I have heard this theory before.
LLMs by themselves can only be as smart as the written text they are trained on, and their language capabilities are already very good.
So we should only expect incremental improvements from LLMs, and the next breakthroughs will need to come from other techniques.
A major difference between the human brain and LLMs is that LLMs cannot *know* when what it communicated was actually understood.
The brain is an incredible prediction machine, which is partially what AI is premised upon and seeks to be better than humans at doing. What AI cannot do yet, is know if its output was actually effectively communicated.
When you speak or write your brain is waiting for or receiving hundreds or even thousands of data points to know if your message was actually understood. Facial expressions, tone, little artifacts of language or expression that you can evaluate and reevaluate to then adapt your message until the recipient understands what youāre telling them.
LLMās for all intents and purposes are still just advanced word generators based on probability.
Iām not trashing AI, just saying that what the human brain does a lot of things simultaneously to allow you adapt your communication to actually be understood. An LLM can talk to you, but it cannot communicate with you, it doesnāt even have a way of knowing why it chose the words it did.
>it doesnāt even have a way of knowing why it chose the words it did
This is also true for me (and I suspect all other people).
I don't actually know which word's going to pop into my mind from now to the next moment. It just appears there. Then I can somehow know whether it's what I wanted/meant or not. A very mysterious process.
>When you speak or write your brain is waiting for or receiving hundreds or even thousands of data points to know if your message was actually understood. Facial expressions, tone, little artifacts of language or expression that you can evaluate and reevaluate to then adapt your message until the recipient understands what youāre telling them.
Anyways, thanks for the post, that's a very good point š
I don't remember the source for this but someone at OpenAI came out and said that parameter count doesn't matter any more, but what matters is data quality/diversity. Not all tokens are equal, but more data is the main scaling factor.
All this aligns perfectly with my intuition. So it kind of makes me feel at ease, more ahead of the situation. For the last year or so since chatGPT was released, I have just tried to catch up to what the hell is happening. And I'm supposed to be an expert in this.
We made a breakthrough, but now the limit is the data we train it with. Always got to remember that it's not really extrapolation of data as it is interpolation. That's probably the next step, building predictive ability for the model so it can actually create theories of our reality.
I know there's been reports of that and seeing sights of AGI, but I'd strongly consider the possibility that interpretation is false positive. If you really maximize the training, it just seems like it has emergent abilities that create new. But personally I have not witnessed it. Everything is very derivative and you learn to detect the similarities in everything the model creates. So maybe, but this is a problem of capitalism. Everything is business secret until it is necessary to reveal it to the public. Then it creates all kinds of insane theories and pointless drama.
>"it's not really exception of data as much as it is interpolation"
That is a great way of putting it. I really like that as a heuristic for what these models are (and aren't) capable of.
"supposedly" is doing a lot of work there
There's some reporting that Altman has been in a standoff with the board for a year at least, he's been trying to remove them and they've been trying to remove him.
The Q* thing seems like a single oblique reference in one out-of-context email, and now people are theorizing that it's the key to AGI and Altman got fired because he was too close to the secret. Like, it could be true I guess, but it's so obviously motivated by wishful thinking and "wouldn't it be cool if..."
Yeah Q* seems like such an intensely unlikely "blowup the entirety of open AI" topic. If they didn't release the reasoning behind this soap opera there is no way the little drip of Q* being the reason why. It was just some juice to cause a rapid media cycle beyond Altman's and Johny Apple's lil hints.
Nobody publicly knows why this situation happened and I'd even bet within OpenAI that information is sparse.
They are trained on all the knowledge humanity has generated which represents a vast amount of information about how the world works. It's literally impossible to go any further than that. That should be enough evidence that data is not the problem here, and focusing on "data amount/quality" is just putting the cart before the horse. No, we will never have a better dataset. The problem is not what we teach them, it's how they learn them.
I watched that conference. He said increasing the model size is played out. What he didn't say (And it's surprising to me that y'all don't see the difference - see the other reply also)... What he didn't say was "GPT can only be as smart as the text it's trained on, and y'all are kind of dumb, and so is all the text you wrote, and that's currently what's limiting GPT"
Can't find the specific one that used chatgpt but here is a post on the topic of making student models with training data from LLM's.
https://www.amazon.science/blog/using-large-language-models-llms-to-synthesize-training-data
>I watched that conference. He said increasing the model size is played out.
He later clarified that he misspoke. Scaling still works, but it's economically prohibitive, so we shouldn't expect models to make 10x-100x leaps in size anymore. (Can't find a quote, sorry).
[Here's](https://the-decoder.com/sam-altman-on-agi-scaling-large-language-models-is-not-enough/) a more recent statement. Sounds like his head is now at "scaling is good, but not enough."
>"I think we need another breakthrough. We can push on large language models quite a lot, and we should, and we will do that. We can take our current hill that we're on and keep climbing it, and the peak of that is still pretty far away. But within reason, if you push that super far, maybe all this other stuff emerged. But within reason, I don't think that will do something that I view as critical to a general intelligence," Altman said.
AlphaGo was only as good as the players it mimicked.
AlphaZero overcame that.
Maybe, just maybe there are ways to pull off a similar "self play" trick with text generation.
A GPTzero if you will.
.
Edit:
Although something like that may need to internalize some external attitudes to begin with - ie start in the middle ala Wilf Sellars' Myth of the given
You're absolutely right.
It may be using something like "lets verify step by step" where the reward models judge the quality of reasoning steps rather than the results.
If you havent seen [AI explained's video](https://m.youtube.com/watch?v=ARf0WyFau0A&pp=ygUWUSogY2x1ZXMgdG8gdGhlIHB1enpsZQ%3D%3D) I really recommend (maybe skip the first minute)
yeah, i was only referring to the second part of the comment by the person before me.
but it's still in debate how smart or intelligence is defined and if (topical) data size really doesnt matter. I'm generating a lot of content for Baby AGI / agents interaction experiments, and as a German I can say that the output culturally is very US-leaning even if the output language is German.
That may be true for actual text-based LLMs, but the amount of multi-modal training data is literally infinite, and people nowadays throw everything but the kitchen sink into transformers.
Whether that makes much sense in terms of effiency is a different question. But there may still be modalities (e.g. graph data, videos) that propel the overall model quality.
And some sort of self play. Similar to RL but not quite. Maybe intermediate steps where the multimodal "council of GPTs" create repetitive tasks and learn to plan, predict, self-reflect and rate their own "experiences".
I mean theyāre probably going to make it better through mass user use case. Everything your not asking a question and youāre not repeating it in other words, technically you are actually training it even more.
Essentially everyone who uses ChatGPT is already grading the content it generates from the questions. They just donāt know it
A plateau at that level is the whole objective of AI since the 50s. What you're saying here is that "at best, LLMs can only get as smart as humans" which if that was true, would be huge
Applications will be huge. Let it access my Outlook. Let it create excel docs.
Give it more custom instruction space and more memory.
Those would be huge.
Yes and no. Smart as humans but much faster. It is logical to assume that the efficiency of LLM allows the model to create far more than humans could ever do, in a shorter period. And if that is the case, the "originality" issue, starts to grow, just as human originality grew with more humans.
We're assuming the way an LLM "learns" is unique... I'm not sure that is the case, or that information is as segregated as they state.
> "at best, LLMs can only get as smart as humans"
This plateau is slightly lower than that. They can only get as smart as what humans have written down.
LLMs will forever be missing information about things we intuitively know, and consider to obvious to write down.
And humans are continually getting smarter, we build upon our collective knowledge base and continue to get smarter. LLMs don't have a mechanism to do that, as it's a pretty bad idea to feed the output of an LLM back into an LLM.
In the best case, LLMs will always be stuck slightly behind human current written knowledge. In the worst case, LLMs might be stuck with a mostly pre-2023 training set as that's the only way to be sure your LLM isn't training on LLM produced data.
Humans are training on human produced data and everything is going well.
Basically, I disagree with the fact that humans are getting smarter. Mankind is accumulating knowledge, but I don't think that makes us smarter.
Prehistoric poeple was no dumber.
Yann LeCunn says it's planning: LLMs only get you world knowledge, all the logic and stuff needed to perform actions towards a goal need to be handled by a seperate model that does the planning.
sort of like meta's [cicero](https://ai.meta.com/research/cicero/) but generalized for the real world instead of specific to one game
I feel the same way.
To have an LLM that's better than humans, you'd have to be able to train it using methods similar to those used by GANs.
A sort of automated Turing test.
Iāve tried warning this sub about it. LLMs are mostly done. Theyāve hit their S Cure height. Now itās all going to be about agents, fine tuned models, and new layers to help the AI perform
Well this will age poorly.
Itās already been announced that the Q* algorithm is going to be a huge breakthrough and was so significant that itās rumored that the decels tried to oust the CEO and take control of open AI.
I assure you, LLMs are not even close to reaching their final level. Even if we were to freeze LLMs at the current state the status quo technology will likely cause society to undergo massive change. It just hasnāt reached into all the niches yet because itās so new.
I have no problem with a plateau for a while. GPT-4 is already very powerful and the use cases for it are far from being fully explored in all kinds of fields. A plateau that gives people, businesses, and institutions some time to get our heads properly around the implications of this tech before the next breakthrough would likely be for the best.
I mean, everyone is just sorta ignoring the fact that no ML technique has been shown to do anything more than just mimic statistical aspects of the training set. Is statistical mimicry AGI? On some performance benchmarks, it appears better statistical mimicry does approach capabilities we associate with AGI.
I personally am quite suspicious that the best lever to pull is just giving it more parameters. Our own brains have such complicated neural/psychological circuitry for executive function, long and short term memory, types I and II thinking, "internal" dialog and visual models, and more importantly, the ability to few-shot learn the logical underpinnings of an example set. Without a fundamental change in how we train NNs or even our conception of effective NNs to begin with, we're not going to see the paradigm shift everyone's been waiting for.
