> This entire piece is based on one massive, unsupported assertion, which is that LLM progress will cease.
Which is countered by...the assertion that it won't?
LLMs won't get intelligent. That's a fact based on their MO. They are sequence completion engines. They can be fine tuned to specific tasks, but at their core, they remain stochastic parrots.
> I want to know only one thing, which is what gives him the confidence necessary to say that.
I want to know only one thing, what gives the confidence to say otherwise?
>Which is countered by...the assertion that it won't?
No it's countered by principled restraint in not making an affirmative claim one way or the other.
I've heard this referred to as the overconfident pessimism problem. Which is that normal, well founded scientific discipline and evidence-based restraint go out of the window when people declare, without evidence that they know certain advances won't happen.
Because people get mentally trapped into this framing of either have to declare that it will happen or that it won't, seeming to forget that you can just adopt the position of modesty and say the dust hasn't yet settled.
AI has been around the corner since the 1950s, this is the historical evidence for the pessimistic stance against over optimistic predictions.
LLMs are a huge stride forward, but AI does not progress like Moore's law. LLM have revealed a new wall. Combining multi agents is not working out as hoped.
Perhaps without intending to, you've cited a pretty appropriate example of overconfident pessimism. Philosopher Hubert Dreyfus is most responsible for this portrayal of AI research in the '50s and '60s. He made a career of insisting that advances in AI would never come to pass, famously predicting that computers couldn't become good at chess because it required "insight", and routinely listing off what he believed were uniquely human qualities that couldn't be embodied in computers, always in the form of underdefined terms such as intuition, insight, and other such terms.
Many of the things AI does now are exactly the type of things that doomsayers explicitly predicted would never happen, because they extrapolated from limited progress in the short term to absolute declarations over the infinite timeline of the future.
There's a difference between the outer limits of theoretical possibility on the one hand, and instant results over the span of a couple new cycles, and it's unfortunate that these get conflated.
This is exactly the point. The pessimists get the pedantic thrill of pointing out that, 60 years ago, some proponents were overconfident. But they neglect to notice the larger picture, which is one of extraordinary progress. They'll be sitting in the passenger seat of a self-driving car, arranging their travel itinerary with a chatbot fluent in English and 60 other languages, and smugly commenting on HackerNews about how "AI pessimists got it right in the 60s."
Lots of progress in some areas, very little in others. Neither the pessimists nor the optimists got it right.
(Btw., a really hardened pessimist might even say your example is mostly things that were doable when there were not many computers around at all... taxi driver to drive you, a secretary to book a flight, meet in a club to discuss things, ...)
To me, if artificial intelligence means anything, it means automating mundane tasks -- most of which humans can accomplish already. If someone tells me they're an AI pessimist because they think automating tasks like "converse intelligently about nearly any subject at length" and "drive my car anywhere while I sleep in the back" isn't an impressive AI achievement, then I think our disagreement pertains to our ambitions for the field, and lies outside the realm of the technical.
Right, I believe this is the more normal framing. I think Hubert Dreyfus spent his life constantly retelling the story of the 50s and 60s with a pessimistic narrative, and that is what made it stick, to the extent that it has. But it is a bizarre framing that, even if it once looked like a guiding light on how to set AI expectations, I think talking about the 50s and 60s over and over is not a helpful way of engaging with what has unfolded over the past 2-3 years.
1. AI has not been "around the corner since the 1950s," and 2. if you think there hasn't been forward progress in AI since the 1950s, you have no valid opinions on this subject.
I think that depends on what "it" refers to. If I claim that "it" is the end of the world, and that it will happen no later than 2024, I suspect that most scientifically minded folks would have no problem considering my claim as having a low probability of coming true.
First of all because so many predictions of the end of the world have been made and we tend to have a hunch about the kind of person who makes them. Which is stereotyping, sure, but at least it's a heuristic, not a straight-up abandonment of evidence-based thinking.
I agree there's uncertainty about the future of AI development, but it's true that we have no idea how to create AI, right now, so the uncertainty is about whether it will happen, not whether it won't. If that makes sense.
The evidence for this, albeit empirical, is the history of AI development itself.
