Ilya Sutskever: We're moving from the age of scaling to the age of research • Hacker News Board

Re: Ilya Sutskever: We're moving from the age of scaling to the age of research

#331

Post by saberience » Wed, Nov 26, 2025, 4:07 PM UTC

ivan_gammel wrote: post #216

orbital-decay wrote: post #132

>You could actually wonder that one possible explanation for the human sample efficiency that needs to be considered is evolution. Evolution has given us a small amount of the most useful information possible. It's definitely not small. Evolution performed a humongous amount of learning, with modern homo sapiens, an insanely complex molecular machine, as a result. We are able to learn quickly by leveraging this "pret…

I think it’s a bit different. Evolution did not give us the dataset. It helped us to establish the most efficient training path, and the data, the enormous volume of it starts coming immediately after birth. Humans learn continuously through our senses and use sleep to compress the context. The amount of data that LLMs receive only appears big. In our first 20 years of life we consume by at least one order of magnitu…

Sorry but this is patently rubbish, we do not consume orders of magnitude more data than the training datasets, nor do we "process" it in anything like the same way.

Firstly, most of what we see, hear, experience etc, is extremely repetitive. I.e. for the first several years of our live we see the same people, see the same house, repeatedly read the same few very basic books, etc etc. So, you can make this argument purely based on "bytes" of data. I.e. humans are getting this super HD video feed, which means more data than an LLM. Well, we are getting a "video feed" but mostly of the same walls in the same room, which doesn't really mean much of anything at all.

Meanwhile, LLMs are getting LITERALLY, all of humanities recorded textual knowledge, more recorded audio than 10000 humans could listen to in their lifetime, more images and more varied images than a single person could view in their entire life, reinforcement learning on the hardest maths, science, and programming questions etc.

The idea that because humans are absorbing "video" means that its somehow more "data" than frontier LLMs are trained with is laughable honestly.

Re: Ilya Sutskever: We're moving from the age of scaling to the age of research

#332

Post by bossyTeacher » Wed, Nov 26, 2025, 4:28 PM UTC

firestell wrote: post #323

bossyTeacher wrote: post #302

Earlier quoted context omitted.

This. This is what I find hilarious that even smart HN folks seem unable to understand. Transformers tech products are a service offered by private companies who are under no obligation to serve it to you indefinitely. At any given point, they are free to end public access. And you better believe that they will do so if it is in their interest. inb4 open source models, those models are also hosted on the servers of p…

Thats borderline aluminum hat conspiracy theory. Corporations arent a monolith, you think amazon is ever going to stop you from renting machines so that you cant run your AI models instead of buying from OpenAI? They have no horse in that race.

> you think amazon is ever going to stop you from renting machines so that you cant run your AI models instead of buying from OpenAI

We are talking about a future with near AGI systems. In such a future, people like you or me have no money to pay those services with because we are all unemployed and starving. And amazon has much bigger ambitions than just resting cloud compute to you. The economy as we know it doesn't really exist in that scenario and neither do the incentives and constraints that exist in our current economies.

People talking about intelligent systems a lot without considering the profound changes it would cause to everything.

There is no future where near AGI and traditional economies coexist. Near AGI is essentially a type of swan

Re: Ilya Sutskever: We're moving from the age of scaling to the age of research

#333

Post by emporas » Wed, Nov 26, 2025, 4:40 PM UTC

TheBlight wrote: post #23

alyxya wrote: post #18

The impactful innovations in AI these days aren't really from scaling models to be larger. It's more concrete to show higher benchmark scores, and this implies higher intelligence, but this higher intelligence doesn't necessarily translate to all users feeling like the model has significantly improved for their use case. Models sometimes still struggle with simple questions like counting letters in a word, and most p…

"Scaling" is going to eventually apply to the ability to run more and higher fidelity simulations such that AI can run experiments and gather data about the world as fast and as accurately as possible. Pre-training is mostly dead. The corresponding compute spend will be orders of magnitude higher.

>Pre-training is mostly dead.

I don't think so. Serious attempts for producing data specifically for training have not being achieved yet. High quality data I mean, produced by anarcho-capitalists, not corporations like Scale AI using workers, governed by laws of a nation etc etc.

Don't underestimate the determination of 1 million young people to produce within 24 hours perfect data, to train a model to vacuum clean their house, if they don't have to do it themselves ever again, and maybe earn some little money on the side by creating the data.

The other part of the comment I agree.