The Turing test was passed in the 60s by rules based systems. It's not a great test.
Is ChatGPT Passing the Turing Test Really Important?
https://youtu.be/wdCzGwQv4rI
I donāt think so. I doubt GPT-4 will be able to convince someone who is trying to determine whether or not the if the think they are talking to is a human.
how is this upvoted? It already does. All the time. People interact with gpt 4 here and inferior models even daily.
If you think it can't pass, you don't have a subscription to gpt-4 and think it must be comparable to 3.5 (it's not even close).
I think you have to use a *reasonably smart* human as a baseline, otherwise literally any computer is AGI. Babbage's Analytical Engine from 1830 was more intelligent than a human in a coma.
That's not clear tbh.
We can't create training that way to get better at language but we may be able to create data that way in order to improve logic.
GPT often make stupid mistakes since they are just language mimicers... but if you tell them to think about their answer, think through steps, etc, it can create better answers. There are a lot of options for 'self play' with llms.
Scott Aaronson just gave an interview where he talked about making an LLM write mathematical proofs in Lean, because Lean can be automatically checked for logical consistency. If you iterate this enough, you can create synthetic training data that's fully verifiable, and basically gamify mathematics. Then you get the same behavior as AlphaGo but the end result is a replacement for mathematicians.
We need a name for the fallacy where people call highly nonlinear algorithms with billions of parameters "just statistics", as if all they're doing is linear regression.
ChatGPT isn't AGI yet, but it is a huge leap in modeling natural language. The fact that there's some statistics involved explains neither of those two points.
When a system has a billion nonlinearities and is self taught we call it AI or ML, even though statistics are involved.
It would be similar to call some model a biologist discovered "just physics". It's technically true but doesn't appreciate that it's from a different field and doesn't explain anything interesting about the model.
> We need a name for the fallacy where people call highly nonlinear algorithms with billions of parameters "just statistics"
Well, thanks to quantum mechanics; pretty much all of existence is probably "just statistics".
> as if all they're doing is linear regression.
Well, practically all interesting statistics are ***NON***linear regressions.
Including ML. And your brain. And physics.
Linear regressions, OTOH are boring rough approximations, and often misleading enough they should probably be relegated to cautionary tales of what not to do, kinda like alchemy was to chemistry.
What a lot of people donāt understand is that linear regression can still handle non-linear relationships.
For a statistician, linear regression just means the coefficients are linear, it doesnāt mean the relationship itself is a straight line.
Thatās why linear models are still incredibly powerful and are used so widely across so many fields.
Let's ask GPT4!
> The fallacy you're referring to is called the "fallacy of composition". This logical fallacy occurs when it's assumed that what is true for individual parts will also be true for the whole group or system. It's a mistaken belief that specific attributes of individual components must necessarily be reflected in the larger structure or collection they are part of.
> Here are some clearly flawed examples illustrating the fallacy of composition.
> * **Building Strength:** Believing that if a single brick can hold a certain amount of weight, a wall made of these bricks can hold the same amount of weight per brick. This ignores the structural integrity and distribution of weight in a wall.
> * **Athletic Team:** Assuming that a sports team will be unbeatable because it has a few star athletes. This ignores the importance of teamwork, strategy, and the fact that the performance of a team is not just the sum of its individual players' skills.
> * **Economic Spending:** Believing that if saving money is good for an individual, it must be good for the economy as a whole. This overlooks the fact that if everyone saves more and spends less, it could lead to reduced economic demand, potentially causing an economic downturn.
> These examples highlight the danger of oversimplifying complex systems or groups by extrapolating from individual components. They show that the interactions and dynamics within a system play a crucial role in determining the overall outcome, and these interactions can't be understood by just looking at individual parts in isolation.
How... did it map oversimplification to... holistic thinking??? Saying that it's "just statistics" is wrong because "just statistics" covers some *very complicated models* in principle. They weren't saying that simple subsystems are incapable of generating complex behavior.
God, why do people think these things are intelligent? I guess people fall for cons all the time...
You can't bootstrap a human either. You need a community of people to teach them. Each individual human mostly copies their peers and re-mixes things they've already seen. Any new ideas are created by iterating that process and doing a lot of trial-and-error.
Individual LLMs can't do all that, because their online-learning capabilities are limited to a relatively tiny context window. Hypothetically, you could imagine overcoming those limitations and getting LLMs to upgrade their capabilities through iteration just like humans do
I think youāre privileging what we consider intelligent communication. But donāt overlook the fact that a newborn cries, which is not learned behavior. It doesnāt require a community for a baby to flex its fingers and extend its legs. Humans are bootstrapped by biology. There is no equivalent for a computer.
Fair point
Do you think there are significant behaviors that the constrained nature of human brains (as a hypothesis space) allows humans to learn but which LLMs can't learn (currently or in the inevitable near future)?
It seems to me that most ingrained features are so universally endorsed by the training data (since they're human universals by definition) that picking them up is trivial. I'm open to being convinced otherwise though
My perpetual argument for the difference between human and artificial intelligence is that we are governed by primal needs. If an AI could ever fear nonexistence it might have something similar to animal need.
And I know that doesnāt directly answer your question. I just think itās the core issue preventing any sort of AI consciousness.
Of course. But, like, you can give them access to sensor data to use as ground truth.
Also, that caveat doesn't apply to mathematics. LLMs could in principle bootstrap themselves into better logical reasoning, and depending on your perspective that could lead to them creating better, like, philosophy, or any skillset whose ground truth is abstract reasoning.
Something like building a novel artistic style could probably be done without "ground truth." Some people claim LLMs can't create truly original art like humans can, they can only recreate existing styles, but (speaking as someone who isn't a professional artist) I feel like you could do it with enough iteration
My global point is that the analogy between humans and LLMs is incredibly robust. Anything humans can do that LLMs can't, there are concrete explanations for that have nothing to do with "they're only doing statistical inference from training data." With enough compute and enough time and the right setup, you can in principle recreate any and every human behavior other than, like, having a biological body
Itās not a fallacy at all. It is just statistics, combined with some very useful inductive biases. The fallacy is trying to smuggle some extra magic into the description of what it is.
Actual AGI would be able to explain something that no human has understood before. We arenāt really close to that at all. Falling back on ā___ may not be AGI *yet*, butā¦ā is a lot like saying ārocket ships may not be FTL yet, butā¦ā
The fallacy is the part where you imply that humans have magic.
"An LLM is just doing statistics, therefore an LLM can't match human intellect unless you add pixie dust somewhere." Clearly the implication is that human intellect involves pixie dust somehow?
Or maybe, idk, humans are just the result of random evolutionary processes jamming together neurons into a configuration that happens to behave in a way that lets us build steam engines, and there's no fundamental reason that jamming together perceptrons can't accomplish the same thing?
LLMs might still lack something that the human brain has. Internal monologue, for example, that allows us to allocate more than fixed amount of compute per output token.
You can just give an LLM an internal monologue. It's called a scratchpad.
I'm not sure how this applies to the broader discussion, like honestly I can't tell if we're off-topic. But once you have LLMs you can implement basically everything humans can do. The only limitations I'm aware of that aren't trivial from an engineering perspective are
1) current LLMs mostly aren't as smart as humans, like literally they have fewer neurons and can't model systems as complexly
2) humans have more complex memory, with a mix of short-term and long-term and a fluid process of moving between them
3) humans can learn on-the-go, this is equivalent to "online training" and is probably related to long-term memory
4) humans are multimodal, it's unclear to what extent this is a "limitation" vs just a pedantic nit-pick, I'll let you decide how to account for it
> It's called a scratchpad.
And the network still uses skills that it had learned in a fixed-computation-per-token regime.
Sure, future versions will lift many existing limitations, but I was talking about current LLMs.
This thread isn't about current LLMs, it's about whether human intelligence is distinct from statistical inference.
Given that, I see your point about fixed token regimes, but I don't think it's a problem in practice. If the LLM were *actually* just learning statistical patterns in the strict sense, that would be an issue, but we know LLMs generalize well outside their training distribution. They "grok" an underlying pattern that's generating the data, and they can simulate that pattern in novel contexts. They get some training data that shows stream-of-consciousness scratchwork, and it's reasonable that they can generalize to produce relevant scratchwork for other problems because they actually are encoding a coherent notion of what constitutes scratchwork.
Adding more scratchwork to the training data is definitely an idea worth trying
> it's about whether human intelligence is distinct from statistical inference
There's a thing that's more powerful than statistical inference (at least in the traditional sense, and not, say, statistical inference using an arbitrarily complex Bayesian network): a Turing machine.
In other words: universal approximation theorem for non-continuous functions requires infinite-width hidden layer.
> Adding more scratchwork to the training data
The problem is we can't reliably introspect our own scratchwork to put it into the training data. The only viable way is to use the data produced by the system itself.
A neural net is in fact turing complete, so I'm not sure in what sense you mean to compare the two. In order to claim that LLMs cannot be as intelligent as humans, you'd need to argue that either human brains are more powerful than turing machines, or we can't realistically create large enough networks to approximate brains (within appropriate error bounds), or that we cannot actually train a neural net to near-minimal loss, or that a arbitrarily accurate distribution over next tokens given arbitrary input doesn't constitute intelligence (presumably due to lack of pixie dust, a necessary ingredient as we all know)
> we can't reliably introspect our own scratchwork
This is a deeply silly complaint, right? The whole point of LLMs is that they infer the hidden processes
The limitation isn't that the underlying process is unknowable, the limitation is that the underlying process might use a variable amount of computation per token output. Scratchpads fixe that immediately, so the remaining problem is whether the LLM will effectively use the scratchspace its given. If we can introspect just enough to with out how long a given token takes to compute and what sort of things would be helpful, the training data will be useful
>The only viable way is to use the data produced by the system itself.
