Ask HN: Is anyone else getting AI fatigue?
AI is great. ChatGPT is incredible. But I feel tired when I see so many new products being built that incorporate AI in some way, like "AI for this..." "AI for that..." I think it misapplies AI. But more than that, it's just too much. Right? Right? Anyone else feel like this? Everything is about ChatGPT, AI, prompts or startups we can build with that. It's like the crypto craze all over again, and I'm a little in dread of the shysters again, the waste, the opportunity cost of folks pursuing this like a mad crowd rather than being a little more thoughtful about where to go next. Not a great look for the "scene" methinks. Am I alone in this view?
378 comments
[ 3.6 ms ] story [ 287 ms ] threadI think we're now past that and people can see that tools like ChatGPT are powerful enough to be applied in many pre existing contexts and industries in unpredictable and inventive ways without huge amounts of manual configuration, which makes it more exciting.
Language models are right now at the very top of the peak of inflated expectations. It's still too early to tell what the real impact will be, but it won't be even remotely close to what you read on the headlines.
Far more impressive technology (like Wolfram Alpha) has existed for almost a decade now, and it's directly comparable to language models for many applications.
My guess is they will end up being something like Rust. Very cool to look at, little impact on your day-to-day.
Sorry, I couldn't help; that is the ChatGPT response to your question. More informatively, AI is clearly at the height of inflated expectations. It will provide a helpful tool. However, it will not push people out of jobs. Furthermore, right now it gives a much better search experience than Google, as it is not yet filled with ads or has been gamed extensively by SEO. It is doubtful this will stay like this in the future.
In other words, if you’re fatigued already, I have some bad news regarding the rest of your life.
> The business plans of the next 10,000 startups are easy to forecast: Take X and add AI.
I think the AI hype cycle isn't done building. A few days ago, Paul Graham tweeted[2] this:
> One of the differences between the AI boom and previous tech booms is that AI is technically more difficult. That combined with VC funds' shift toward earlier stage investing with less analysis will mean that, for a while, money will be thrown at any AI startup.
[1]: https://twitter.com/kevin2kelly/status/718166465216512001
[2]: https://twitter.com/paulg/status/1623060319403905026
This is all speculative, of course, but I have seen the fall of the Soviet system, and I am well aware that forms of government are not eternal.
tl;dr but yes. Crypto of the future will look more or less similar to the crypto of today. Governments of the future will look nothing like today’s nation-states.
It will be increasingly tiresome until it becomes commonplace, then the disastrous consequences will become the next tedium.
When I started with the topic I watched a documentary with Joseph Weizenbaum ([1]) and felt weirded out that someone would step away from such an interesting and future-shaping topic. But the older I get, the more I feel that technology is not the solution to everything and AI might actually make more problems than it solves. I still think Bostrom's paperclip maximizer ([2]) is lacking fundamental understandings of the status quo and just generated unnecessary commotion.
[1] http://www.plugandpray-film.de/en/ [2] https://www.lesswrong.com/tag/paperclip-maximizer
I'm finding the current hype cycle very frustrating from both sides. On one side there is frequent overplaying current capabilities, and cherry picked examples given as it they're representative. On the other side there is an over simplistic "AI is evil" reaction. It's hard to deny that progress in the past few years greatly exceeds expectations and could make a significant improvement to individual creativity and learning, as well as how we cooperate but so much of the discussions are fear based.
> I love the models, the statistics and just the cleverness of everything but I just can't stand the "scene" anymore
This really sums up my feelings too.
(…or take a good step back from the news cycle, check in once or twice a week instead of several times daily. News consumption reduction is good for mental health.)
I'm actually optimistic about both crypto and AI, but I see the authors point. I really don't think the comparison is hard to spot between the AI hype and, say, the NFT hype from 1 year ago.
A lot of people are claiming that these technologies will imminently change everything, fundamentally. In reality, both of them are just neat things that give us a glimpse of what the future may hold, and hold a bunch of promise, but aren't really changing anything fundamentally. Not yet, at least.
At least all the previous crazes didn't threaten to replace humans, so I suppose this tech hype bubble is arguably even more irritating.
In the meantime, all the attention and media is easing people into thinking about some difficult questions that we may end up having to deal with sooner than we'd like.
