It is built on the observation how fast AI is getting better. If the speed of improvement stays anywhere near the level it was the last two years, then over the next two decades, it will lead to massive changes in how we work and which skills are valuable.
Just two years ago, I was mesmerized by GPT-3's ability to understand concepts:
https://twitter.com/marekgibney/status/1403414210642649092
Nowadays, using it daily in a productive fashion feels completely normal.
Yesterday, I was annoyed with how cumbersome it is to play long mp3s on my iPad. I asked GPT-4 something like "Write an html page which lets me select an mp3, play it via play/pause buttons and offers me a field to enter a time to jump to". And the result was usable out of the box and is my default mp3 player now.
Two years ago it didn't even dawn on me that this would be my way of writing software in the near future. I have been coding for over 20 years. But for little tools like this, it is faster to ask ChatGPT now.
It's hard to imagine where we will be in 20 years.
I don't think so. When you say "it's not capable of actually reasoning", that's because it's a LLM; and if it "changes in the future", that's because the new system must no longer be a pure LLM. The appearance of reasoning in LLMs is an illusion.
Rather than a binary it's much more likely that it's a mix of factors going into results that includes basic reasoning capabilities developed from the training data (much like board representations and state tracking abilities developed feeding board game moves into a toy model in Othello-GPT) as well as statistic driven autocomplete.
In fact often when I've seen GPT-4 get hung up with logic puzzle variations such as transparency, it tends to seem more like the latter is overriding the former, and changing up tokens to emoji representations or having it always repeat adjectives attached to nouns so it preserves variation context gets it over the hump to reproducible solutions (as would be expected from a network capable of reasoning) but by default it falls into the pattern of the normative cases.
For something as complex as SotA neural networks, binary sweeping statements seem rather unlikely to actually be representative...
Define reasoning. Because in my definition GPT 4 could reason without doubt. It definitely can't reason better than experts in the field, but it could reason better than say interns.
Your conclusion doesn't follow from your premise.
None of these models are trained to do their best on any kind of test. They're just trained to predict the next word. The fact that they do well at all on tests they haven't seen is miraculous, and demonstrates something very akin to reasoning. Imagine how they might do if you actually trained them or something like them to do well on tests, using something like RL.
If it’s a debate on the illusion of reasoning, I’d be careful how I step here, because it’s been found these things probably work so well because the human brain is also a biological real-time prediction machine and “just” guessing too: https://www.scientificamerican.com/article/the-brain-guesses...
This language embodies the anthropomorphic assumptions that the author is attacking.
We are in a Cambrian Explosion on the software side and hardware hasn’t yet reacted to it. There’s a few years of mad discovery in front of us.
People have different impressions as to the shape of the curve that’s going up and right, but only a fool would not stop and carefully take what is happening.
Making your own "internal family system" of AI's is a making this exponential (and frightening), like an ensemble on top of the ensemble, with specific "mindsets", that with shared memory can build and do stuff continuously. Found this from a comp sci professor on Tiktok so be warned: https://www.tiktok.com/@lizthedeveloper/video/72835773820264...
I remember a couple of comments here on HN when the hype began about how some dude thought he had figured out how to actually make an AGI - can't find it now, but it was something about having multiple ai's with different personalities discoursing with a shared memory - and now it seems to be happening.
This coupled with access to linux containers that can be spawned on demand, we are in for a wild ride!
That's a big assumption to make. You can't assume that the rate of improvement will stay the same, especially over a period of 2 decades, which is a very long time. Every advance in technology hits diminishing returns at some point.
Technological progress seems rather accelerated than diminishing to me.
Computers are a great example: They have been getting more capable exponentially over the last decades.
In terms of performance (memory, speed, bandwidth) and in terms of impact. First we had calculators, then we had desktop applications, then the Internet and now we have AI.
And AI will help us get to the next stage even faster.
I simply don't see it a being the same today. The obvious element of scaling or techniques that imply a useful overlap isn't there. Whereas before researchers brought together excellent and groundbreaking performance on different benchmarks and areas together as they worked on GPT-3, since 2020, except instruction following, less has been predictable.