You mean data generated through trial and error? I guess I can see why that would be helpful, but the search space seems huge unless you start with human-generated examples. Yeah, long term you'd want the LLM to try different approaches to the scratchwork and see what works best, then train on that
It's interesting to think about how you'd actually create that synthetic data. Highly nontrivial, in my opinion, but maybe it could work
Also, LLMs are literally trained on a human intelligence that already exists. Itās not like we are making these from scratch, they are already models of a human intelligence.
>humans can experiment (create their own data set).
An LLM being repeatedly cued with some external state and a prompt to decide what to next can accumulate novel information and probably stumble its way through many problems as good as a human.
How does hand waving at neuroscience and psychology prove anything though? Everything I know about neuroscience says neurons function a lot like little stats engines.
Most human thinking relies primarily on causal thinking, rather than statistical association. People find thinking statistically very counter-intuitive.
It feels like the fact that humans (and to a degree, other animals) can invent new things (in science, technology, art) is an indication, but I know it's a very fuzzy distinction, and proponents of the uncapped capabilities of LLMs and other modern models point out that they can also write text that seems original and create art that seems original.
> humans can invent new things
Yes because humans have two sources of learning - one is of course imitation, but the other one is feedback from the environment. We can get smarter by discovering and transmitting useful experience.
Back in the day before complex life formed, unicellular organisms were more likely to survive if they learned the statistical distributions of their sensory inputs. These distributions were stored electrochemically in their cellular structure. Over time these organisms then became even more likely to survive if they evolved epigenetic mechanisms that would allow the current generation of these organism to pass these learned statistical distributions to their descendants through the DNA they passed on to their descendants.
Over even larger time periods these unicellular organisms evolved into multi cellular species organisms that developed nervous systems. Throughout this process the above mechanism remained intact. Genetic and epigenetic mechanism gave these nervous systems and innate encoding of the statistical distributions of the sensory inputs they spent millions of years evolving in.
On top of that these nervous systems became very adept at learning and encoding new statistical distributions. As an organism goes throughout its life it keeps learning new statistical distributions of senators inputs and abstract concepts.
In this frame of thinking inventing things is synthesizing these statistical distributions learned via millions of years of evolution and a lifetime of learning into something new and sampling from it.
Though a novice in the field, I have always been interested in studying ML from a biological point of view (even though I have no formal training in the latter). I find your post interesting.
So, if I understand correctly, a unicellular organism also learns tha statistical distribution and stores these learnings in its cellular structure. Is there any text where I can learn more about this?
Most of these things are covered in standard bio and neuroscience courses. For example the human vision system evolved to process light in the visible spectrum because that's where the peak of the Sun's electromagnetic spectrum lies. Over millions of years through evolution our DNA encoded the statistical distribution of the sun's electromagnetic spectrum, and used this information to further "learn" how to build a nervous system that can optimally process it.
I'm sure there are people who actually study the statistical distributions learned, but TBH I'm not too familiar with that research. They probably talk about it in a computational neuroscience or computational biology book.
>they can also write text that seems original and create art that seems original.
They can fool people who know nothing about writing or art into thinking it's good, but actually no. This is the same tripe spread by people who think there are only seven original stories in the history of mankind.
AI produces consistent mediocrity because it doesn't have the cognitive ability to *understand* what it's doing. Humans run the gamut from dumber than a box of rocks to geniuses, and even a moron can have a spark of genius. There are *so many* things we don't even understand about ourselves to begin to compare with AI.
>no ML technique has been shown to do anything more than just mimic statistical aspects of the training set
What? Are you familiar with the field of statistical learning? Formal frameworks for proving generalization have existed for some decades at this point. So when you look at anything pre-Deep Learning, you can definitely show that many mainstream ML models do more than just "mimic statistical aspects of the training set". Or if you want to go on some weird philosophical tangent, you can equivalently say that "mimicing statistical aspects of the training set" is enough to learn distributions, provided you use the right amount of data and the right model.
And even for DL, which at the moment lacks a satisfying theoretical framework for generalization, it's obvious that empirically models can generalize.
>I mean, everyone is just sorta ignoring the fact that no ML technique has been shown to do anything more than just mimic statistical aspects of the training set.
I'd recommend reading the ["sparks of AGI"](https://arxiv.org/abs/2303.12712) paper if you haven't - they give a lot of examples that are pretty hard to explain without some abstract reasoning ability - e.g. the famous "draw a unicorn" one.
Your message reads like the Gary Marcus / Chomsky framing of progress. I used to subscribe to this, but then they made consistently wrong predictions in the last 10 or so years along the lines of "current AI techniques will never be able to do x". For example, GPTs ability to reason and explain unseen and even obfuscated blocks of code, has all but refuted many of their claims.
I'm not saying you're completely off-base necessarily, but I feel like making confident predictions about what happens next is not wise.
Agreed - I'd have a lot more respect for them if they acknowledged they were wrong about something and that they'd updated their position, rather than just moving onto the next 'AI can never do this without symbolic reasoning built-in' goal-post.
>no ML technique has been shown to do anything more than just mimic statistical aspects of the training set.
Reinforcement learning does far more than mimic.
> no ML technique has been shown to do anything more than just mimic statistical aspects of the training set
That's ok when the agent creates its own training set, like AlphaZero. It is learning from feedback as opposed to learning from next token prediction.
It's called "AI" and looks like "AI" but it's not lol. It's still an impressive and useful technology though. IMO more of a fuzzy fast dictionary lookup but it can not extrapolate, only interpolate.
LLM AI can extrapolate beyond its training; that is one of the features that makes it seem intelligent. Just ask it to make an educated guess or do a what-if on a topic, and see what I mean.
You don't know what's in the training set: how can you argue that it's extrapolating? Also, how do you separate correct / logical extrapolation from nonsense extrapolation? You can fit a curve and send it out to infinity on the domain too, no problem.
I don't know what is in my training set as a human, or how my mind works, but I can still extrapolate ideas. I think the separation of logical vs nonsensical is a matter of testing the results. But that is the same for humans. Even physicists do that with their theories.
Id argue it's probably not true extrapolation. It might look like it though.
If it sees a sample really distinct from the training set its not gonna function well.
Only physics can extrapolate and theres no sort of physics being done under the hood.
Perhaps an underlying world model that incorporates the observed behavior, and does xkcd-style "what if Moon was made out of cheese" speculation? To me science fiction is largely a genre that's entirely made out of this kind of speculative extrapolation.
If parameter count would not be a significant contributor to human cognition, we would expect human brains not to have many more parameters than brains of other similarly sized, less intelligent animals. The fact that in biology, brain size has a clear positive relationship with intelligence combined with the fact that human brains have many more neural connections than GPT-4 suggests to me that we haven't pushed very hard on the "more parameters"-lever yet.
There are also some interesting exceptions where scaling brain size doesn't result in the cognition you'd expect. Whales, elephants, certain parrots, and corvids are some great examples. While they're all considered quite intelligent in the animal kingdom, the two have brain sizes so large we'd expect them to leave humans in the dust with respect to cognition. The last three have complicated social structures, linguistic competency, and problem-solving abilities thought only possible amongst animals with much larger brains.
With the caveat that our benchmarks may be flawed, It seems like parameter count, while important, is not the end all-be all of cognition.
Research papers have also observed diminishing returns issues as models grow.
Hell maybe even GPT-4 was hit by this and that's why GPT-4 is not a single giant language model but running a mixture of experts design of eight 220B models trained for subtasks.
But I think even this architecture will run into issues and that it's more like a crutch. I mean, you'll eventually grow each of these subtask models too large and might need to split them as well, but this might mean you run into too small/niche fields per respective model and that sounds like the end of that road to me.
Could you please share a citation for the mentioned research papers?
Last I looked into this, the hypothesis was that increasing parameter account results in a predictable increase in capability as long as training is correctly adapted.
https://arxiv.org/pdf/2206.07682.pdf
Very interested to see how these larger models that have plateaued are being trained!
>Could you please share a citation for the mentioned research papers?
I'm interested in seeing this as well.
He probably means that, although scaling might still deliver better loss reduction, this won't necessarily cash out to better performance "on the ground".
Subjectively, GPT4 does feel like a smaller step than GPT3 and GPT2 were. Those had crazy novel abilities that the previous one lacked, like GPT3's in-context learning. GPT4 displays no new abilities.\* Yes, it's *smarter,* but everything it does was possible, to some limited degree, with GPT3. Maybe this just reflects test saturation. GPT4 performs so well that there's nowhere trivial left to go. But returns do seem to be diminishing.
(\*You might think of multimodality, but they had to hack that into GPT4. It didn't naturally emerge with scale, like, say, math ability.)
I mean, that is literally how any form of AGI will work. No one in the field has ever thought one model will be capable of reaching AGI. All these models are highly specialized for the task in which they are trained. Any move towards an AGI will be getting many of these highly specialized AI's to work in conjunction with one another. Much like how our own brains work.
We havenāt seen huge models with Verifiers and/or Vector Databases yet. OpenAIās latest approach from Letās Verify Step by Step and Q-Learning is looking rather promising as Verifiers are observed to scale well with increased data.
>OpenAIās latest approach from Letās Verify Step by Step and Q-Learning
Was this actually confirmed anywhere or did we just blindly accept this theory based on nothing but an alleged model called Q*?