The hype can be annoying, and I'm sure they'll be suckers who lose a lot of money chasing it, but I'm also sure AI will get better, and be better understood too, as a result of all of the attention and attempts to shoehorn it into new roles and environments.
It's not AI it's an IF statement for crying out loud :-(
But this is the industry we're in, and buzzword-driven headlines and investment are how it goes.
Actual proper AI getting some attention makes a pleasant change tbh :-)
They are not addressing the public or swaying opinion
The thing is that AI is just about the most general term for the type of computing that gives the illusion of intelligence. Machine learning is a more specific region of the space of AI, and generally is made of statistical models that lead to algorithms that can train and modify their behavior based on data. But this includes "mundane" algorithms like k-means clustering or line-fitting. Deep learning (aka neural networks) is yet a more specific subfield of ML.
I think the term AI just has more "sex appeal" because people confuse it with the concept of AGI, which is the holy grail of machine intelligence. But we don't even know if this is achievable, or what technology it will use.
So in terms of conceptual spaces, we can say that AI > ML > DL, and we can say (by definition) that AI > AGI. And it seems very likely that AGI > ML. But it's not known, for instance, whether AGI > DL, ie, we don't know for sure that deep learning/neural networks are sufficient to obtain AGI.
In any case, people should put less weight on the term AI, as it's a pretty low bar. But also yes, the term is way over hyped.
As folks that work in tech we can tell the difference between stuff that's got some form of depth to it in "proper" AI: ML, DL, AGI as you suggest, vs the over-hyped basic computation stuff. And the selling of the latter as the former can rankle.
https://arxiv.org/abs/2210.05189 but all NNs _are_ if statements!
Just like with most of these hype cycles there is an actual useful interesting technology, but the hype beasts take it way overboard and present it as if it's the holy grail or whatever. It's not.
That's tiring, and really annoying.
It's incredibly cool technology, it is great at certain use cases, but those use cases are somewhat limited. In case of GPT-3 it's good at generative writing, summarization, information search and extraction, and similar things.
It also has plenty of issues and limitations. Lets just be realistic about it, apply it where it works, and let everything else be. Now it's becoming a joke.
Also, a lot of products I've seen in the space are really really bad and I'm kinda worried AI will get a scam/shitty product connotation.
made up bullshit
> summarization
except you can't possibly know the output has any relation whatsoever to the text being summarized
> information search and extraction
except you can't possibly know the output has any relation whatsoever to the information being extracted
people still fall for this crap?
I think it's fair to say this is not one of the use cases where it shines. It's not great at logic, it's also not that smart.
That's exactly what the hype does. Too big claims and then it gets dismissed when it inevitably doesn't live up to the hype.
It will even claim it can generate citations for you too, which is pretty messed up because when I tried it just fabricated them replete with faked DOIs.
Where it shines is at squishy language stuff, like generating the framework of an email, paraphrasing a paragraph for you, or summarizing a news article.
It really is revolutionary at language tasks, but unfortunately the hype machine and these weird "AI sycophants" have caused people to dramatically overestimate it's use cases.
Yes, it's overhyped, but it's not useless, it actually does work quite well if you apply it to the right use cases in a correct way.
In terms of accuracy, in ChatGPT the hallucination issue is quite bad, for GPT3 it's a lot less and you can reduce it even further by good prompt writing, fine tuning, and settings.
Can we just recognize it for what it is?
I think we're already there. A legion of AI based startups seem to be coming out daily (https://www.futuretools.io/) that offer little more than gimmicks.
My last resort is to just remove all AI references from my marketing and just deliver the product.
See also: Gartner hype cycle
I agree with this, I feel like I've seen a lot of really cool technology get swept up in a hype storm and get carried away into oblivion.
I wonder what ways there are for the people who put out these innovations to shield them/their products from it?
Luckily I have a lot of faith in the OpenAI people - I hope their shielding themselves from the technological form of audience capture.
I've criticized it whenever it gets brought up as an alternative for academic research, coding, math, other more sophisticated knowledge based stuff. In my experience at least, it falls apart at reliably dealing with these and I haven't gone back.
But man, is it ever revolutionary at actually dealing with language and text.