Multi modal could change everything (things like the ScienceQA paper suggest so), but also, it might not shift benchmarks. It's just not so clear that the future is as predictable or will be faster than the last few years. I do have my own beliefs similar to Yann Lecun about what architecture or rather infrastructure makes most sense intuitively going forward, and there's not really the openness we used to have from top labs to know if they are going these ways, or not. So you are absolutely right that it's hard to imagine where we will be in 20 years, but in a strange way, because it is much less clear than in 2020 where we will be in 3 years time onwards, I would say it is much less guaranteed progress than it is felt by many...
The way that LLMs and humans "think" is inherently different. Giving an LLM a test designed for humans is akin to giving a camera a 'drawing test.'
A camera can make a better narrow final output than a human, but it cannot do the subordinate tasks that a human illustrator could, like changing shadings, line width, etc.
An LLM can answer really well on tests, but it often fails at subordinate tasks like 'applying symbolic reasoning to unfamiliar situations.'
Eventually the thinking styles may converge in a way that makes the LLMs practically more capable than humans on those subordinate tasks, but we are not there yet.
AI is getting subjectively better, and we need better tests to figure out if this improvement is objectively significant or not.
OpenAI is reportedly losing 4 cents per query. With a thousandfold increase in model size, and assuming linear scale in cost, that's a problem. Training time is going to go up too. Moore's law isn't going to help any more. Algorithmic improvements may help...if any significant ones can be found.
Training a model on more data improves generalization not memorization.
To store more information in the same number of parameters requires the commonality between examples to be encoded.
In contrast, the less data trained on, especially if repeated, lets the network learn to provide good answers for that limited set without generalizing. I.e. memorizing.
——
It’s the same as with people. The more variations people see of something, the more likely they intuit the underlying pattern.
The fewer examples, the more likely they just pattern match.
If it had perfect recall I would be so thrilled.
And just because it's memorized the data--as all intelligences would need to do to spit data out--doesn't mean it can't still do useful operations on the data, or explain it in different words, or whatever a human might do with it.
https://chat.openai.com/share/29d695e6-7f23-4f03-b2be-29b7c9...
Do you (or anyone) know of any products that allow for iterating on the generated output through further chatting with the ai? What I mean, is that each subsequent prompt here either generated a new whole output, or new chunks to add to the output. Ideally, whether generating code or prose, I’d want to keep prompting about the generated output and the AI further modifies the existing output until it’s refined to the degree I want it.
Or is that effectively what Copilot/cursor do and I’m just a bad operator?
So you were ignorant two years ago, GitHub Copilot was already available to users back then. The only new big thing the past two years was GPT-4, and nothing suggest anything similar will come the next two years. There are no big new things on the horizon, we knew for quite a while that GPT-4 was coming, but there isn't anything like that this time.
But when Copilot came out, I was indeed ignorant! I remember when a friend showed it to me for the first time. I was like "Yeah, it outputs almost correct boilerplate code for you. But thankfully my coding is so that I don't have to write boilerplate". I didn't expect it to be able to write fully functional tools and understand them well enough to actually write pretty nice code!
Regarding "there isn't anything like that this time." : Quite the opposite! We have not figured out where using larger models and throwing more data at them will level off! This could go on for quite a while. With FSD 12, Tesla is already testing self driving with a single large neural net, without any glue code. I am super curious how that will turn out.
The whole thing is just starting.
Other breakthroughs in graph machine learning https://towardsdatascience.com/graph-ml-in-2023-the-state-of...
I agree completely with you on this.
In defence of the executives however is that some businesses will be seriously affected. Call centres and plagiarism scanner have already been affected, but it’s unclear which industries will be affected too. Maybe the probability is low, but the impact could be very high. In think this reasoning is driving the executives.
I’ve said it before, but as someone to whom “AI” means something more than making API calls to some SAAS, I look forward to the day they hire me at $300/hour to replace their “AI strategy” with something that can be run locally off of a consumer-grade GPU or cheaper.
It started a few years back and it is now really inflamed with LLM, because of the consumer level hype and general media reporting about it.
You can perceive that by the multiple AI startups capturing millions in VC capital for absolutely bogus value proposition. Bizarre!