AI doesn't show continuous development over a long period of time. It always developed in steps. A new architecture or method is discovered and able to solve some previously hard or unsolveable problems.
Then this solution slowly develops in capability, mostly based on better and cheaper hardware, while it's quality plateaus.
It may be that LLMs will not follow that pattern. I don't think so, for reasons outlined above. But until it can be shown that they don't, that this really is the long-sought-after AI architecture that just gets better and better over time, I don't think that a healthy dose of pessimism is unwarranted based on history.
> LLMs won't get intelligent. That's a fact based on their MO.
I kind of agree. However, I see a real possibility that in the near future LLM behaviour would be practically indistinguishable from intelligent/sentient behavior. And at that point we (or at least I) are facing some really interesting/difficult questions, namely how do you know an intelligent looking thing actually is intelligent (or sentient). How do you prove me you/LLM are/aren't a philosophical zombie?
How we are supposed to treat very much intelligent/sentient looking things when we are not sure if they are sentient/intelligent or not? Let's face it, lots of people are dumb as rock (too often very much me included). Why we should be able to treat something badly just because we think we know they can't be intelligent, even if they walk , look and quack like intelligent duck?
I personally have started to think that the behavior of humans should be judged by the behaviour, not the target. If you want to behave like an asshole towards a teddy bear, then you most likely are an asshole.
Quite easy to happen if like many of us you were taugh British English, and then you remember that you're on a US forum, and everybody around you uses the US spelling for things (and you get to read the US variants all the time in other comments).
It seems to me that the real question here is what is true human intelligence. Ai has made it plain to see, by being able to replicate it so convincingly, that much of what we have considered intelligence has been pattern matching or acting as complex parrots.
There is much more to the abilities of human body-mind-emotional-experiential being, but it is only slowly becoming mainstream.
(Edit: Of course there are also many analytical skills that AI cannot match at this point. My point is that we shouldn’t overlook any area of human capacity.)
One salient question in this is: will we reach a level of intelligence where we become beings capable of actual collaboration that doesn’t waste so much effort in conflicts, or one that is capable of living in harmony within its environment?
What capabilities of awareness, trauma work, emotional maturity and self reflection does this require? What resources hidden inside humanity that we have forgotten do we need to wield?
Does AI have something to contribute to this process happening?
> It seems to me that the real question here is what is true human intelligence.
IMHO the main weakness with LLMs is they can’t really reason. They can statistically guess their way to an answer - and they do so surprisingly well I will have to admit - but they can’t really “check” themselves to ensure what they are outputting makes any sense like humans do (most of the time) - hence the hallucinations.
Does it do that because it can check it’s own reasoning? Or is it just doing so because OpenAI programmed it to not show alternative answers if the probability of the current answer being right is significantly higher than the alternatives?
I don't know. I don't think anyone is directly programming GPT-4 to behave in any way, they're just training it to give the responses they want, and it learns. Something inside it seems to be figuring out some way of representing confidence in its own answers, and reacting in the appropriate way, or perhaps it is checking its own reasoning. I don't think anyone really knows at this point.
As the other poster said, they can check themselves but this requires an iterative process where the output is fed back in as input. Think of LLMs as the output of a human's stream of consciousness: it is intelligent, but has a high chance of being riddled with errors. That's why we iterate on our first thoughts to refine them.
I'm getting suspicious that there is a bit of a blind spot in understanding the world and the usefulness of what we call intelligence, as Noval Yuah Harari says, intelligence is overrated. Look at what we've done to the planet and our environment we have fucked it properly, yet we consider ourselves to be intelligent?
Could it be that intelligence is overrated and discovery of new ideas / thing is underrated? Our egos tell us it's intelligence that makes us special and creative and awesome but maybe most of the special stuff is already there for us to find and we conflate discovery with extrapolation. Maybe knowledge and experience are the "important bits" of intellect.
Example: Einstein didn't really invent anything, he discovered things about the world that blew our mind and changes our lives. Yes he was a great thinker and a courageous soul to go against the grain and he had the balls to be open minded enough to discover new things. We obviously believe Einstein to be intelligent but was he just a great explorer ?