As an example, I have a bunch of boring drama going on right now with my family, endless fucking emails while I'm trying to work.
I just paste them into chat gpt and get it to summarize them, and then I get it to write a response. The onerous safeguards make it so I don't have to worry about it being a dick.
Family has texted me about how kind and diplomatic I'm being and I honestly don't even really know what they're squabbling about, it's so nice!
Good luck with the drama! Make sure to read a summary for the next family meeting haha.
Yeah I will be sure to read it before meeting them, would be awkward if they found out I was using it during one of the disputes, which was whether or not to keep resuscitating Grandma.
ChatGPTs stupid ethical filters made it so I actually had to type my response to that one all by myself.
Which has happened before. The original semantic/heuristic AI, most notably expert systems, over-promised and ultimately under-delivered. This led directly to the so-called "AI winter" which lasted more than two decades and didn't end until quite recently. It's a very real concern, especially among people who want to push the technology forward and not just profit from it.
It seems too exciting to me and I am eager to see more AI. It's fascinating stuff.
It's bold (to put kindly) how lengthy some of these critical comments are from folks who later in the thread admit to not personally used Copilot (for example) much themselves.
The quality of LLM output can wildly vary based on what prompts (or series of prompts) are used.
The sad truth is that ChatGPT is about as good an AI as ELIZA was in 1966, it's just better (granted: much better) at hiding its total lack of actual human understanding. It's nothing more than an expensive parlor trick, IMHO.
Github CoPilot? Great, now I have to perform the most mentally taxing part of developing software, namely understanding other people's code (or my own from 6 months ago...) while writing new code. I'm beyond thrilled ...
So, no, I don't have an AI fatigue, because we absolutely have no AI anywhere. But I have a massive bullshit and hype fatigue that is getting worse all the time.
I've yet to see it. It's barely above IDEAs autocomplete in the rare cases when it manages to trigger on my code, and it has already been wrong more than once in the few times it did deign to provide autocompletion.
Copilot is not like autocomplete. It only works in the second mode, because any nontrivial code it generates needs to be read, considered, and understood. (And any trivial code it generates can be done by autocomplete or long-existing non-AI tools.) This is especially true given LLMs' hallucinatory behavior - by definition it will often spit out something that "looks right" even if it's absolutely not - and such code is harder to review than code that looks obviously wrong.
So if you do use autocomplete in the second mode, maybe you find Copilot a super-powered version of that. And if you have the same weaknesses as Copilot, reviewing its code after it's done writing it is probably not any different than reviewing your own code after writing it, so for you it takes the same amount of time. For me, that's not the case.
When I used it, and when I see others use it, Copilot is like an impossibly overenthusiastic junior developer I will never be able to teach better habits to.
If writing boilerplate becomes effortless, then you'll write more of it, instead of feeling the pain of writing it and then trying to reduce it, because you don't want to spend time writing it.
And since Copilot was accepted as a way to help the developers on the teams, the increase of boilerplate have been immersive.
I'm borderline pissed, but mostly at our own development processes, not at Copilot per se. But damn if I didn't wish it existed somehow, although it was inevitable it would at one point.
Has it really? Or are you worried that this is something that will happen?
Of course I don't know how other people use it but I find that it's very much like having a fairly skilled pair programmer on board. I still need to do a lot of work but I get genuine help. I don't find that I personally write more boilerplate code than before, every programming principle applies as it always has.
One simple example that I've had to reject more than once.
- Function 1 does something
- Developer needs something like Function 1 but minor change
- Developer starts typing name of function which has a similar name to Function 1, but again, minor difference
- Copilot helpfully suggests copy-pasting Function 1 but with the small change incorporated
- Developer accepts it, commits and sends the patch my way
Rather than extracting the common behavior into it's own function and call that from both of them, refactors which Copilot doesn't suggest, the developers is fine with just copy-pasting the function.
Now we have to maintain two full slightly different functions, rather than 1 full functions + 2 minor ones.
Obviously a small example, and it wouldn't be worth extracting it the first time it happens or on a smaller scale. But once you have entire teams doing something like this, it becomes a bit harder to justify copy-paste approach, especially when you want the codebase not to evolve to complete spaghetti.