While I agree with you in general, I don't think this bit is particularly fair. I'd say we know the limitations, and we also know that using LLMs might bring some advantage, and the companies that are able to use it properly will have a better position, so it makes sense to at least investigate the options.
This only appears so because we here have some insight into the domain. But there have always been hype cycles. We just didn't notice them so readily.
The speed with which this happens makes me suspect there is a hidden "generic hype army" that was already in place, presumably hyping the last thing, and ready to jump on this thing.
In Capitalism, you grow or you die and sometimes you need to bullshit people about growth potential to buy yourself time
At one point, they showed some old footage which featured a montage of daily life in a small Mississippi town. You'd see people shopping for groceries, going on walks, etc. Some would stop and wave at the camera.
In the documentary, they noted that this footage exists because at the time, they'd show it on screen during intermission at movie theaters. Film was still in its infancy in that time, and was so novel that people loved seeing themselves and other people on the big screen. It was an interesting use of a new technology, and today it's easy to understand why it died out. Of course, it likely wasn't obvious at the time.
I say all that because I don't think we can know at this point what AI is capable of, and how we want to use it, but we should expect to see lots of failure while we figure it out. Over the next decade there's undoubtedly going to be countless ventures similar to the "show the townspeople on the movie screen" idea, blinded by the novelty of technological change. But failed ventures have no relevance to the overall impact or worth of the technology itself.
I think it's probably more sociological than technical. People love to see themselves and their friends/family. My work has screens that show photos of events and it always causes a bit of a stir ("Did you see X's photo from the summer picnic?") Yearbooks are perennially popular and there's a whole slew of social media.
However, for this to be "fun", there must be a decent chance that most people in the audience know a few people in a few of the pictures. I can't imagine this working well in a big city, for example, or a rural theatre that draws from a huge area.
The custom of showing film consisting of footage of the general public in movie theaters.
LLMs seem to use little or no abstract reasoning (is-a) or hierarchical perception (has-a), as humans do -- both of which are grounded in semantic abstraction. Instead, LLMs can memorize a brute force explosion in finite state machines (interconnected with Word2Vec-like associations) and then traverse those machines and associations as some kind of mashup, akin to a coherent abstract concept. Then as LLMs get bigger and bigger, they just memorize more and more mashup clusters of FSMs augmented with associations.
Of course, that's not how a human learns, or reasons. It seems likely that synthetic cognition of this kind will fail to enable various kinds of reasoning that humans perceive as essential and normal (like common sense based on abstraction, or physically-grounded perception, or goal-based or counterfactual reasoning, much less insight into the thought processes / perceptions of other sentient beings). Even as ever-larger LLMs "know more" by memorizing ever more FSMs, I suspect they'll continue to surprise us with persistent cognitive and perceptual deficits that would never arise in organic beings that do use abstract reasoning and physically grounded perception.
That's actually the closest to a working definition of what a concept is. The discussion about language representation has little bearing on humans or intelligence, because it's not how we learn and use language. Similarly, the more people - be it armchair or diploma-carrying philosophers - try to find the essence of a meaning of some word, the more they fail, because it seems that meaning of any concept is defined entirely through associations with other concepts and some remembered experiences. Which again seems pretty similar to how LLMs encode information through associations in high-dimensional spaces.
It _is_ correct to say that an LLM is not ready to be a medical doctor, even if it can pass the test.
But I think a better conclusion is that test scores don’t help us understand LLM capabilities like we think they do.
Using a human test for an LLM is like measuring a car’s “muscles” and calling it horsepower. They’re just different.
But the AI hype is justified, even if we struggle to measure it.
That's why I'm hyped. If it's that good for me, and it's generalizable, then it's going to rock the world.
I am currently transliterating a language PDF into a formatted lexicon, I wouldn't even be able to do this without co-pilot, it has turned this seemingly impossibly arduous task into a pleasurable one.
One is just to wow factor. It will be short lived. A bit like VR, which is awesome when you first try it, but it wears out quickly. Here, you can have a bot write convincing stories and generate nice looking images, which is awesome until you notice that the story doesn't make sense and that the images has many details wrong. This is not just a score, it is something you can see and experience.