Re: Ilya Sutskever: We're moving from the age of scaling to the age of research

#334

Post by ACCount37 » Wed, Nov 26, 2025, 4:57 PM UTC

filleduchaos wrote: post #328

ACCount37 wrote: post #322

Earlier quoted context omitted.

You don't get it at all, do you? "Implements the same math" IS the similarity. I'm baffled that someone in CS, a field ruled by applied abstraction, has to be explained over and over again that abstraction is a thing that exists.

In case you have missed it in the middle of the navel-gazing about abstraction, this all started with the comment "Please stop comparing these things to biological systems. They have very little in common."[0] If you insist on continuing to miss the point even when told explicitly that the comment is referring to what's inside the box, not its interface, then be my guest. There isn't much of a sensible discussion abo…

You are missing the point once again.

They have "very little in common", except for the fact that they perform the same kind of operations.

Re: Ilya Sutskever: We're moving from the age of scaling to the age of research

#335

Post by HarHarVeryFunny » Wed, Nov 26, 2025, 5:14 PM UTC

jebarker wrote: post #326

HarHarVeryFunny wrote: post #308

Earlier quoted context omitted.

> This sounds magical though Not really, this is just the way that evolution works - survival of the fittest (in the prevailing environment). Given that the world is never same twice, then generalization is a must-have. The second time you see the tiger charging out, you better have learnt your lesson from the first time, even if everything other than "it's a tiger charging out" is different, else it wouldn't be very…

> Note how different, and massively more complex, the spatio-temporal real world of messy analog never-same-twice dynamics is to the 1-D symbolic/discrete world of text that "AI" is currently working on. I agree that the real world perceived by a human is vastly more complex than a sequence of text tokens. But it’s not obvious to me that it’s actually less full of repeating patterns or that learning to recognize and…

I'm not sure how they can be compared, but of course the real world is highly predictable and repetitious (if you're looking at the right generalizations and abstractions), with brains being the proof of that. Brains are very costly, but their predictive benefit is big enough to more than offset the cost.

The difference between brains and LLMs though is that brains have evolved with generality as a major driver - you could consider it as part of the "loss function" of brain optimization. Brains that don't generalize quickly won't survive.

The loss function of an LLM is just next-token error, with no regard as to HOW that was achieved. The loss is the only thing shaping what the LLM learns, and there is nothing in it that rewards generalization. If the model is underparamized (not that they really are), it seems to lead to superposed representations rather than forcing generalization.

No doubt the way LLMs are trained could be changed to improve generalization, maybe together with architectural changes (put an autoencoder in there to encourage compressed representations ?!), but trying to take a language model and tweak it into a brain seems the wrong approach, and there is a long list of architectural changes/enhancements that would be needed if that is the path.

With animal brains, it seems that generalization must have been selected for right from the simplest beginnings of a nervous system and sensory driven behavior, given that the real world demands that.

Re: Ilya Sutskever: We're moving from the age of scaling to the age of research

#336

Post by riku_iki » Wed, Nov 26, 2025, 6:02 PM UTC

HarHarVeryFunny wrote: post #321

joelthelion wrote: post #227

> When do you expect that impact? I think the models seem smarter than their economic impact would imply. > Yeah. This is one of the very confusing things about the models right now. As someone who's been integrating "AI" and algorithms into people's workflows for twenty years, the answer is actually simple. It takes time to figure out how exactly to use these tools, and integrate them into existing tooling and workf…

No doubt LLMs and tooling will continue to improve, and best use cases for them better understood, but what Ilya seems to be referring to is the massive disconnect between the headline-grabbing benchmarks such as "AI performs at PhD level on math", etc, and the real-world stupidity of these models such as his example of a coding agent toggling between generating bug #1 vs bug #2, which in fact largely explains why th…

> the real-world stupidity of these models such as his example of a coding agent toggling between generating bug #1 vs bug #2, which in fact largely explains why the current economic and visible impact is much less than if the "AI is PhD level" benchmark narrative was actually true.

this could be true in the past, but in recent weeks I started more and more trust top AI models and less PhDs I work with. Quality jump is very real imo.

Re: Ilya Sutskever: We're moving from the age of scaling to the age of research

#337

Post by notnullorvoid » Wed, Nov 26, 2025, 6:50 PM UTC

orbital-decay wrote: post #132

>You could actually wonder that one possible explanation for the human sample efficiency that needs to be considered is evolution. Evolution has given us a small amount of the most useful information possible. It's definitely not small. Evolution performed a humongous amount of learning, with modern homo sapiens, an insanely complex molecular machine, as a result. We are able to learn quickly by leveraging this "pret…

I think the important part in that statement is the "most useful information", the size itself is pretty subjective because it's such an abstract notion.