And finally, I'm not blaming the tool, it's not Copilots fault. But it does seem to have made developers who rely on it think less, compared to the ones that don't.
Why not get some of the freed up, Copilot augmented developer labor budget moved to testing and do more there or build more tools to make your personal, boilerplate, repetitive tasks more efficient?
If the coders are truly just dumping bad code your way, that's an externality and the cost should be called out.
Often I have times where I'm think about a specific piece of code that I need and I have it partially in my head and github copilot "just completes" it. I press tab and that's it.
I'm not talking about writing entire functions where you have to mentally strain yourself to understand what it wrote.
But I've never seen any autocompleter do it so good then github copilot. Even for documentation purposes like JSdoc and related commenting system it's amazing.
It's a tool I pay for now since it's proven to be a tool that increases my productivity.
Is it gonna replace us? I hope not, but it does look promising as one of those tools people will talk about in the future.
Helping write boilerplate is to Copilot what cropping is to Photoshop.
Some of the ways I've found Copilot a powerful tool in my toolbox: Writing missing comments (especially unfamiliar code bases), "translating" parts of unfamiliar code to a more familiar language, suggesting ideas for how to implement a feature (!) in comments.
The writing is on the wall. Programming as we know it is going to end. We should be embracing these tools and should start moving from software developers to software architects role.
What I have seen about it ranged from things that can be nearly just as well handled by your $EDITOR's snippet functionality to things where my argument kicked in - I have to verify this generated code does what I want, ergo I have to read and understand something not written by me. Paired with the at least somewhat legally and ethical questionable source of the training data, this is not for me.
ad.: Code review takes less time than writing code for the same reason reading a book takes less time than writing one. Distillation and organization of ideas requires expertise gained through experience and long thought. Reading a book requires reading ability.
Understanding a book (and the intricacies underlying it) takes effort on the order of the original writing, but most people don't seek that level of understanding. The same is true of code.
However, in this case, it does seem that there is a level of fraudulence and deception. Given that “fake” often is used exactly the way you say, maybe “fake intelligence” would indeed be a more appropriate term.
[1] Synonyms of artificial has "faked" : https://www.thesaurus.com/browse/artificial
[2] Synonyms of fake has "artificial": https://www.thesaurus.com/browse/fake
But defining "intelligence" is a philosopical question that doesn't necessarily have one answer for everything and everyone.
Intelligence may be a fuzzily defined word in everyday usage, but I don't think it's the mystery you present it to be. Joe public may argue against any and all definitions of the word that they personally disagree with (maybe just dislike), but it's nonetheless quite easy to come up with a straightforward and reductive definition if you actually want to!
It’s also plain that many people are very interested in looking inside the black box and think the contents of the black box are relevant and important. This fact doesn’t change just by your saying so.
Being able to define what you want to achieve isn't generally the same as knowing HOW to achieve it (except in this case the definition of intelligence rather does suggest the right path).
It is often frustrating that English has words with such different (but clearly related) definitions, as it can make it far too easy to end up talking past each other.
[0] https://en.wiktionary.org/wiki/artificial
If you don't want to be banned, you're welcome to email hn@ycombinator.com and give us reason to believe that you'll follow the rules in the future. They're here: https://news.ycombinator.com/newsguidelines.html.
But yes indeed, there are many, many AI products launched during this era of rapid progress. Even kind of shoddy products can be monetized if they provide value over what we had before. I think the crowded market and all the bullshit and all the awesome, all at once, is a sign of very rapid progress in this space. It will probably not always be like this and who knows what we are approaching.
I'll also throw random programming questions into it, and it's been hit and miss. SO is probably still faster, and I like seeing the discussion. The problem with chatGPT right now is it gives an answer like it's certainty when it's often wrong.
I can see the benefits of this interaction model (basically summarizing all the things from a search into what feels like a person talking back), but I don't see change the world level hype at the moment.
I also wonder if LLMs will get worse over time through propagation error as content is generated by other LLMs.
- Embedding free text data on safety observations, clustering them together, using text completion to automatically label the clusters, and identifying trends
- Embedding free text data on equipment failures. Some of our equipment failures have been classified manually by humans into various categories. I use the embeddings to train a model to predict those categories for uncategorized failures.