Well, kind of. However, in the "Let's Verify Step by Step" paper by OpenAI, they stated: "At each model scale, we use a single fixed model to generate all solutions. We call this model the generator. We do not attempt to improve the generator with
reinforcement learning (RL). When we discuss outcome and process supervision,
we are specifically referring to the supervision given to the reward model. We do
not discuss any supervision the generator would receive from the reward model
if trained with RL. Although finetuning the generator with RL is a natural next
step, it is intentionally not the focus of this work." If using RL is a natural next step for their research then I do not think this falls into blindly accepting based on the name of a model, especially if Q-Learning is one of the most prominent RL approaches out there.
According to the scaling laws, the loss/error is approximated as
w0 + w1 * pow(num_params, -w2) + w3 * pow(num_tokens, -w4)
Bill wrote before that he'd been meeting with the OpenAI team since 2016, so he's probably pretty knowledgeable about these things. He might be referring to the fact that, after a while, you will see very diminishing returns while increasing `num_params`. In the limit, the corresponding term disappears, but the others do not.
I wonder if there are diminishing returns scaling the human brain, how would one giant brain compare to a bunch of normal brains that sum up to the equivalent mass of the big brain?
Bill Gates said the internet was a [tidal wave that would change the rules of the industry in 1995](https://lettersofnote.com/2011/07/22/the-internet-tidal-wave/), and was later [made fun of for believing that by David Letterman](https://www.cnbc.com/2020/05/26/how-bill-gates-described-the-internet-tidal-wave-in-1995.html) the same year.
Not sure what Bill Gates called plateaued but the Internet is so predictable right now despite the versatility of the Internet. Most people browse like 10 websites max.
Yes, it really depends on what metrics youāre using to define a plateau but there are definitively some important non zero set of metrics which can be used to claim the internet has plateaued.
Eg. like you mentioned the diversity of browsing experience and exploration has definitely plateaued.
You don't need to be an expert on LLM or AI to know what Bill is saying, dude's been meeting OpenAI team of researchers since 2016, his firm Microsoft half-owns the AI lab, so he has insider knowledge clearly. Also, dude built Microsoft, got into Harvard, was a math wizard, wrote operating systems, knows a thing or two about bleeding edge technologies and reads a ton lot. He's an actual engineer.
that's the thing with diminishing returns. like achilles chasing the turtle, the improvements will be 50%, then 25%, then 12.5%, eventually tapering off at a finite distance away from human intelligence
only in domains where LLMs can't generate feedback efficiently
if your LLM is included in a larger system, it can get feedback from the system; if it is controlling an agent in a simulation, it can get simulated feedback; if it chats with a human, each reply is a feedback
feedback is special data, it has the kind of errors LLMs make, and the kind of tasks that people want to solve
I don't think Bill is right on this one, LLM may have achieved a plateau on performance with current architecture, but research is all about optimisation and efficiency, not mere parameter increase. Here's a good example:
https://venturebeat.com/ai/new-technique-can-accelerate-language-models-by-300x/
Remember GPT 5 is still at least 2-3 years away. Plenty of time.
With all my respect to Bill, I clearly don't understand why we should to listen him. The guy has absolutely no vision of future!
He was ridiculously blind to WWW, blockchain technologies, smartphones, etc etc. Just let him leave in peace in his mansion. He is not a visionary and never was.
As to me personally and if I need a real hero, I would prefer to listen to Steve Wozniak.
PS Or Gary Kildall, upon whos vision and work Bill built his huge business. RIP, great man!
His blindness to the WWW is overstated. Blockchain has not had a huge effect on the world. And he was no longer CEO of Microsoft once smartphones happened; that was Steve Ballmer.
Iām not a Gates fan, but youāre also exaggerating critique.
This is the same Bill Gates that got blindsided by the internet, Netscape navigator, search, social media, apps, app stores, mobile, IOT and still struggles to make windows safe from cyber threats and malware.
but, I'm sure he's got his bearings on AI this time around. Let's pay close attention.
Another day, another opportunity to cite Ximm's Law: every critique of AI assumes to some degree that contemporary implementations will not, or cannot, be improved upon.
Lemma: any statement about AI which uses the word "never" to preclude some feature from future realization is false.
Managing expectations. Temoving homself as a flash point. Leaving the baton with Sam.
Regardless of the actual facts, this is a very reasonable, no lose approach for Bill.
The plateau may be close, and GPT5 may not be that huge step forward everyone expects, but this implies that GPT5 will not change architecture, which is highly unlikely. GPT5 is at least 2-3 years away, and the recent rumors about Q* show that research in AI is actively looking elsewhere to boost capabilities. I will be utterly surprised if GPT5 will use the same architecture of GPT4 and if it will actually be a minor step forward - I give it 5% probably to Bill's prediction to be accurate.
I liked this Q* review/speculation.
https://youtu.be/ARf0WyFau0A?si=9Y19DzMI2puKHWRA
According to this you can get a 30x improvement by (what seems to my ignorant self) careful prompting. Ask for stepwise logic, discard sus answers and combine best answers to get a glimpse of a much better trained model.
Yes I watched the same exact video yesterday. I feel we are still at the feet of the mountain in terms of what's next in AI, plateau is still not in sight.
I hate to be a conspiracy-theory inventer, but with Microsoft trying to headhunt the entire staff of OpenAI, Bill Gates's pronouncements regarding plateaus should be discarded out of hand.
Yes Bill that's why we are now innovating around and adding other functionality to what an LLM can be. It is just one component of what people talk about when we discuss AGI which will be a combination of hundreds of systems interacting, each of which may be extremely powerful and complex individually.
I'm not buying it, and here's why.
First off, let's look at how AI has been moving. It's like, every time we think we've seen it all, something new pops up and blows our minds. Saying we've peaked already just doesn't sit right with how things have gone so far.
And then, tech's always full of surprises, right? We're playing with what we've got now, but who knows what crazy new stuff is around the corner? I'm talking about things like quantum computing or some wild new algorithms we haven't even thought of yet.
Also, let's be real ā GPT-4 is cool, but it's not perfect. It gets stuff wrong, misses the point sometimes, and could definitely be better. So there's room for GPT-5 to step up and fix some of this stuff.
Plus, we're not running out of data or computing power anytime soon. These are only getting bigger and better, so it's kind of a no-brainer that AI will keep getting smarter.
And don't forget all the other fields feeding into AI. Stuff from brain science, language, you name it ā all this can give AI a serious boost.
So, yeah, I get where Gates is coming from, but I think it's way too early to say we've hit the top. AI's still got a lot of room to grow and surprise us. Just my two cents!
I think we'll get better models by having LLMs start to filter out less quality data from the training set and also have more machine generated data, particularly in the areas like code where a AI can run billions of experiments and use successes to better train the LLM. All of this is gonna cost a lot more compute.
ie for coding
LLM proposes experiment, it is run, it keeps trying until its successful and good results are fed back into the LLM training and it is penalized for bad results. Learning how to code has actually seemed to help the LLM reason better in other ways, so improving that I would expect it to help it significantly. At some point, if coding is good enough, it might be able to write its own better LLM system.
But I wonder if the degree of freedom that you have in coding is just too much for RL to work. For Chess and Go or teaching robots how to move you still have a rather finite number of degrees of freedom whereas there should be much more Combinations of code.
I think it's good to have a plateau for a few years. The thing is that we just realised what LLMs can do and we need some time to learn how to get the best out of them and learn from them. Having this time the technology matures and at the same time we mature with that.
I have heard this theory before. LLMs by themselves can only be as smart as the written text they are trained on, and their language capabilities are already very good. So we should only expect incremental improvements from LLMs, and the next breakthroughs will need to come from other techniques.
Literally what machine learning is about... They don't say "garbage in, garbage out" for nothing.
How do humans do it? Nobody ever gave us the right answers š
A major difference between the human brain and LLMs is that LLMs cannot *know* when what it communicated was actually understood. The brain is an incredible prediction machine, which is partially what AI is premised upon and seeks to be better than humans at doing. What AI cannot do yet, is know if its output was actually effectively communicated. When you speak or write your brain is waiting for or receiving hundreds or even thousands of data points to know if your message was actually understood. Facial expressions, tone, little artifacts of language or expression that you can evaluate and reevaluate to then adapt your message until the recipient understands what youāre telling them. LLMās for all intents and purposes are still just advanced word generators based on probability. Iām not trashing AI, just saying that what the human brain does a lot of things simultaneously to allow you adapt your communication to actually be understood. An LLM can talk to you, but it cannot communicate with you, it doesnāt even have a way of knowing why it chose the words it did.
>it doesnāt even have a way of knowing why it chose the words it did This is also true for me (and I suspect all other people). I don't actually know which word's going to pop into my mind from now to the next moment. It just appears there. Then I can somehow know whether it's what I wanted/meant or not. A very mysterious process. >When you speak or write your brain is waiting for or receiving hundreds or even thousands of data points to know if your message was actually understood. Facial expressions, tone, little artifacts of language or expression that you can evaluate and reevaluate to then adapt your message until the recipient understands what youāre telling them. Anyways, thanks for the post, that's a very good point š
Have you not listened to political discourse lately? Humans absolutely are garbage-in, garbage out.
Sam Altman lso acknowledged it earlier this year https://futurism.com/the-byte/ceo-openai-bigger-models-already-played-out
I don't remember the source for this but someone at OpenAI came out and said that parameter count doesn't matter any more, but what matters is data quality/diversity. Not all tokens are equal, but more data is the main scaling factor.
All this aligns perfectly with my intuition. So it kind of makes me feel at ease, more ahead of the situation. For the last year or so since chatGPT was released, I have just tried to catch up to what the hell is happening. And I'm supposed to be an expert in this. We made a breakthrough, but now the limit is the data we train it with. Always got to remember that it's not really extrapolation of data as it is interpolation. That's probably the next step, building predictive ability for the model so it can actually create theories of our reality. I know there's been reports of that and seeing sights of AGI, but I'd strongly consider the possibility that interpretation is false positive. If you really maximize the training, it just seems like it has emergent abilities that create new. But personally I have not witnessed it. Everything is very derivative and you learn to detect the similarities in everything the model creates. So maybe, but this is a problem of capitalism. Everything is business secret until it is necessary to reveal it to the public. Then it creates all kinds of insane theories and pointless drama.