Evolution gave us very good spatial understanding/prediction capabilities, good value functions, dexterity (both mental and physical), memory, communication, etc.

> It's pretty obvious that artificially created models don't have synthetic datasets of the quality even remotely comparable to what we're able to use.

This might be controversial, but I don't think the quality or amount of data matters as much as people think if we had systems capable of learning similar enough to the way human's and other animals do. Much of our human knowledge has accumulated in a short time span, and independent discovery of knowledge is quite common. It's obvious that the corpus of human knowledge is not a prerequisite of general intelligence, yet this corpus is what's chosen to train on.

Re: Ilya Sutskever: We're moving from the age of scaling to the age of research

#338

Post by anthonypasq » Wed, Nov 26, 2025, 7:14 PM UTC

highfrequency wrote: post #277

Great respect for Ilya, but I don’t see an explicit argument why scaling RL in tons of domains wouldn’t work.

doesnt RL by definition not generalize? thats Ilya's entire criticism of the current paradigm

Re: Ilya Sutskever: We're moving from the age of scaling to the age of research

#339

Post by anthonypasq » Wed, Nov 26, 2025, 7:15 PM UTC

ilaksh wrote: post #156

JimmyBuckets wrote: post #106

I respect Ilya hugely as a researcher in ML and quite admire his overall humility, but I have to say I cringed quite a bit at the start of this interview when he talks about emotions, their relative complexity, and origin. Emotion is so complex, even taking all the systems in the body that it interacts with. And many mammals have very intricate socio-emotional lives - take Orcas or Elephants. There is an arrogance I…

The question of how emotions function and how they might be related to value functions is absolutely central to that discussion and very relevant to his field. Doing fundamental AI research definitely involves adjacent fields like neurobiology etc. Re: the discussion, emotions actually often involve high level cognition -- it's just subconscious. Let's take a few examples: - amusement: this could be something simple…

i think the contention is the idea that emotions are simple.

Re: Ilya Sutskever: We're moving from the age of scaling to the age of research

#340

Post by ivan_gammel » Wed, Nov 26, 2025, 7:25 PM UTC

saberience wrote: post #331

ivan_gammel wrote: post #216

Earlier quoted context omitted.

I think it’s a bit different. Evolution did not give us the dataset. It helped us to establish the most efficient training path, and the data, the enormous volume of it starts coming immediately after birth. Humans learn continuously through our senses and use sleep to compress the context. The amount of data that LLMs receive only appears big. In our first 20 years of life we consume by at least one order of magnitu…

Sorry but this is patently rubbish, we do not consume orders of magnitude more data than the training datasets, nor do we "process" it in anything like the same way. Firstly, most of what we see, hear, experience etc, is extremely repetitive. I.e. for the first several years of our live we see the same people, see the same house, repeatedly read the same few very basic books, etc etc. So, you can make this argument p…

I like your confidence, but I think you missed a few things here and there.

Training datasets are repetitive too. Let’s say, you feed some pretty large code bases to an LLM: how many times there will be a for loop? Or how many times Newton laws (or any other important ideas) are mentioned there? Not once, not two times, but many more. How many times you will encounter a description of Paris, London or St.Petersburg? If you eliminate repetition, how much data will actually be left there? And what’s the point anyway: this repetition is required part of the training, because it places that data in context, linking it to everything else.

Is repetition that we have in our sensory inputs really different? If you had children or had opportunity to observe how do they learn, they are never confined in the same static repetition cycle. They experience things again and again in a dynamic environment that evolves over time. When they draw a line, they get instant feedback and learn from it, so that next line is different. When they watch something on TV for fifth time, they do not sit still, they interact — and learn, through dancing, repeating phrases and singing songs. In a familiar environment that they have seen so many times, they notice subtle changes and ask about them. What was that sound? What was that blinking light outside? Who just came in and what’s in that box? Our ability to analyze and generalize probably comes from those small observations that happen again and again.

Even more importantly, when nothing is changing, they learn through getting bored. Show me an LLM that can get bored when digging through another pointless conversation on Reddit. When sensory inputs do not bring anything valuable, children learn to compensate through imagination and games, finding the ways to utilize those inputs better.

You measure quality of data using wrong metrics. The intelligence is not defined by the number of known facts, but by the ability to adapt and deal with the unknown. The inputs that humans use prepare us for that better than all written knowledge of the world available to LLM.