- Analyzing employee development goals and locating common themes. Then using this to identify where there are gaps we can fill in training offerings.
ChatGPT, when provided with a synthetic prompt is reliably a synthesizer, or to use the loaded term, a bullshiter.
When provided with an analytic prompt, it is reliably a translator.
Terms, etc: https://www.williamcotton.com/articles/chatgpt-and-the-analy...
sounds like most people tbf
ChatGPT isn't as good as a human who puts in a lot of effort, but in many jobs it can easily outperform humans who don't care very much.
In terms of closing the gap between AI hype and useful general purpose AI tools, no one can reasonably deny that it's an absolute quantum leap.
It's just not a daily driver for technical experts yet.
Ironically accurate.
Given we are not talking about state changes in electrons, there is nothing wrong with this description of ChatGPT - it truly does feel like a massive advance to anyone who has even cursorily played with it.
For example, you can ask it questions like "Who was born first, Margaret Thatcher or George Bush?" and "Who was born first, Tony Blair or George Bush?" and in each instance it infers which George Bush you are talking about.
I honestly couldn't imagine something like this being this good only three years ago.
(2) Counter. I asked it the other day "how many movies were Tom Hanks and Meg Ryan in together" and the answer ChatGPT gave was 2 ... not only is that wrong it is astonishingly wrong (IMO). You could be forgiven for forgetting Ithaca from 2015. I could forgive ChatGPT for forgetting that one. But You've Got Mail? That's a very odd omission. So much so I'm genuinely curious how it could possible get the answer wrong in that way. And for the record, Google presents the correct answer (4) in a cut out segment right at the top, a result and presentation very close to what one would expect from ChatGPT.
I don't know about other use cases like generating stories (or tangentially art of any kind) for inspiration, etc. But as a search engine things like ChatGPT NEED to have attributions. If I ask the question "Does a submarine appear in the movie Battlefield Earth?" it will confidently answer "no". I _think_ that answer is right, but I'm not really all that confident it is right. It needs to present the reasons it thinks that is right. Something like "No. I believe this because (1) the keyword submarine doesn't appear in the IMDb keywords (<source>), (2) the word submarine doesn't appear in the wikipedia plot synopsis (<source>), (3) the film takes place in Denver (<source>) which is landlocked making it unlikely a submarine would be found in that location during the course of the film."
The Tom Hanks / Meg Ryan question/answer would at least more interesting if it explained how it managed to be so uniquely incorrect. That question will haunt me though ... there's some rule about this right? Asking about something you have above average knowledge in and watching someone confidently answer it incorrectly. How am I supposed to ever trust ChatGPT again about movie queries?
Before this point in history we accepted 'I am that I am' because there wasn't any challenger to the title. Now that we are putting this to question we realize our definitions may not work well.
My point was that “consciousness” and “intelligence” are very different things. One does not imply the other.
Consciousness is about self reflection. Intelligence is about insight and/or problem solving. The two are often correlated, especially in animals, especially in humans, but they’re not the same thing at all.
“Is chatgpt consciousness” is a totally different question than “is chatgpt intelligent”.
We will know chatgpt is intelligent when it passes our tests of intelligence, which are imperfect but at least directionally correct.
I have no idea if/when we we know whether chatgpt is conscious, because we don’t really have good definitions of consciousness, let along tests, as you note.
Well, I'm no fan of chatGPT. But it appears most people are worse than chatGPT, because just regurgitate what they hear with no thought or contemplation. So you can't really blame average folks who struggle with the concepts of intelligence/understanding that you mention.
I think there's an argument to be made that AI is being used here to help you tackle the more trivial tasks so you have more time to focus on the more important, and challenging tasks. Albeit I recognise GitHub CoPilot is legally questionable.
But yes, I agree with your overall point that AI has still not been able to 'think' like a human but rather can only still pretend to think like a human, and history has shown that users are often fooled by this.
Copilot is amazing for reducing the tedium of typing obvious but lengthy code (and strings!). And it’s inline and passive; it’s not like you go edit -> insert -> copilot function and it dumps in 100 lines of code you have to debug. Which is what it sounds like parent is mistaking it for.
I’m reminded of 1995, when an elderly relative told me everything wrong with the internet based on TV news and not having ever actually seen the internet.