I have a similar attitude towards technological progress, yes we've done amazing things but fundamentally the air we breathe, the water we drink and the beauty we are subjected too when looking at a sunset are taken for granted while we stare at our phones.
What we've done to the planet is perhaps less a consequence intelligence, more a consequence of multi-polar traps. Though it's in our collective self-interest to protect the planet, it's in our individual self interest to ignore the problem / prepare so that our own children can "weather the storm".
It kinda depends on how much you care about people across the world and future generations.
But yeah, if humans were more intelligent, we probably would have sorted all this out by coming up with better coordination mechanisms, and by overcoming our tribal tendencies more effectively.
But, if we were more intelligent, we might have raped the earth much faster and much harder too?
I see this too often, more intelligence = positive outcomes, but no, some of the smartest people ever put their intellect towards stupid causes, such as oil exploration and AI to capture peoples attention.
>One salient question in this is: will we reach a level of intelligence where we become beings capable of actual collaboration that doesn’t waste so much effort in conflicts, or one that is capable of living in harmony within its environment?
That's more about ethics and an wish for moral behavior and conflict aversion, than about intelligence.
Intelligence (human and AI) could just as well opt for conflict and evil, if this helps it get the upper hand for its own private goals and interests.
Simply put, the interests of the collective, are not necessarily the interests of the individual intelligence.
(Even assuming there was a single, easy to agree upon, "interest of the collective" for most problems).
>Not sure how being able to reason about good behaviour in an effective manner that's collectively beneficial isn't in the domain of intelligence.
That's neither here nor there.
Having the intelligence "to reason about good behaviour in an effective manner that's collectively beneficial" doesn't mean you're constrained to reason and act only on that, and not also able to reason and act on behavior which is beneficial to you to the detriment of others and the collective.
And it's pefectly intelligent to follow the latter if you can get away with it, and if the benefit for you is more than your share of the collective benefit alternatives would be.
>We have moral philosophy as an academic discipline, after all.
And how has that been working out for us?
(Not to mention, keyword: academic).
>Human brains develop in interrelation. Much, if not all of our intelligence gets developed in relation to other humans and beings.
Yes, and a lot of it is devoted to duping and getting the upper hand of those other humans and beings. So?
>LLMs won't get intelligent. That's a fact based on their MO. They are sequence completion engines. They can be fine tuned to specific tasks, but at their core, they remain stochastic parrots.
This is absolutely wrong. There is nothing about their MO that stops them from being intelligent. Suppose I build a human LLM as follows:
A random human expert is picked and he is shown the current context window. He is given 1 week to deliberate and then may choose the next word/token/character.
Then you hook this human LLM into an auto-GPT style loop.
There is no reason it couldn't operate with high intelligence on text data.
Not also that LLMs are not really about language at all anymore, the architectures can be used on any sequence data.
Right now we are compute limited. If compute was 100x cheaper we could have GPT-6, bring 100x bigger, we could have really large and complex agents using GPT-4 power models, or we could train on tupled text-video data of subtitles videos. Given the world model LLMs manage to learn out of text data, I am 100% certain that a sufficiently large transformer can learn a decent world model from text-video data. Then our agents could also have a good physical understanding.
Humans will never be intelligent. They're optimized for producing offspring, not reasoning. Humans may appear to be intelligent from a distance, but talk to one for any length of time and you'll find they make basic errors of reasoning that no truly thinking being would fall for. /s
Take out the /s tag and you are right on the money. Humans can not be trusted with anything because they are trivially fallible. Humans are terribly stupid, destroy their own societies and refuse to see reason. They also hallucinate when their destructive tendencies start catching up to them.
If the most intelligent machines ever observed in the universe do not count as "intelligent," then we have a semantic, and not a substantive difference of opinion.
Geoffrey Hinton, Andrew Ng, and quite a few other top AI researchers believe that current LLMs (and incoming waves of multimodal LFMs) learn world models; they are not simply 'stochastic parrots'.
If one feeds GPT-4 a novel problem that does not require multi-step reasoning or very high precision to solve, it can often solve it.