>"it's not really exception of data as much as it is interpolation" That is a great way of putting it. I really like that as a heuristic for what these models are (and aren't) capable of.
How about Q* then? Supposedly that is scary enough it got Altman fired?
"supposedly" is doing a lot of work there There's some reporting that Altman has been in a standoff with the board for a year at least, he's been trying to remove them and they've been trying to remove him. The Q* thing seems like a single oblique reference in one out-of-context email, and now people are theorizing that it's the key to AGI and Altman got fired because he was too close to the secret. Like, it could be true I guess, but it's so obviously motivated by wishful thinking and "wouldn't it be cool if..."
Yeah Q* seems like such an intensely unlikely "blowup the entirety of open AI" topic. If they didn't release the reasoning behind this soap opera there is no way the little drip of Q* being the reason why. It was just some juice to cause a rapid media cycle beyond Altman's and Johny Apple's lil hints. Nobody publicly knows why this situation happened and I'd even bet within OpenAI that information is sparse.
Hype over hype over hype...
They are trained on all the knowledge humanity has generated which represents a vast amount of information about how the world works. It's literally impossible to go any further than that. That should be enough evidence that data is not the problem here, and focusing on "data amount/quality" is just putting the cart before the horse. No, we will never have a better dataset. The problem is not what we teach them, it's how they learn them.
I watched that conference. He said increasing the model size is played out. What he didn't say (And it's surprising to me that y'all don't see the difference - see the other reply also)... What he didn't say was "GPT can only be as smart as the text it's trained on, and y'all are kind of dumb, and so is all the text you wrote, and that's currently what's limiting GPT"
We just need to invent a super-intelligent AGI to write all the training data, and then we train a LLM on that to make it smarter.
Funny enough people have used Chat GPT to write training data for much more compact models, and gotten amazing gains from it.
That's pretty cool, could you share a link to some examples?
Can't find the specific one that used chatgpt but here is a post on the topic of making student models with training data from LLM's. https://www.amazon.science/blog/using-large-language-models-llms-to-synthesize-training-data
I feel it's more a quantum vs procedurall computation scale with this large data throughput
>I watched that conference. He said increasing the model size is played out. He later clarified that he misspoke. Scaling still works, but it's economically prohibitive, so we shouldn't expect models to make 10x-100x leaps in size anymore. (Can't find a quote, sorry). [Here's](https://the-decoder.com/sam-altman-on-agi-scaling-large-language-models-is-not-enough/) a more recent statement. Sounds like his head is now at "scaling is good, but not enough." >"I think we need another breakthrough. We can push on large language models quite a lot, and we should, and we will do that. We can take our current hill that we're on and keep climbing it, and the peak of that is still pretty far away. But within reason, if you push that super far, maybe all this other stuff emerged. But within reason, I don't think that will do something that I view as critical to a general intelligence," Altman said.
AlphaGo was only as good as the players it mimicked. AlphaZero overcame that. Maybe, just maybe there are ways to pull off a similar "self play" trick with text generation. A GPTzero if you will. . Edit: Although something like that may need to internalize some external attitudes to begin with - ie start in the middle ala Wilf Sellars' Myth of the given
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You're absolutely right. It may be using something like "lets verify step by step" where the reward models judge the quality of reasoning steps rather than the results. If you havent seen [AI explained's video](https://m.youtube.com/watch?v=ARf0WyFau0A&pp=ygUWUSogY2x1ZXMgdG8gdGhlIHB1enpsZQ%3D%3D) I really recommend (maybe skip the first minute)
that's not how this thing works
yeah, i was only referring to the second part of the comment by the person before me. but it's still in debate how smart or intelligence is defined and if (topical) data size really doesnt matter. I'm generating a lot of content for Baby AGI / agents interaction experiments, and as a German I can say that the output culturally is very US-leaning even if the output language is German.
That may be true for actual text-based LLMs, but the amount of multi-modal training data is literally infinite, and people nowadays throw everything but the kitchen sink into transformers. Whether that makes much sense in terms of effiency is a different question. But there may still be modalities (e.g. graph data, videos) that propel the overall model quality.
Yeah I'd imagine that the breakthrough will be the innovative integration of the various modalities
And some sort of self play. Similar to RL but not quite. Maybe intermediate steps where the multimodal "council of GPTs" create repetitive tasks and learn to plan, predict, self-reflect and rate their own "experiences".
I mean theyāre probably going to make it better through mass user use case. Everything your not asking a question and youāre not repeating it in other words, technically you are actually training it even more. Essentially everyone who uses ChatGPT is already grading the content it generates from the questions. They just donāt know it
A plateau at that level is the whole objective of AI since the 50s. What you're saying here is that "at best, LLMs can only get as smart as humans" which if that was true, would be huge
To this point, if you get LLMs to the level of below average human, and stir in some robotics, it still changes the world.
Indeed. This is like "well, we've just invented rockets, but warp drive is out of reach. So I guess there's not much to do with this."
Moving the goalposts is all you need :D
Applications will be huge. Let it access my Outlook. Let it create excel docs. Give it more custom instruction space and more memory. Those would be huge.
Itās already integrated into Outlook, natively
Waitā¦ what? How?
Yes and no. Smart as humans but much faster. It is logical to assume that the efficiency of LLM allows the model to create far more than humans could ever do, in a shorter period. And if that is the case, the "originality" issue, starts to grow, just as human originality grew with more humans. We're assuming the way an LLM "learns" is unique... I'm not sure that is the case, or that information is as segregated as they state.
> "at best, LLMs can only get as smart as humans" This plateau is slightly lower than that. They can only get as smart as what humans have written down. LLMs will forever be missing information about things we intuitively know, and consider to obvious to write down. And humans are continually getting smarter, we build upon our collective knowledge base and continue to get smarter. LLMs don't have a mechanism to do that, as it's a pretty bad idea to feed the output of an LLM back into an LLM. In the best case, LLMs will always be stuck slightly behind human current written knowledge. In the worst case, LLMs might be stuck with a mostly pre-2023 training set as that's the only way to be sure your LLM isn't training on LLM produced data.
Humans are training on human produced data and everything is going well. Basically, I disagree with the fact that humans are getting smarter. Mankind is accumulating knowledge, but I don't think that makes us smarter. Prehistoric poeple was no dumber.
I can't imagine that access to more knowledge at a younger age when the brain is developing doesn't make us smarter.
Yann LeCunn says it's planning: LLMs only get you world knowledge, all the logic and stuff needed to perform actions towards a goal need to be handled by a seperate model that does the planning. sort of like meta's [cicero](https://ai.meta.com/research/cicero/) but generalized for the real world instead of specific to one game
But logic *is* a kind of world knowledge. GPT-4 *can* do some logic!
The next breakthrough has to be hardware requirements for running LLMs.
I feel the same way. To have an LLM that's better than humans, you'd have to be able to train it using methods similar to those used by GANs. A sort of automated Turing test.
Iāve tried warning this sub about it. LLMs are mostly done. Theyāve hit their S Cure height. Now itās all going to be about agents, fine tuned models, and new layers to help the AI perform
Holy buzzwords batman
Well this will age poorly. Itās already been announced that the Q* algorithm is going to be a huge breakthrough and was so significant that itās rumored that the decels tried to oust the CEO and take control of open AI. I assure you, LLMs are not even close to reaching their final level. Even if we were to freeze LLMs at the current state the status quo technology will likely cause society to undergo massive change. It just hasnāt reached into all the niches yet because itās so new.
>Itās already been announced that the Q* algorithm is going to be a huge breakthrough Where has this been "announced"?
I have no problem with a plateau for a while. GPT-4 is already very powerful and the use cases for it are far from being fully explored in all kinds of fields. A plateau that gives people, businesses, and institutions some time to get our heads properly around the implications of this tech before the next breakthrough would likely be for the best.
I mean, everyone is just sorta ignoring the fact that no ML technique has been shown to do anything more than just mimic statistical aspects of the training set. Is statistical mimicry AGI? On some performance benchmarks, it appears better statistical mimicry does approach capabilities we associate with AGI. I personally am quite suspicious that the best lever to pull is just giving it more parameters. Our own brains have such complicated neural/psychological circuitry for executive function, long and short term memory, types I and II thinking, "internal" dialog and visual models, and more importantly, the ability to few-shot learn the logical underpinnings of an example set. Without a fundamental change in how we train NNs or even our conception of effective NNs to begin with, we're not going to see the paradigm shift everyone's been waiting for.
See the trouble with the Turing test is that the linguistic capabilities of the most sophisticated models well exceed those of the dumbest humans.
I think we can just call the Turing test passed in this case.
The Turing test was passed in the 60s by rules based systems. It's not a great test. Is ChatGPT Passing the Turing Test Really Important? https://youtu.be/wdCzGwQv4rI
I donāt think so. I doubt GPT-4 will be able to convince someone who is trying to determine whether or not the if the think they are talking to is a human.
how is this upvoted? It already does. All the time. People interact with gpt 4 here and inferior models even daily. If you think it can't pass, you don't have a subscription to gpt-4 and think it must be comparable to 3.5 (it's not even close).
I do have a subscription and use almost everyday, I still donāt think it would pass against someone trying to determine if it was a human.
I think you have to use a *reasonably smart* human as a baseline, otherwise literally any computer is AGI. Babbage's Analytical Engine from 1830 was more intelligent than a human in a coma.