Which it occasionally mistypes. Then you're off to chase a small piece of error in a tub of boilerplate. Great stuff! For actual example, see [0]
[0] https://blog.ploeh.dk/2022/12/05/github-copilot-preliminary-...
And if it’s wrong, you just. . . keep typing. It’s autocomplete, just like IDEs have for other things. I’m kind of astounded that people have such an emotional reaction to an optional, low-key, passive, easily-ignored tool that sometimes saves a bunch of typing. Yes, if you always accept the suggestions you’ll have problems. Just like literally every other coding assistance tool.
> I was thinking more like:
That example is straight up from any of those "programming is not bound by typing speed" essays of yore.
> people have such an emotional reaction to an optional, low-key, passive, easily-ignored tool that sometimes saves a bunch of typing.
Maybe because it's not generally advertised by proponents as "an optional, low-key, passive, easily-ignored tool that sometimes saves a bunch of typing"? Just look at the rest of the thread, it's pronounced as a game-changer in productivity.
I can see how someone who’s always working on sophisticated, mentally challenging code would get less benefit and would see more frequent errors.
Hype or not, it's incredibly useful and has increased my productivity by at least 20%. Worth every penny.
Lack of actual human understanding? Of course, by definition a machine will always lack human understanding. Why does that matter so much if it's a helpful tool?
For what it's worth, I do agree that there is a lot of hype. But contrary to blockchain, NFTs, web3, etc., this is actually useful for many people in many everyday use cases.
I see it as more similar to the dot com hype - buying a domain and creating a silly generic website didn't really multiply the value of your company as some people thought in that era, but that doesn't mean that websites weren't a useful technology with staying power, as time has shown.
I don't think there is any because there is no functional model for what organic intelligence is or how it operates. There are plethora of fascinating attempts / models but only a subset implore that it is solely "statistical". And even if it was statistical, the implementation of the wet system is absolutely not like a gigantic list of vectorized (stripped of their essence) tokens
See Scott Alexander for attempts to explain what is apparently impenetrable papers on it.
Consciousness is a subjective experience (regardless of what you believe/understand to be responsible for that experience), so discussing "consciousness/intelligence" is rather like discussing "cabbages/automobiles".
https://twitter.com/marvinvonhagen/status/162365814434901197...
We're about six minutes away from "AI bros" becoming a thing.
The same kind of grifters who always latch onto the latest thing and hype it up in order to make a quick buck are already knocking on AI's door.
See also: Cryptocurrency, and Beanie Babies.
I suppose it makes sense though. Denial is the default response when we face threats to our identity and sense of self worth.
Think about it.
What's the most expressive medium we have which is also absolutely inundated with data?
To broadly be able to predict human speech you need to broadly be able to predict the human mind. To broadly predict a human mind requires you build a model of it, and to have a model of a human mind? Welcome to general intelligence.
We won't realize we've created an AGI until someone makes a text model, starts throwing random problems at it, and discovers that it's able to solve them.
Language is way, way far removed from intelligence. This is well-known in cognitive psychology. You'll find plenty of examples of stroke victims who are still intelligent but have lost the ability to produce coherent sentences, and (though much rarer) examples of people who can produce clear, eloquent prose, yet are so learning and mentally challenged that they can't even tell the difference between fantasy and reality.
This is a non sequitur. The human mind does a whole lot more than string words together. Being able to predict which word would logically follow another does not require the ability to predict anything other than just that.
The expressiveness of language lets this be true of almost everything.
> Being able to predict which word would logically follow another does not require the ability to predict anything other than just that.
Why? Wouldn't you expect that technique to generally fail if it isn't intelligent enough to know what's happening in the sentence?
ChatGPT is good at making up stories.
We don't really understand what intelligence means -- in humans or our creations -- but ChatGPT gives us a little more insight (just like ELIZA, and the psychological research behind it, did).
At the very least, ChatGPT helps us build increasingly better Turing tests.
It is also obvious that we are in the middle of a shift of some kind. Very hard to see from within, but clearly we will look back at 2022 as the beginning of something…
It's important to note that this is your assumption which I believe to be wrong (for most people here).
> These arguments take the form, "I grant you that you can make machines do all the things you have mentioned but you will never be able to make one to do X."