Anyone who has worked a bit with a top LLM thinks that they learn world models. Otherwise, what they are doing would be impossible. I've used them for things that are definitely not on the web, because they are brand new research. They are definitely able to apply what they've learnt in novel ways.
What really resonated with me is the following observation from a fellow HNer (I forgot who):
In many cases, we humans have structured our language such that it encapsulates reality very closely. For these cases, when an LLM learns the language it will by construction appear to have a model of the world. Because we humans already spent thousands of years and billions of actually intelligent minds building the language to be the world model.
But in a sense when YOU learned language YOU also learned a world model. For instance when your teacher explains to you the difference between the tenses (had, have, will have) you realize that time is a thing that you need to think about. Even if you already had some sense of this, you now have it made explicit.
Why should we say the LLM hasn't learned a world model when it's done what a kid has done, and everyone agrees the kid understands things?
From what I see, there are some things it hasn't learned correctly. Notably with limbs, it doesn't know how fingers and elbows work, for some reason. But it does know something about what they should look like, and so we get these hilarious images. But I also don't see why it shouldn't overcome this eventually, since it's come pretty far as it is.
The reason why the LLM apparent world model should not be considered to be the same as a human's world model is because of the modality of learning. The world model we learn as we learn a language includes the world model embedded in language. But the human world model includes models embedded in flailing about limbs, the permanence of an object, sounds and smells associated with walking through the world. Now, all those senses and interactions obviously aren't required for a robust world model. But I would be willing to make a large wager that training on more than "valid sequences of words" definitely is required. That's why hallucinations, confident wrongness, and bizarre misunderstandings are endemic to the failings of LLMs. Don't get me wrong. LLMs are a technological breakthrough in AI for language processing. They are extremely useful in themselves. However, they are not and will not become AGI through larger models. Lessons learned from LLMs will transfer to other modes of interaction. I believe multi-modal learning and transfer learning are the most interesting fields in AI right now.
That makes sense, but isn't this a matter of presenting it with more models? Maybe a physical model discovered via video or something like that? Then it will be similar to what babies are trained with, images and sound. Tactile and olfactory would be similar.
By doing this you'd glue the words to sights, sounds, smells, etc.
But it also seems like this is already someone has thought of and is being explored.
You are correct, there is active research on this. And words and pictures are associated in models like stable diffusion. There has been some success combining GANs and LLMs, but it is far from a solved problem. And as the training data gets more complex the required training resources increase too. Currently it's more like a confusing barrier than a happy extension of LLMs.
Do LLMs trained on languages that treat any double (or more) negatives as one have a slightly different world model than those that treat negatives like separate logical elements, like English? I wonder if that'd be one way to demonstrate what you're saying.
This statement on "learning world models" lies between overhyping, nitpicking and wishful thinking. There are many different ways we represent world knowledge, and llms are great in problems that relate with some of them, and horrible at others. For example, they are really bad with anything that has to do with spatial relations, and with logical problems where a graphical approach helps. There are problems that grade school children can easily solve with a graphical schema and the most advanced LLMs struggle with.
You can very easily give "evidence" of gpt4 being anywhere between emerging super-intelligence and a naked emperor depending what you ask it to solve. They do not learn models of the world, they learn models of some class of our models of the world, which are very specific and already very restricted in how they represent the world.
> For example, they are really bad with anything that has to do with spatial relations, and with logical problems where a graphical approach helps
Of course they are, they haven't been trained on anything spatial, they've only been trained on text that only vaguely describes spatial relations. A world model built from an anemic description of the world will be anemic.
If they learn world models, those world models are incredible poor, i.e., there is no consistency of thought in those world models.
In my experience, things outside coding quickly devolve into something more like "technobabble" (and in coding there is always a lot of made-up stuff that doesn't exists in terms of functions etc.).
It's like if a squirrel started playing chess and instead of "holy shit this squirrel can play chess!" most people responded with "But his elo rating sucks"
There are many reasons. Failing at extrapolating exponentials. Uncertain thresholds for how much compute and data each individual task requires. Moravec's paradox, and relatedly people expecting formalizable/scientific problems to be solved first before arts. There are still some non-materialists. And a fairly basic reason: Not following the developments in the field.