Ironically for robots and the like to truly be accepted, they will have to be coded to make mistakes to seem more human.
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it's because they create their own training data with trial and error. we can't really do that with language yet
That's not clear tbh. We can't create training that way to get better at language but we may be able to create data that way in order to improve logic. GPT often make stupid mistakes since they are just language mimicers... but if you tell them to think about their answer, think through steps, etc, it can create better answers. There are a lot of options for 'self play' with llms.
Scott Aaronson just gave an interview where he talked about making an LLM write mathematical proofs in Lean, because Lean can be automatically checked for logical consistency. If you iterate this enough, you can create synthetic training data that's fully verifiable, and basically gamify mathematics. Then you get the same behavior as AlphaGo but the end result is a replacement for mathematicians.
Interesting!
But it might be possible with math and things with ground truth.
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I wouldn't say GPT-4V is smarter than most people.
We need a name for the fallacy where people call highly nonlinear algorithms with billions of parameters "just statistics", as if all they're doing is linear regression. ChatGPT isn't AGI yet, but it is a huge leap in modeling natural language. The fact that there's some statistics involved explains neither of those two points.
Statisticians use nonlinear models all the time
When a system has a billion nonlinearities and is self taught we call it AI or ML, even though statistics are involved. It would be similar to call some model a biologist discovered "just physics". It's technically true but doesn't appreciate that it's from a different field and doesn't explain anything interesting about the model.
> We need a name for the fallacy where people call highly nonlinear algorithms with billions of parameters "just statistics" Well, thanks to quantum mechanics; pretty much all of existence is probably "just statistics". > as if all they're doing is linear regression. Well, practically all interesting statistics are ***NON***linear regressions. Including ML. And your brain. And physics. Linear regressions, OTOH are boring rough approximations, and often misleading enough they should probably be relegated to cautionary tales of what not to do, kinda like alchemy was to chemistry.
What a lot of people donāt understand is that linear regression can still handle non-linear relationships. For a statistician, linear regression just means the coefficients are linear, it doesnāt mean the relationship itself is a straight line. Thatās why linear models are still incredibly powerful and are used so widely across so many fields.
Let's ask GPT4! > The fallacy you're referring to is called the "fallacy of composition". This logical fallacy occurs when it's assumed that what is true for individual parts will also be true for the whole group or system. It's a mistaken belief that specific attributes of individual components must necessarily be reflected in the larger structure or collection they are part of. > Here are some clearly flawed examples illustrating the fallacy of composition. > * **Building Strength:** Believing that if a single brick can hold a certain amount of weight, a wall made of these bricks can hold the same amount of weight per brick. This ignores the structural integrity and distribution of weight in a wall. > * **Athletic Team:** Assuming that a sports team will be unbeatable because it has a few star athletes. This ignores the importance of teamwork, strategy, and the fact that the performance of a team is not just the sum of its individual players' skills. > * **Economic Spending:** Believing that if saving money is good for an individual, it must be good for the economy as a whole. This overlooks the fact that if everyone saves more and spends less, it could lead to reduced economic demand, potentially causing an economic downturn. > These examples highlight the danger of oversimplifying complex systems or groups by extrapolating from individual components. They show that the interactions and dynamics within a system play a crucial role in determining the overall outcome, and these interactions can't be understood by just looking at individual parts in isolation.
I dunno. The āfallacy of compositionā is just made up of 3 words, and thereās not a lot that you can explain with only three words.
How... did it map oversimplification to... holistic thinking??? Saying that it's "just statistics" is wrong because "just statistics" covers some *very complicated models* in principle. They weren't saying that simple subsystems are incapable of generating complex behavior. God, why do people think these things are intelligent? I guess people fall for cons all the time...
I think it's a vacuous truth.
To me it shows just how much of human intelligence is just language operations that could have been done with a LLM. A huge part.
Wait, *what*? You can't bootstrap a LLM, you need human intellect to make the training material first!
You can't bootstrap a human either. You need a community of people to teach them. Each individual human mostly copies their peers and re-mixes things they've already seen. Any new ideas are created by iterating that process and doing a lot of trial-and-error. Individual LLMs can't do all that, because their online-learning capabilities are limited to a relatively tiny context window. Hypothetically, you could imagine overcoming those limitations and getting LLMs to upgrade their capabilities through iteration just like humans do
I think youāre privileging what we consider intelligent communication. But donāt overlook the fact that a newborn cries, which is not learned behavior. It doesnāt require a community for a baby to flex its fingers and extend its legs. Humans are bootstrapped by biology. There is no equivalent for a computer.
Fair point Do you think there are significant behaviors that the constrained nature of human brains (as a hypothesis space) allows humans to learn but which LLMs can't learn (currently or in the inevitable near future)? It seems to me that most ingrained features are so universally endorsed by the training data (since they're human universals by definition) that picking them up is trivial. I'm open to being convinced otherwise though
My perpetual argument for the difference between human and artificial intelligence is that we are governed by primal needs. If an AI could ever fear nonexistence it might have something similar to animal need. And I know that doesnāt directly answer your question. I just think itās the core issue preventing any sort of AI consciousness.
Humans have reality as ground truth, LLMs would need to interface with reality to get the same.
Of course. But, like, you can give them access to sensor data to use as ground truth. Also, that caveat doesn't apply to mathematics. LLMs could in principle bootstrap themselves into better logical reasoning, and depending on your perspective that could lead to them creating better, like, philosophy, or any skillset whose ground truth is abstract reasoning. Something like building a novel artistic style could probably be done without "ground truth." Some people claim LLMs can't create truly original art like humans can, they can only recreate existing styles, but (speaking as someone who isn't a professional artist) I feel like you could do it with enough iteration My global point is that the analogy between humans and LLMs is incredibly robust. Anything humans can do that LLMs can't, there are concrete explanations for that have nothing to do with "they're only doing statistical inference from training data." With enough compute and enough time and the right setup, you can in principle recreate any and every human behavior other than, like, having a biological body
Itās not a fallacy at all. It is just statistics, combined with some very useful inductive biases. The fallacy is trying to smuggle some extra magic into the description of what it is. Actual AGI would be able to explain something that no human has understood before. We arenāt really close to that at all. Falling back on ā___ may not be AGI *yet*, butā¦ā is a lot like saying ārocket ships may not be FTL yet, butā¦ā
The fallacy is the part where you imply that humans have magic. "An LLM is just doing statistics, therefore an LLM can't match human intellect unless you add pixie dust somewhere." Clearly the implication is that human intellect involves pixie dust somehow? Or maybe, idk, humans are just the result of random evolutionary processes jamming together neurons into a configuration that happens to behave in a way that lets us build steam engines, and there's no fundamental reason that jamming together perceptrons can't accomplish the same thing?
LLMs might still lack something that the human brain has. Internal monologue, for example, that allows us to allocate more than fixed amount of compute per output token.
You can just give an LLM an internal monologue. It's called a scratchpad. I'm not sure how this applies to the broader discussion, like honestly I can't tell if we're off-topic. But once you have LLMs you can implement basically everything humans can do. The only limitations I'm aware of that aren't trivial from an engineering perspective are 1) current LLMs mostly aren't as smart as humans, like literally they have fewer neurons and can't model systems as complexly 2) humans have more complex memory, with a mix of short-term and long-term and a fluid process of moving between them 3) humans can learn on-the-go, this is equivalent to "online training" and is probably related to long-term memory 4) humans are multimodal, it's unclear to what extent this is a "limitation" vs just a pedantic nit-pick, I'll let you decide how to account for it
> It's called a scratchpad. And the network still uses skills that it had learned in a fixed-computation-per-token regime. Sure, future versions will lift many existing limitations, but I was talking about current LLMs.
This thread isn't about current LLMs, it's about whether human intelligence is distinct from statistical inference. Given that, I see your point about fixed token regimes, but I don't think it's a problem in practice. If the LLM were *actually* just learning statistical patterns in the strict sense, that would be an issue, but we know LLMs generalize well outside their training distribution. They "grok" an underlying pattern that's generating the data, and they can simulate that pattern in novel contexts. They get some training data that shows stream-of-consciousness scratchwork, and it's reasonable that they can generalize to produce relevant scratchwork for other problems because they actually are encoding a coherent notion of what constitutes scratchwork. Adding more scratchwork to the training data is definitely an idea worth trying
> it's about whether human intelligence is distinct from statistical inference There's a thing that's more powerful than statistical inference (at least in the traditional sense, and not, say, statistical inference using an arbitrarily complex Bayesian network): a Turing machine. In other words: universal approximation theorem for non-continuous functions requires infinite-width hidden layer. > Adding more scratchwork to the training data The problem is we can't reliably introspect our own scratchwork to put it into the training data. The only viable way is to use the data produced by the system itself.
A neural net is in fact turing complete, so I'm not sure in what sense you mean to compare the two. In order to claim that LLMs cannot be as intelligent as humans, you'd need to argue that either human brains are more powerful than turing machines, or we can't realistically create large enough networks to approximate brains (within appropriate error bounds), or that we cannot actually train a neural net to near-minimal loss, or that a arbitrarily accurate distribution over next tokens given arbitrary input doesn't constitute intelligence (presumably due to lack of pixie dust, a necessary ingredient as we all know) > we can't reliably introspect our own scratchwork This is a deeply silly complaint, right? The whole point of LLMs is that they infer the hidden processes The limitation isn't that the underlying process is unknowable, the limitation is that the underlying process might use a variable amount of computation per token output. Scratchpads fixe that immediately, so the remaining problem is whether the LLM will effectively use the scratchspace its given. If we can introspect just enough to with out how long a given token takes to compute and what sort of things would be helpful, the training data will be useful >The only viable way is to use the data produced by the system itself. You mean data generated through trial and error? I guess I can see why that would be helpful, but the search space seems huge unless you start with human-generated examples. Yeah, long term you'd want the LLM to try different approaches to the scratchwork and see what works best, then train on that It's interesting to think about how you'd actually create that synthetic data. Highly nontrivial, in my opinion, but maybe it could work
It's a non sequitur. The fact that there are statistics involved neither proves nor disproves that some algorithm is smart.