> [...]
> The criticisms that we are considering here are often disguised forms of the argument from consciousness, Usually if one maintains that a machine can do one of these things, and describes the kind of method that the machine could use, one will not make much of an impression.
Every time "learning machines" are able to do a new thing, there's a "wait, it is just mechanical, _real_ intelligence is the goalpost".
[0] https://www.espace-turing.fr/IMG/pdf/Computing_Machinery_and...
Just because people shift the goalposts doesn't mean that the new position of the goalposts isn't closer to being correct than the old position. You can criticise the people for being inconsistent or failing to anticipate certain developments, but that doesn't tell you anything about where the goalposts should be.
For _what purpose_, tho? It's a good party trick, but its tendency to be confidently wrong makes using it for anything important a bit fraught.
That's about the only purpose I've found so far, but it seems a big one?
I can't really imagine asking it a question about anything I cared about and not verifying via a second source, though, given its accuracy issues. This makes it feel a lot less useful.
That reminds me how in my youth many were planning on vacations to Mars resorts and unlimited fusion energy) Stars looked so close, only a matter of time!
This seems to have been the rallying cry of AI-ish stuff for the past 30 years, tho. At a certain point you have to ask "but how much time"? Like, a lot of people were confidently predicting speech recognition as good as a human's from the 90s on, for instance. It's 2023, and the state of the art in speech recognition is a fair bit better than Dragon Dictate in the 90s, but you still wouldn't trust it for anything important.
That's not to say AI is useless, but historically there's been a strong tendency to say, of AI-ish things "it's 95% of the way there, how hard could the last 5% be?" The answer appears to be "quite hard, actually", based on the last few decades.
As this AI hype cycle ramps up, we're actually simultaneously in the down ramp of _another_ AI hype cycle; the 5% for self-driving cars is going _very slowly indeed_, and people seem to have largely accepted that, while still predicting that the 5% for generative language models will be easy. It's odd.
(Though, also, I'm not convinced that it _is_ just a case of making a better ChatGPT; you could argue that if you want correct results, a generative language model just isn't the way to go at all, and that the future of these things mostly lies in being more convincingly wrong...)
One of major problems of modern computer-based work is that there are too many people already in those roles, doing work that isn't needed. Case in point: the culling of tens of thousands of software engineers, people who would consider themselves to be doing 'bullshit jobs'.
I cannot but think that this approach of "Strong Opinions, Weakly Held" is a much stronger path forward towards AGI than what we had before.
Respectfully, that reads as needlessly combative within the context. It sounds like the blockchain proponents who say that the only people who are against cryptocurrencies are the ones who are “bitter for having missed the boat”.¹
It is possible and perfectly reasonable to identify problems in ChatGPT and similar technologies without feeling threatened. Simple example: someone who is retired and monetarily well off, whose way of living and sense of self worth are in no way affected by developments in AI, can still be critical and express valid concerns when these models tell you that it’s safe to boil a baby² or give other confident but absurdly wrong answers to important questions.
¹ I’m not saying that’s your intention, but consider that type of rhetoric may be counterproductive if you’re trying to make another understand your point of view.
² I passed by that specific example on Mastodon but I’m not finding it now.
It you ask it to go through and comment code it does a pretty good job of that.
some things better than others(not that great at CSS)
need a basic definition of something. got it.
tell it to write a function it's not bad.
As a BA just tell it what your trying to do and what questions it should ask users. It will get some good ideas for you.
Want it to be a PM have create a loop asking every 10 minutes if your done yet.
Is it a senior engineer? no. can it pass a senior engineering interview? quite possibly.
debug code hit or miss.
I think the big thing it's not that great at front end code. It can't see so that probably makes sense. a fine-tuned version of clip that interacted with a browser would probably be pretty scary.
I agree.
And the worst thing is that the bullshit hype comes round every decade or so, and people run around like headless chickens insisting that "this time its different", and "this time its the REAL THING".
As you say, first(ish) there was ELIZA. Than this that and everything else. Then Autonomy and all that dot-com era jazz. Now with compute becoming more powerful and more compact, any man and his dog can stuff some AI bullshit where it doesn't belong.