I see them more as creative artists who have very good intuition, but are poor logicians. Their world model is not a strict database of consistent facts, it is more like a set of various beliefs, and of course those can be highly contradictory.
That maybe sufficient for advertising, marketing, some shallow story telling etc., it is way too dangerous for anything in the physical sciences, legal, medicine, ...
On their own, yes. But if you have an application where you can check the correctness of what they come up with, you are golden. Which is often the case in the hard sciences.
It's almost like we need our AI's to have two brain parts. A fast one, for intuition, and a slow one, for correctness. ;-)
Unclear to me. The economics might not be so great as you might need (i) expensive people, (ii) there could be a lot to check for correctness, and (iii) checking could involve expensive things beyond people. Net productivity might not go up much then.
For some industries where I understand the cost stacks with lower and higher skilled workers, I'd say it only takes out the "cheap" part and thereby not taking out a large chunk of costs (more like 10% cost out prior to paying for the AI). That is still a lot of cost reduction, but something that also will potentially be relatively quickly be "arbitraged away", i.e., will bleed into lower prices.
My interpretation of the parent post is not that LLMs' output should be checked by humans, or that they are used in domains where physical verification is expensive; no, what they're suggesting is using a secondary non-stochastic AI system/verification solution to check the LLM's results and act as a source of truth.
An example that exists today would be the combination of ChatGPT and Wolfram [1], in which ChatGPT can provide the method and Wolfram can provide the execution. This approach can be used with other systems for other domains, and we've only just started scratching the surface.
Yes, your interpretation is correct. I think the killer app here is mathematical proof. You often need intuition and creativity to come up with a proof, and I expect AI to become really good at that. Checking the proof then is completely reliable, and can be done by machine as well.
Once we have AI's running around with the creativity of artists, and the precision of logicians, ... Well, time to read some Iain M. Banks novels.
> But if you have an application where you can check the correctness of what they come up with, you are golden.
You're glossing over a shocking about of information here. The problems we'd like to use AI for are hard to find correct answers for. If we knew how to do this, we wouldn't need the AI.
Not sure that matters much as they are only for low risk stuff without skilled supervision, so back to advertising, marketing, cheap customer support, etc.
I would love to see examples. In my attempts to get something original on a not that challenging field (finance), with lots of guidance and hand holding on my end, I was getting a very bad version of what would be a consultant's marketing piece in a second rate industry publication. I am still surprised in other respects, e.g. performance in coding but not in terms originality and novel application.
A typical parrot repeats after you said something. A parrot that could predict your words before you said them, and could impersonate you in a phone call, would be quite scary (calling Hollywood, sounds like an interesting move idea). A parrot that could listen to you talking for hours, and then provide you a short summary, would probably also be called intelligent.
Our parrot does not simply repeat - he associates sounds and intent with what we doing.
At night when he is awake (he sleeps in our room in a covered cage) he knows not to vocalize anything more "Dear" when my wife gets up - he says nothing when I do this as he is not bonded to me.
When I sit at my computer and put on my headset he switches to using English words and starts having his own Teams meetings.
When the garage door opens or we walk out he the back door he starts saying Goodbye - Seeya later and then does the sound of the creaky outside gate.
Just to further this, it's not just 'big names' that feel this way. Read this paper from a team at Microsoft Research: https://arxiv.org/abs/2303.12712 . These folks spent months studying properties of GPT-4, that paper is ~150 pages of examples probing the boundaries of the model's world understanding. There is obviously some emergent complexity arising from the training procedure.
That paper makes some pretty strong claims in the abstract that are not all really supported by the body of the paper. For example, there isn't much on the law or medicine claims in the paper.
If something stays in motion and has been so for some time, it's more important to explain why it would not continue rather than the default assumption that it will stop instantaneously. Show me a curve of diminishing returns and I'll believe you. If an object is in motion you'd need to show me that there is deceleration, or there is a wall just up ahead.
But the fact is that the loss goes down predictably with increased compute budget, data and model size (see Chinchilla Scaling Law). We've also seen that decreased loss suddenly results in new capabilities in discontinuous jumps. There is all reason to believe there is still some juice left in this scaling, exactly how far it can be taken is difficult to tell.