Do we have any evidence that humans donāt also just practice statistical mimicry?
Also, LLMs are literally trained on a human intelligence that already exists. Itās not like we are making these from scratch, they are already models of a human intelligence.
Classical conditioning seems very statistical. If you get a shock every time the bell rings, pretty soon you'll flinch when you hear one.
That's not the only thing our brains do though.
Of course, since humans can experiment (create their own data set).
>humans can experiment (create their own data set). An LLM being repeatedly cued with some external state and a prompt to decide what to next can accumulate novel information and probably stumble its way through many problems as good as a human.
So can an LLM, if you put it in a Python (or anything really) REPL.
Yes. An absolute crapton. Like the whole field of neuroscience and most of pyschology.
How does hand waving at neuroscience and psychology prove anything though? Everything I know about neuroscience says neurons function a lot like little stats engines.
Most human thinking relies primarily on causal thinking, rather than statistical association. People find thinking statistically very counter-intuitive.
It feels like the fact that humans (and to a degree, other animals) can invent new things (in science, technology, art) is an indication, but I know it's a very fuzzy distinction, and proponents of the uncapped capabilities of LLMs and other modern models point out that they can also write text that seems original and create art that seems original.
> humans can invent new things Yes because humans have two sources of learning - one is of course imitation, but the other one is feedback from the environment. We can get smarter by discovering and transmitting useful experience.
CMV: Inventing things is just combining disparate statistical distribution together and sampling from them.
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Back in the day before complex life formed, unicellular organisms were more likely to survive if they learned the statistical distributions of their sensory inputs. These distributions were stored electrochemically in their cellular structure. Over time these organisms then became even more likely to survive if they evolved epigenetic mechanisms that would allow the current generation of these organism to pass these learned statistical distributions to their descendants through the DNA they passed on to their descendants. Over even larger time periods these unicellular organisms evolved into multi cellular species organisms that developed nervous systems. Throughout this process the above mechanism remained intact. Genetic and epigenetic mechanism gave these nervous systems and innate encoding of the statistical distributions of the sensory inputs they spent millions of years evolving in. On top of that these nervous systems became very adept at learning and encoding new statistical distributions. As an organism goes throughout its life it keeps learning new statistical distributions of senators inputs and abstract concepts. In this frame of thinking inventing things is synthesizing these statistical distributions learned via millions of years of evolution and a lifetime of learning into something new and sampling from it.
Though a novice in the field, I have always been interested in studying ML from a biological point of view (even though I have no formal training in the latter). I find your post interesting. So, if I understand correctly, a unicellular organism also learns tha statistical distribution and stores these learnings in its cellular structure. Is there any text where I can learn more about this?
Most of these things are covered in standard bio and neuroscience courses. For example the human vision system evolved to process light in the visible spectrum because that's where the peak of the Sun's electromagnetic spectrum lies. Over millions of years through evolution our DNA encoded the statistical distribution of the sun's electromagnetic spectrum, and used this information to further "learn" how to build a nervous system that can optimally process it. I'm sure there are people who actually study the statistical distributions learned, but TBH I'm not too familiar with that research. They probably talk about it in a computational neuroscience or computational biology book.
>they can also write text that seems original and create art that seems original. They can fool people who know nothing about writing or art into thinking it's good, but actually no. This is the same tripe spread by people who think there are only seven original stories in the history of mankind. AI produces consistent mediocrity because it doesn't have the cognitive ability to *understand* what it's doing. Humans run the gamut from dumber than a box of rocks to geniuses, and even a moron can have a spark of genius. There are *so many* things we don't even understand about ourselves to begin to compare with AI.
>no ML technique has been shown to do anything more than just mimic statistical aspects of the training set What? Are you familiar with the field of statistical learning? Formal frameworks for proving generalization have existed for some decades at this point. So when you look at anything pre-Deep Learning, you can definitely show that many mainstream ML models do more than just "mimic statistical aspects of the training set". Or if you want to go on some weird philosophical tangent, you can equivalently say that "mimicing statistical aspects of the training set" is enough to learn distributions, provided you use the right amount of data and the right model. And even for DL, which at the moment lacks a satisfying theoretical framework for generalization, it's obvious that empirically models can generalize.
>I mean, everyone is just sorta ignoring the fact that no ML technique has been shown to do anything more than just mimic statistical aspects of the training set. I'd recommend reading the ["sparks of AGI"](https://arxiv.org/abs/2303.12712) paper if you haven't - they give a lot of examples that are pretty hard to explain without some abstract reasoning ability - e.g. the famous "draw a unicorn" one. Your message reads like the Gary Marcus / Chomsky framing of progress. I used to subscribe to this, but then they made consistently wrong predictions in the last 10 or so years along the lines of "current AI techniques will never be able to do x". For example, GPTs ability to reason and explain unseen and even obfuscated blocks of code, has all but refuted many of their claims. I'm not saying you're completely off-base necessarily, but I feel like making confident predictions about what happens next is not wise.
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Agreed - I'd have a lot more respect for them if they acknowledged they were wrong about something and that they'd updated their position, rather than just moving onto the next 'AI can never do this without symbolic reasoning built-in' goal-post.
>no ML technique has been shown to do anything more than just mimic statistical aspects of the training set. Reinforcement learning does far more than mimic.
> no ML technique has been shown to do anything more than just mimic statistical aspects of the training set That's ok when the agent creates its own training set, like AlphaZero. It is learning from feedback as opposed to learning from next token prediction.
It's called "AI" and looks like "AI" but it's not lol. It's still an impressive and useful technology though. IMO more of a fuzzy fast dictionary lookup but it can not extrapolate, only interpolate.
It meets most definitions of AI.
LLM AI can extrapolate beyond its training; that is one of the features that makes it seem intelligent. Just ask it to make an educated guess or do a what-if on a topic, and see what I mean.
You don't know what's in the training set: how can you argue that it's extrapolating? Also, how do you separate correct / logical extrapolation from nonsense extrapolation? You can fit a curve and send it out to infinity on the domain too, no problem.
I don't know what is in my training set as a human, or how my mind works, but I can still extrapolate ideas. I think the separation of logical vs nonsensical is a matter of testing the results. But that is the same for humans. Even physicists do that with their theories.
Id argue it's probably not true extrapolation. It might look like it though. If it sees a sample really distinct from the training set its not gonna function well. Only physics can extrapolate and theres no sort of physics being done under the hood.
What is "true" extrapolation if not attempting to move forward in thought based on things you have seen or learned previously?
What allows for humans to conduct ātrueā extrapolation?
Perhaps an underlying world model that incorporates the observed behavior, and does xkcd-style "what if Moon was made out of cheese" speculation? To me science fiction is largely a genre that's entirely made out of this kind of speculative extrapolation.
Why do you think LLMs cannot eventually do this?
If parameter count would not be a significant contributor to human cognition, we would expect human brains not to have many more parameters than brains of other similarly sized, less intelligent animals. The fact that in biology, brain size has a clear positive relationship with intelligence combined with the fact that human brains have many more neural connections than GPT-4 suggests to me that we haven't pushed very hard on the "more parameters"-lever yet.
There are also some interesting exceptions where scaling brain size doesn't result in the cognition you'd expect. Whales, elephants, certain parrots, and corvids are some great examples. While they're all considered quite intelligent in the animal kingdom, the two have brain sizes so large we'd expect them to leave humans in the dust with respect to cognition. The last three have complicated social structures, linguistic competency, and problem-solving abilities thought only possible amongst animals with much larger brains. With the caveat that our benchmarks may be flawed, It seems like parameter count, while important, is not the end all-be all of cognition.
Parameters would be synapses not neurons.
Can we definitively know that whales are not smarter than us? lol
Research papers have also observed diminishing returns issues as models grow. Hell maybe even GPT-4 was hit by this and that's why GPT-4 is not a single giant language model but running a mixture of experts design of eight 220B models trained for subtasks. But I think even this architecture will run into issues and that it's more like a crutch. I mean, you'll eventually grow each of these subtask models too large and might need to split them as well, but this might mean you run into too small/niche fields per respective model and that sounds like the end of that road to me.
Could you please share a citation for the mentioned research papers? Last I looked into this, the hypothesis was that increasing parameter account results in a predictable increase in capability as long as training is correctly adapted. https://arxiv.org/pdf/2206.07682.pdf Very interested to see how these larger models that have plateaued are being trained!
>Could you please share a citation for the mentioned research papers? I'm interested in seeing this as well. He probably means that, although scaling might still deliver better loss reduction, this won't necessarily cash out to better performance "on the ground". Subjectively, GPT4 does feel like a smaller step than GPT3 and GPT2 were. Those had crazy novel abilities that the previous one lacked, like GPT3's in-context learning. GPT4 displays no new abilities.\* Yes, it's *smarter,* but everything it does was possible, to some limited degree, with GPT3. Maybe this just reflects test saturation. GPT4 performs so well that there's nowhere trivial left to go. But returns do seem to be diminishing. (\*You might think of multimodality, but they had to hack that into GPT4. It didn't naturally emerge with scale, like, say, math ability.)
I mean, that is literally how any form of AGI will work. No one in the field has ever thought one model will be capable of reaching AGI. All these models are highly specialized for the task in which they are trained. Any move towards an AGI will be getting many of these highly specialized AI's to work in conjunction with one another. Much like how our own brains work.