I have seen comments below on this thread where people talk about "well, it's closing the gap". The thing you have to understand is that the gap will always exist. Ultimately you will always be asking a computer to do something. And computers are dumb. They are and will always be beholden to the humans that program them and the information that you feed them. The human will always have the upper hand at any tasks that require actual intelligence (i.e. thoughtful reasoning, adapting to rapidly changing events etc.).
I'm sorry, what sort of bullshit argument is that ?
Flight and engines are both natural evolution using natural physics and mechanics.
Artificial Intelligence is nothing but a square-peg-round-hole, when you have a sledgehammer everything looks like a nut scenario.
This. To answer the OPs question, this is what I'm fatigued about.
I'm glad we're making progress. It's a hell of a parlor trick. But the hype around it is astounding considering how often it's answers are completely wrong. People think computers are magic boxes, and so we must be just a few lever pulls away from making it correct all the time.
Or maybe my problem is that I've overestimated the average human's intelligence. If you can't tell ChatGPT apart from a good con-man, can we consider the Turing test passed? It's likely time for a redefinition of the Turing test.
Instead of AI making machines smarter, it seems that computers are making humans dumber. Perhaps the AI revolution is about dropping the level of average human intelligence to match the level of a computer. A mental race to the bottom?
I'm reminded of the old Rod Serling quote: We're developing a new citizenry. One that will be very selective about cereals and automobiles, but won't be able to think.
I think "human level intelligence" is an emergent phenomenon arising from a variety of smaller cognitive subsystems working together to solve a problem. It does seem that ChatGPT and similar models have at least partially automated one of the subsystems in this model. Still, it can't reason, doesn't know it's wrong, and can't lie because it doesn't understand what a lie is. So it has a long way to go. But it's still real progress in the sense that it's allowing us to better see the dividing lines between the subsystems that make up general intelligence.
I think that we'll need to build a better systems level model of what general intelligence is and the pieces it's built out of. With a better defined model, we can come up with better tests for each subsystem. These tests will replace the Turing test.
I came here to make this comment. Thank you for doing it for me.
I remember feeling shocked when this article appeared in the Atlantic in 2008, "Is Google Making Us Stupid?": https://www.theatlantic.com/magazine/archive/2008/07/is-goog...
The existence of the article broke Betteridge's law for me. The fact that this phenomenon it is not more widely discussed describes the limit of human intelligence. Which brings me back around to the other side... perhaps we were never as intelligent as we suspected?
Yeah, I think you're right. Intelligence is just something our species has evolved as a strategy for survival. It isn't about intelligence, it's about survival.
The cognitive skills needed to survive/navigate/thrive in the digital era are very different than the cognitive skills required to survive in the pre-digital era.
We're biologically programmed through millions of years of evolution to survive in a world of scarcity. Intelligence used to be about tying together small bits of scarce information to find larger patterns so that we can better predict outcomes.
Those skills are being rendered more and more irrelevant in a world of information abundance. Perhaps the "best fit" humans of the future are those that possess new form of "intelligence", relying less on reason and more on the ability to quickly digest the firehose of data thrown at them 24-7.
If so, then the AI we were trying to build in the 1950s would necessarily be different than the AI that our grandchilden would find helpful.
I forgot to add something to my original post. >>"I remember feeling shocked when this article appeared in the Atlantic in 2008..."
At the time I was shocked that the question was even being asked!
Time will tell, I certainly can’t predict.
Co-pilot has been semi-useful. It's faster than search SO, but like you said, I still have to review all the code and it's often wrong in subtle ways.
It will turn out to be a useful tool for those who know what they’re asking about so they can check the answer quickly; but it will be USED by tons of people who don’t have a way of verifying the answers given.
It seems more likely that we'll surpass the hype than not in the next few decades. I think people have forgotten how quickly technology can move after the last 20 years of relative stability where more powerful hardware didn't really change what a computer can do.
It looks much less likely for the cost of developing and training an AI system to come down for the time being, making it out of reach for most individuals.
When the PC revolution was happening, everyone interested had a good chance of getting in, they just needed some money to buy/rent a computer and learn to use it or program it.
Compared to that, the AI revolution doesn't seem to have the same quality.
The barrier to entry seems much much higher this time.