> LLMs won't get intelligent. That's a fact based on their MO. They are sequence completion engines.
A system that could perfectly predict what I would do in response to any particular stimuli, as a continuing sequence, would be exactly as intelligent as me.
> They can be fine tuned to specific tasks, but at their core, they remain stochastic parrot
Othello GPT was an attempt at answering this exact question, it's a simplified setup and appears to learn a world model: https://thegradient.pub/othello/
A system that could perfectly predict what I would do in response to any particular stimuli, as a continuing sequence, would be exactly as intelligent as me.
That's certainly interesting but it's not a depiction of a LLM is it ? LLM's are not deterministic, and (perhaps) so are we so two non-deterministic systems can only occasionally align (or so I assume). Intuition says they may get "close enough", whatever that might be, and close enough is good enough in this case but I think you are making a giant assumption to the likes of since we can speed up matter to 1000km/h then IF we sped it up to light speed then ...[something]...
LLMs are deterministic if the temperature parameter is set to 0. Randomness is artificially injected into their outputs otherwise in order to make them more interesting, but they're just a series of math operations.
To elucidate on this: the LLM can be viewed as a function that takes the context as an attachment and produces a probability distribution for the next token over all known tokens.
Most inference samples from that distribution using a composition of sampling rules or such, but there's nothing stopping you from just always taking the most probable token (temperature = 0) and being fully deterministic. The results are quite bland, but it's perfect for extraction tasks.
(note: GPT-4 is not fully deterministic; there's no details on this but the running theory is it is a mixture of experts model and that their expert routing algorithm is not deterministic/is dependent on the resources available)
I'd argue everything about a LLM is artificial, there is no natural process involved is there ? Since its design is to mimic us (at face value, though I don't know how fair of a description this is) then randomness is essential I think.
> but I think you are making a giant assumption to the likes of since we can speed up matter to 1000km/h then IF we sped it up to light speed then ...[something]...
This is an odd comparison.
The point here is that a sequence prediction system can be as intelligent as the system it's predicting unless you invoke woo. That doesn't make llms intelligent but it means the argument that they just predict the next thing isn't enough to say the can't be.
I think this sentence doesn't mean much unless we have a strict definition of what intelligence means.
Just today ChatGPT helped me solve a DNS issue that I would not have been able to solve on my own in one day, let alone an hour. I'd consider it already more intelligent than myself when it comes to DNS.
It's seen more DNS content than you and anybody else have seen in their entire lives, and are able to regurgitate what it read because it has far faster memory access than you did.
A dictionary contain knowledge but no intelligence.
And LLMs are the opposite of dictionary, actually. They suck at storing facts. They excel at extracting patterns from noise and learning them. It's not obvious to me that this isn't intelligence; on the contrary, I feel it's very much a core component of it.
Yes, I have noticed that a lot of extreme AI cynics have been arguing that any and every example of reasoning or thinking that an LLM displays is just some variant of memorisation.
The biggest evidence that LLMs can’t reason is hallucinations. If it could reason it would have rejected fictional generated output that make no sense.
> The biggest evidence that LLMs can’t reason is hallucinations.
If I asked you a question and you had to respond with a stream of consciousness reply, no time to reflect on the question and think about your reply, how inaccurate would your response be? The "hallucinations" aren't a problem with the LLM per se, but how we use them. Papers have shown that feeding the output back into the input, as happens when humans iterate on their own initial thoughts, helps tremendously with accuracy.
Maybe it’s more accurate to say that LLMs lack (self-)awareness. Because when you point out things that make no sense, they do have some limited ability to produce reasoning about that. But I agree that this lack of awareness is a serious and maybe fundamental deficit.
It’s more likely it’s just, once again, generating the most probable answer - and if you shake the magic 8 ball enough you will get the answer you were expecting.
Yeah, but the thing is, this seems exactly what people are doing too, at the boundary of conscious and unconscious, with the "inner voice" being most directly comparable to LLMs. It too generates language that feels like best completion, regardless of whether the output is logically correct or not.