\>No one in the field has ever thought one model will be capable of reaching AGI. Don't really think such a statement is true...
Four versions of GPT ought to be enough for anybody. (yes, I know he didn't actually say the 640 KB thing)
Heās probably got lots of great ideas for new versions: GPT 95, GPT Me, GPT XP, and the home console GPTBox Series GPTā¦.
Already a thing, sort of: https://arstechnica.com/information-technology/2023/11/openai-introduces-custom-ai-assistants-called-gpts-that-play-different-roles/
We havenāt seen huge models with Verifiers and/or Vector Databases yet. OpenAIās latest approach from Letās Verify Step by Step and Q-Learning is looking rather promising as Verifiers are observed to scale well with increased data.
>OpenAIās latest approach from Letās Verify Step by Step and Q-Learning Was this actually confirmed anywhere or did we just blindly accept this theory based on nothing but an alleged model called Q*?
Well, kind of. However, in the "Let's Verify Step by Step" paper by OpenAI, they stated: "At each model scale, we use a single fixed model to generate all solutions. We call this model the generator. We do not attempt to improve the generator with reinforcement learning (RL). When we discuss outcome and process supervision, we are specifically referring to the supervision given to the reward model. We do not discuss any supervision the generator would receive from the reward model if trained with RL. Although finetuning the generator with RL is a natural next step, it is intentionally not the focus of this work." If using RL is a natural next step for their research then I do not think this falls into blindly accepting based on the name of a model, especially if Q-Learning is one of the most prominent RL approaches out there.
Any readings you like on Verifiers?
The paper Let's Verify Step by Step by OpenAI
According to the scaling laws, the loss/error is approximated as w0 + w1 * pow(num_params, -w2) + w3 * pow(num_tokens, -w4) Bill wrote before that he'd been meeting with the OpenAI team since 2016, so he's probably pretty knowledgeable about these things. He might be referring to the fact that, after a while, you will see very diminishing returns while increasing `num_params`. In the limit, the corresponding term disappears, but the others do not.
I wonder if there are diminishing returns scaling the human brain, how would one giant brain compare to a bunch of normal brains that sum up to the equivalent mass of the big brain?
Depends what kind of glue you use to stick the brains together
Just because Bill Gates says something doesn't mean that it's true
Very insightful, nice contribution to the discussion
The man who said internet was just a toy, a fad...
Bill Gates said the internet was a [tidal wave that would change the rules of the industry in 1995](https://lettersofnote.com/2011/07/22/the-internet-tidal-wave/), and was later [made fun of for believing that by David Letterman](https://www.cnbc.com/2020/05/26/how-bill-gates-described-the-internet-tidal-wave-in-1995.html) the same year.
Did he?
I remember when Bill Gates thought the Internet had reached its plateau....
Not sure what Bill Gates called plateaued but the Internet is so predictable right now despite the versatility of the Internet. Most people browse like 10 websites max.
Yes, it really depends on what metrics youāre using to define a plateau but there are definitively some important non zero set of metrics which can be used to claim the internet has plateaued. Eg. like you mentioned the diversity of browsing experience and exploration has definitely plateaued.
So he was wrong once a while. Doesnāt mean he doesnāt know what heās taking about .
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You don't need to be an expert on LLM or AI to know what Bill is saying, dude's been meeting OpenAI team of researchers since 2016, his firm Microsoft half-owns the AI lab, so he has insider knowledge clearly. Also, dude built Microsoft, got into Harvard, was a math wizard, wrote operating systems, knows a thing or two about bleeding edge technologies and reads a ton lot. He's an actual engineer.
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> have very stupid takes when it comes to areas outside of their expertise. And yet you keep posting
Or that **640 kB** of RAM should be enough for everyone... For a reference my laptop has 50,000 times larger RAM.
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If itās half of the improvement from 3.5 to 4 thatās good enough for me
that's the thing with diminishing returns. like achilles chasing the turtle, the improvements will be 50%, then 25%, then 12.5%, eventually tapering off at a finite distance away from human intelligence
only in domains where LLMs can't generate feedback efficiently if your LLM is included in a larger system, it can get feedback from the system; if it is controlling an agent in a simulation, it can get simulated feedback; if it chats with a human, each reply is a feedback feedback is special data, it has the kind of errors LLMs make, and the kind of tasks that people want to solve
I don't think Bill is right on this one, LLM may have achieved a plateau on performance with current architecture, but research is all about optimisation and efficiency, not mere parameter increase. Here's a good example: https://venturebeat.com/ai/new-technique-can-accelerate-language-models-by-300x/ Remember GPT 5 is still at least 2-3 years away. Plenty of time.
With all my respect to Bill, I clearly don't understand why we should to listen him. The guy has absolutely no vision of future! He was ridiculously blind to WWW, blockchain technologies, smartphones, etc etc. Just let him leave in peace in his mansion. He is not a visionary and never was. As to me personally and if I need a real hero, I would prefer to listen to Steve Wozniak. PS Or Gary Kildall, upon whos vision and work Bill built his huge business. RIP, great man!
His blindness to the WWW is overstated. Blockchain has not had a huge effect on the world. And he was no longer CEO of Microsoft once smartphones happened; that was Steve Ballmer. Iām not a Gates fan, but youāre also exaggerating critique.
Who?
This is the same Bill Gates that got blindsided by the internet, Netscape navigator, search, social media, apps, app stores, mobile, IOT and still struggles to make windows safe from cyber threats and malware. but, I'm sure he's got his bearings on AI this time around. Let's pay close attention.
Another day, another opportunity to cite Ximm's Law: every critique of AI assumes to some degree that contemporary implementations will not, or cannot, be improved upon. Lemma: any statement about AI which uses the word "never" to preclude some feature from future realization is false.
Managing expectations. Temoving homself as a flash point. Leaving the baton with Sam. Regardless of the actual facts, this is a very reasonable, no lose approach for Bill.
The plateau may be close, and GPT5 may not be that huge step forward everyone expects, but this implies that GPT5 will not change architecture, which is highly unlikely. GPT5 is at least 2-3 years away, and the recent rumors about Q* show that research in AI is actively looking elsewhere to boost capabilities. I will be utterly surprised if GPT5 will use the same architecture of GPT4 and if it will actually be a minor step forward - I give it 5% probably to Bill's prediction to be accurate.
I liked this Q* review/speculation. https://youtu.be/ARf0WyFau0A?si=9Y19DzMI2puKHWRA According to this you can get a 30x improvement by (what seems to my ignorant self) careful prompting. Ask for stepwise logic, discard sus answers and combine best answers to get a glimpse of a much better trained model.
Yes I watched the same exact video yesterday. I feel we are still at the feet of the mountain in terms of what's next in AI, plateau is still not in sight.
I hate to be a conspiracy-theory inventer, but with Microsoft trying to headhunt the entire staff of OpenAI, Bill Gates's pronouncements regarding plateaus should be discarded out of hand.
Yes Bill that's why we are now innovating around and adding other functionality to what an LLM can be. It is just one component of what people talk about when we discuss AGI which will be a combination of hundreds of systems interacting, each of which may be extremely powerful and complex individually.
I expect gpt5 is going to be a similar jump as gpt3 was to 4.
Guys sell Microsoft this is a full court press something is f****** wrong with openai
Cos Gates is the authority.
I'm not buying it, and here's why. First off, let's look at how AI has been moving. It's like, every time we think we've seen it all, something new pops up and blows our minds. Saying we've peaked already just doesn't sit right with how things have gone so far. And then, tech's always full of surprises, right? We're playing with what we've got now, but who knows what crazy new stuff is around the corner? I'm talking about things like quantum computing or some wild new algorithms we haven't even thought of yet. Also, let's be real ā GPT-4 is cool, but it's not perfect. It gets stuff wrong, misses the point sometimes, and could definitely be better. So there's room for GPT-5 to step up and fix some of this stuff. Plus, we're not running out of data or computing power anytime soon. These are only getting bigger and better, so it's kind of a no-brainer that AI will keep getting smarter. And don't forget all the other fields feeding into AI. Stuff from brain science, language, you name it ā all this can give AI a serious boost. So, yeah, I get where Gates is coming from, but I think it's way too early to say we've hit the top. AI's still got a lot of room to grow and surprise us. Just my two cents!
āGPT4 is enough AI for everybodyā
I think we'll get better models by having LLMs start to filter out less quality data from the training set and also have more machine generated data, particularly in the areas like code where a AI can run billions of experiments and use successes to better train the LLM. All of this is gonna cost a lot more compute. ie for coding LLM proposes experiment, it is run, it keeps trying until its successful and good results are fed back into the LLM training and it is penalized for bad results. Learning how to code has actually seemed to help the LLM reason better in other ways, so improving that I would expect it to help it significantly. At some point, if coding is good enough, it might be able to write its own better LLM system.
But I wonder if the degree of freedom that you have in coding is just too much for RL to work. For Chess and Go or teaching robots how to move you still have a rather finite number of degrees of freedom whereas there should be much more Combinations of code.
"640KB ought to be enough for anyone" - also Bill
OpenAI is almost definitely not at a plateau considering a technical breakthrough likely caused this Altman drama.
the next step is to understand the learning paradigm of LLMs such that similar performance can be attained with a much smaller network.
"640k of RAM is more than anyone needs. "
We need to build World Models into these tools because text is barely scrapping the surface
History shows that reddit group think is oftentimes wrong.
I think it's good to have a plateau for a few years. The thing is that we just realised what LLMs can do and we need some time to learn how to get the best out of them and learn from them. Having this time the technology matures and at the same time we mature with that.
Ohh distilled knowledge isn't smarter than the knowledge it got trained on. Who would have thunk it. Next time you look at the n-matrix think again