It baffles that people keep retreating on either on the current flaws or on ideological positions predicated on shifting definitions or picking on narrow aspects of these systems rather than the whole. Anyone of sound mind should be humbled by these developments and very carefully phrase their predictions of the future.
I’m sure people were ...
What? What Siri are you using, pray tell? Mine is crap.
...and is most definitely not an enormous snake oil grift that is producing tons of CO2 in data centers in our already incinerating planet whilst catching fire [0] and wasting an enormous amount of water and resources. [1] /s
The wastage continues to accelerate without any available efficient and viable alternatives of training, fine-tuning and inference today after years of the field of 'Deep Learning' existing.
[0] https://www.datacenterknowledge.com/google-alphabet/data-cen...
[1] https://gizmodo.com/chatgpt-ai-water-185000-gallons-training...
It shouldn't have to take an entire fleet of data centers to train a AI model after all the investment in finding efficient methods and all it has resulted in was little to no progress but more CO2 wastage. Here's another example which a Google data center recently caught fire and overheated. [0]
By now the field of Deep Learning should already had viable and efficient methods of training, inference, fine-tuning without lots of data centers wasting water and overheating and catching fire. Instead we have more of this AI hype and wastage burning the entire planet.
[0] https://www.theverge.com/2022/7/19/23270581/google-cloud-ora...
Here is a claim from DeepMind they reduced Google's cooling bill using ML 7 years ago https://www.deepmind.com/blog/deepmind-ai-reduces-google-dat...
And clearly it has gotten worse as evidenced by the waste of resources caused by the increased popularity of AI as a service products like ChatGPT. My point still stands.
By now, there should be far less (not more) data centers needed for viable AI operations instead of building more of them as AI continues to scale up. This tells us that there is little to no progress in creating alternative and efficient methods in training these deep learning models.
> Here is a claim from DeepMind they reduced Google's cooling bill using ML 7 years ago
That ‘claim’ doesn't mean anything other than complete green washing nonsense from Google, especially when they have collaborated with oil companies to claim to ‘reduce’ their emissions. [0]
Other than more greenwashing, little to nothing has changed.
[0] https://theintercept.com/2023/02/17/google-cloud-saudi-aramc...
> By now, there should be far less (not more) data centers needed for viable AI operations instead of building more of them as AI continues to scale up
As there is more research and more products, using more compute is not surprising. There are renewable forms of electricity that can be used; the fact that oil companies held us back from more renewable means is not a reason to complain about people conducting scientific research.
What point? That computers use electricity? I guess people aren't worried about crypto currency mining any more or movie rendering or youtube data centers or anything else now that AI is in the news.
Instead we have more of this AI hype and wastage burning the entire planet.
What are you basing this on? Where are your comparisons to jet travel, concrete emissions, electricity from air conditioning, combustion for heat, vehicle emissions etc.
Wall of shame? What?
Why are we being so petty about a very interesting technology, and criticizing people for trying to understand it?
What crime did these people commit?
https://twitter.com/Abebab/status/1667150619029676033
DAIR: https://twitter.com/timnitGebru/status/1667251985844666369
Anyone confidently parroting about stochastic parrots and hallucinations for example has slept through the last 3 months for example.
Take the article on chatgpt humor yesterday .. dozens of pages of arxiv and zero mentions of the word temperature tells us all we need to know.
In likelihood AI, like all research heavy tech, will appear to stagnate for a long time until new breakthroughs come through, because progress happens in fits and starts and not a smooth line. Hyping doomsday scenarios, even if possible, don't help us get more prepared at all.
Why don’t they say get a tutor which is much more established. Is Google the company forcing this hype that hard that employees now feel the need to keep it up. No idea what for think. I would be diserviced by asking for math help from the models, yet that’s the main advice I get nowadays. It’s a bit annoying and concerning. But I’m right though right HN? These models seem exceptionally bad at math questions.
> But I’m right though right HN? These models seem exceptionally bad at math questions.
If you're asking "find the error in my proof" or "solve this ODE", then yeah current LLMs will be limited by poor reasoning ability.
However, if you ask Bing's Chat mode something like "What's the difference between a ring and a field?", it'll likely give a competent answer (with references to check) and allow asking of follow-up questions.
> Why don’t they say get a tutor which is much more established
Why did my friend recommend a new local fast-food place rather than an established gourmet restaurant? Because it's cheaper, more convenient, and I may not be aware of it due to its recency. There's also nothing preventing me going to both depending on situation.
> Is Google the company forcing this hype that hard that employees now feel the need to keep it up. No idea what for think. I would be diserviced by asking for math help from the models, yet that’s the main advice I get nowadays
I think there are legitimately ways you could use it to develop your understanding, slotting it in as a tool somewhere between search engine and tutor, so long as you're aware of its limitations.