In comparing to human minds, LLMs are better understood as the "inner voice" part, not the mind. From that perspective, it's eerie how similar the two are in success and failure modes alike.
Yes, I'm saying here that peoples' inner voices are hallucinating in very similar fashion; "rejecting fictional generated output that makes no sense" is a process that's consciously observable and involves looping the inner voice on itself.
Find me a dictionary that will turn to the correct page when I say or type "what's a word that means (thing)" and yes, I'd consider it intelligent.
But still, you don't have to agree with me about what intelligence means. But it is important in these discussions to understand that not every participant shares the same definition of the term intelligence.
intelligence to me has to be based off initiative. In that sense, a dog or a cat have more intelligence than GPT.
I actually very much look forward silicon (or other non-biotic material) attaining intelligence, I consider that the only way that Earth civilization can colonize space. But this aint it.
> Just today ChatGPT helped me solve a DNS issue that I would not have been able to solve on my own in one day, let alone an hour. I'd consider it already more intelligent than myself when it comes to DNS.
Would you consider a search engine, or a book, to be as intelligent?
I'm put in mind of the OpenAI DoTA bot that was winning 99% of its games and some people refused to admit that it knew how to play DoTA based on some esoteric interpretation of the word "play".
We're going to see exponential increases in processing power of the best GPU clusters and human brains are a stationary target. And there is precious little evidence that the average human is much more than an LLM. LLMs are already more likely to understand a topic to a high standard than a given human.
They're going to progress and if they aren't intelligent then intelligence is overrated and I'd rather have whatever they have.
We found that extrapolating the performance given a few data points with smaller models is actually very accurate. That's how they determined hyper parameters, by tuning them on multiple smaller scale models and then extrapolating. So far, all those predictions were quite good.
Together with a bigger model, we also need more data to get better performance. If we add video and audio to the text data, we have still a lot more data we can use, so this is also not really a problem.
It would be very unexpected that those scaling laws are suddenly not true anymore for the next order of magnitude in model and data size.
Scaling laws apply to a single model. The best single model right now is supposedly a 8x mixture of experts, so not even really a single model in the purist sense.
I still expect the final solution will be more along the lines of picking the best model(s) from a sea of possible models, switching them in and out as needed, and then automatically reiterating as needed.
> LLMs won't get intelligent. That's a fact based on their MO. They are sequence completion engines. They can be fine tuned to specific tasks, but at their core, they remain stochastic parrots.
> stochastic parrots
I mean, let's be honest, so are enough of the bell curve humanity - so LLM's don't need to be amazing. They need to chain together communication that makes them seem sentient (as now) & then be exposed to smaller data sets with specialized, higher level knowledge. This is how humans are... and the reason some are smarter than others.
Even assuming that is true: LLMs aren't all that exists in AI research and just like LLMs are amazing in terms of language it's possible similar breakthroughs could be made in more abstracted areas that could use LLMs for IO.
If you think ChatGPT is nice, wait for ChatGPT as frontend for another AI that doesn't have to spend a single CPU cycle on language.
The next AI wave hasn't even started. Imagine an LLM the size of GPT-4 but it's trained on nothing but gene sequence completion.
All the models being used in academia are basically toys, none of those guys are running hardware at a scale that can even remotely touch Azure, Meta, etc, and right now there is a massive global shortage of GPU compute that's eventually going to clear up. We know models get A LOT better when they are scaled up and are fed more data, so why wouldn't the same be true for other problems besides text completion?
Frankly, I'm a bit worried about all the rest now that LLMs proved to be so successful. We might exploit them and arrive to a dead end. In the meantime, other potentially crucial developments in AI might get less attention and funding.
It's countered by not making the assertion and not being able to make conclusions. You only need a lack of confidence for that. It's orders of magnitude easier to not have knowledge compared to having it.
Which is countered by...the assertion that it won't?
LLMs won't get intelligent. That's a fact based on their MO. They are sequence completion engines. They can be fine tuned to specific tasks, but at their core, they remain stochastic parrots.
> I want to know only one thing, which is what gives him the confidence necessary to say that.
I want to know only one thing, what gives the confidence to say otherwise?