And there is also the real thing. People start using GPT for real work. I have used it to document my code for instance, and it works really well, with it I can do a better job than without, and I can do it faster. Many students use it to do their homework, which may not be something you want, but it no less of a real use. Many artists are strongly protesting against generative AI, this in itself is telling, it means it is taken seriously, and at the same time, other artists are making use of it.
It is even use for great effect where you don't notice. Phone cameras are a good example, by enhancing details using AI, they give you much better pictures than what the optics are capable of. Some people don't like that because the picture are "not real", but most enjoy the better perceived quality. Then, there are image classifiers, speech-to-text and OCR, fuzzy searching, content ranking algorithms we love to hate, etc... that all make use of AI.
Note: here AI = machine learning with neural networks, which is what the hype is about. AI is a vague term that can mean just about anything.
They put the test scores front and center in the initial announcement with a huge image showing improvements on AP exams, it was the main thing people talked about during the announcement and the first thing anyone who read anything about gpt-4 sees.
I don't think many who are hyped about these things missed that.
I seriously don't remember hearing these test results being mentioned in any casual conversation, and I heard a lot of casual conversations about AI. The majority of these center around personal experiences ("I asked ChatGPT this and I got that..."), homework is another common topic. When we compare systems, we won't say "this one got a 72 and the other got a 94", but more like "I asked new system to give me a specific piece of code (or cocktail recipe, or anything) and the result is much better". Again, personal experience and anecdotes before scores.
Maybe people in the field hype themselves with score, but not the general public, and probably not the investors either, who will most likely look at the financial performance of the likes of OpenAI instead.
He is of the opinion the current generation transformers architecture is flawed and it will take a new generation of models to get close to the hype.
The tests are used (and, despite their flaws, useful) to compare various facets of model A to model B - however, the validation whether a model is good now comes from users, and that validation really can't be flawed much - if it's helpful (or not) to someone, then it is what it is, the proof of the pudding is in the eating.
> But when a large language model scores well on such tests, it is not clear at all what has been measured. Is it evidence of actual understanding? A mindless statistical trick? Rote repetition?
It is measuring how well it does _at REPLACING HUMANS_. It is hard to believe how the author clearly does not understand this. I don't care how it obtains its results.
GPT-4 is like a hyperspeed entry to mid level dev that has almost no ability to contextualize. Tools built on top of 32k will allow repo ingestion.
This is the worst it will ever be.
It's possible to do well on a test and have no ability to do the thing the job tests for.
GPT-4 scores well on an advanced sommelier exam, but obviously cannot replace a human sommelier, because it does not have a mouth.
Also an aside:
> This is the worse it will ever be.
I hear this a lot and it really bothers me. Just because something is the worst it’ll ever be doesn’t mean it’ll get much better. There could always be a plateau on the horizon.
It’s akin to “just have faith.” A real weird sentiment that I didn’t notice in tech before 2021.
Lots of things usefully correlate with test scores in humans but might not in an AI.
Whenever there's a news or article noting the limits of current LLM tech (especially the GPT class of models from OpenAI), there's always a comment that says something along the lines of "ah did you test it on GPT-4"?
Or if it's clear that it's the limitation of GPT-4, then you have comments along the lines of "what's the prompt?", or "the prompt is poor". Usually, it's someone who hasn't in the past indicated that they understand that prompt engineering is model specific, and the papers' point is to make a more general claim as opposed to a claim on one model.
Can anyone explain this? It's like the mere mention of LLMs being limited in X, Y, Z fashion offends their lifestyle/core beliefs. Or perhaps it's a weird form of astroturfing. To which, I ask, to what end?
Perhaps because whenever there's "a news or article noting the limits of current LLM tech", it's a bit like someone tried to play a modern game on a machine they found in their parents' basement, and the only appropriate response to this is, "have you tried running it on something other than a potato"? This has been happening so often over the past few months that it's the first red flag you check for.
GPT-4 is still qualitatively ahead of all other LLMs, so outside of articles addressing specialized aspects of different model families, the claims are invalid unless they were tested on GPT-4.
(Half the time the problem is that the author used ChatGPT web app and did not even realize there are two models and they've been using the toy one.)
Expect the model to continue to perform like it does today, and then lots of dumb integrations added to it, and you will get a very accurate prediction of how most of new tech hype turns out. Dumb integrations can't add intelligence, but it can add a lot of value, so the rational hype still sees this as a very valuable and exciting thing, but it isn't a complete revolution in its current form.
So my perception is this leads to people who have good luck and perceive LLMs as near AGI because it arrives at a useful answer more often than not, and these people cannot believe there are others who have bad luck and get worthless output from their LLM, like someone at a roulette table exhorting "have you tried betting it all on black? worked for me!"
2. LLMs, in spite of the complaints about the research leaders, are fairly democratic. I have access to several of the best LLMs currently in existence and the ones I can't access haven't been polished for general usage anyway. If you make a claim with a prompt, it's easy for me to verify it
3. I've been linked legitimate ChatGPT prompts where someone gets incorrect data from ChatGPT - my instinct is to help them refine their prompt to get correct data
4. If you make a claim about these cool new tools (not making a claim about what they're good for!) all of these kick in. I want to verify, refine, etc.
Of course some people are on the bandwagon and it is akin to insulting their religion (it is with religious fervor they hold their beliefs!) but at least most folks on hn are just excited and trying to engage
^^ I actually think making this claim is in bad form generally. It's like looking for the existence of aliens on a planet. Absence of evidence is not evidence of absence
If you are trying to make categorical statements about what AI is unable to do, at the very least you should use a state-of-the-art system, which conveniently is easily available for everyone.
It's a weird thing to get hung up on if you ask me.
I agree there has been many attention-grabbing headlines that are due to simple issues like contamination. However, I think AI has already proved its business value far beyond those issues, as anyone using ChatGPT with a code base not present in their dataset can attest.
It seems pretty important to counter that and to debunk any wild claims such as these. To provide context and to educate the world on their shortcomings.
The article is actually fine and pretty balanced, but it is a bit unfortunate that 80% of their examples are not illustrative of current capabilities. At least for me, most of my optimism about the utility of LLM's comes from GPT-4 specifically.
I find the whole hype & anti-hype dynamic so tiresome. Some are over-hyping, others are responding with over-anti-hyping. Somewhere in-between are many reasonable, moderate and caveated opinions, but neither the hypesters or anti-hypesters will listen to these (considering all of them to come from people at the opposite extreme), nor will outside commentators (somehow being unable to categorize things as anything more complicated than this binary).
There is a possible world where AI will be a truly transformative technology in ways we can't possibly understand.
There is a possible world where this tech fizzles out.
So one of the reasons that there is a broad 'hype' dynamic here is because the range of possibilities is broad.
I sit firmly in the first camp though - I believe it's truly a transformative technology, and struggle to see the perspective of the 'anti-hype' crowd.
There are millions of hustlers out there pushing snake oil. The probability that something is the real deal and not snake oil is small. Better to assuming the glass is half empty.
I'm sure that is just a matter of prompt engineering, though.
No, it's built on people using DALLE and Midjourney and ChatGPT.
‘Pre-training on the Test Set Is All You Need‘
GPT-4 is really smart to dig information it has seen before, but please don’t use it for any serious reasoning. Always take the answer with a grain of salt.
You start with everyone knows there's AI hype from tech bros. Then you introduce a PhD or two at institutions with good names. Then they start grumbling about anthropomorphizing and who knows what AI is anyway.
Somehow, if it's long enough, you forget that this kind of has nothing to do with anything. There is no argument. Just imagining other people must believe crazy things and working backwards from there to find something to critique.
Took me a bit to realize it's not even an argument, just parroting "it's a stochastic parrot!" Assumes other people are dunces and genuinely believe it's a minihuman. I can't believe MIT Tech Review is going for this, the only argument here is the tests are flawed if you think they're supposed to show the AI model is literally human.
The hype is based entirely on the fact that I can talk (in text) to a machine and it responds like a human. It might sometimes make up stuff, but so do humans. I therefore don't consider that a significant downside, or problem. In the end chatgpt is still ... a baby.
The hype builds around the fact that I can run a language model that fits into my graphics cards and responds at faster-than-typing speed, which is sufficient.
The hype builds around the fact that it can create and govern whole text based games for me, if I just properly ask it to do so.
The hype builds around the fact that I can have this everywhere with me, all day long, whenever I want. It never grows tired, it never stops answering, it never scoffs at me, it never hates me, it never tells me that I'm stupid, it never tells me that I'm not capable of doing something.
It always teaches me, always offers me more to learn, it always is willingly helping me, it never intentionally tries to hide the fact that it doesn't know something and never intentionally tries to impress me just to get something from me.
Can it get things wrong? Sure! Happens! Happens to everyone. Me, you, your neighbour, parents, teachers, plumbers.
Not a single minute did I, or dozens of millions of others, give a single flying fuck about test scores.
I wasn't sure that the phenomena they discussed was as relevant to the question of whether AI is overhyped as they made it out to be, but I did think a lot of questions about the meaning of the performances were important.
What's interesting to me is you could flip this all on its head and, instead of asking "what can we infer about the machine processes these test scores are measuring?", we could ask "what does this imply about the human processes these test scores are measuring?"
A lot of these test are well-validated but overinterpreted I think, and leaned on too heavily to make inferences about people. If a machine can pass a test, for instance, what does it say about the test as used in people? Should we be putting as much weight on them as we do?
I'm not arguing these tests are useless or something, just that maybe we read into them too much to begin with.
Yes, but the models we're talking about have been trained specifically on the task of "complete arbitrary textual input in a way that makes sense to humans", for arbitrary textual input, and then further tuned for "complete it as if you were a person having conversation with a human", again for arbitrary text input - and trained until they could do so convincingly.
(Or, you could say that with instruct fine-tuning, they were further trained to behave as if they were an AI chatbot - the kind of AI people know from sci-fi. Fake it 'till you make it, via backpropagation.)
In short, they've been trained on an open-ended, general task of communicating with humans using plain text. That's very different to typical ML models which are tasked to predict some very specific data in a specialized domain. It's like comparing a Python interpreter to Notepad - both are just regular software, but there's a meaningful difference in capabilities.
As for seeing glimpses of understanding in SOTA LLMs - this makes sense under the compression argument: understanding is lossy compression of observations, and this is what the training process is trying to force to happen, squeezing more and more knowledge into a fixed set of model weights.
Why I think AI is not the appropriate term is that if it were AI, the AI would have already figured everything out for us (or for itself). LLM can only chain text, it does not really understand the content of the text, and can't come up with new novel solutions (or if it accidentally does, it's due to hallucination), this can be easily confirmed by giving current LLMs some simple puzzles, math problems and so on.. Image models have similar issues.
https://en.wikipedia.org/wiki/AI_effect
Just because you don't like how poorly the term AI is defined, doesn't mean it is the wrong term.
AI can never be well defined because the word intelligence itself is not well defined.
"25% of the potential target audience dislikes AI and do not have their opinion positively represented in the media they consume. The potential is unsaturated. Maximum saturation estimated at 15 articles per week."
A bit more serious: AI hasn't even scratched the surface. Once we apply LLMs to speech synth and improve the visual generators by just a tiny bit, to fix faces, we can basically tell the AI to "create the best romantic comedy ever made".
"Oh, and repeat 1000 times, please".
The ones who have to dismantle the hype are the proper technologies such as Yann LeCun and Grady Booch who know exactly what they are talking about.
“People have been giving human intelligence tests—IQ tests and so on—to machines since the very beginning of AI,” says Melanie Mitchell, an artificial-intelligence researcher at the Santa Fe Institute in New Mexico. “The issue throughout has been what it means when you test a machine like this. It doesn’t mean the same thing that it means for a human.”
It's not an apples/apples comparison. The nature of the capability profile of a human vs. any known machine is radically different. Machines are intentionally designed to have extreme peaks of performance in narrow areas. Present-generation AI might be wider in its capabilities than what we've previously built, but it's still rather narrow as you quickly discover if you start trying to use it on real tasks.
I suppose in the context of this article “AI” means statistical language models.