In the meantime, 1000x or 10000x inference time cost for running an LLM gets you into pretty ridiculous cost territory.

After that, just adding up the material on both sides, without taking into account the position of the pieces at all, is a heuristic that will correctly predict the winning player on the vast majority of all possible board positions.

This is why you need to constantly babysit todays AI and tell it to do steps and correct itself all the time, because you are much better at getting to pure knowledge than the AI is, it would quickly veer away into nonsense otherwise.

You got to take a step back and look at LLMs like ChatGPT. With 180 million users and assuming 10,000 tokens per user per month, that's 1.8 trillion interactive tokens.

LLMs are given tasks, generate responses, and humans use those responses to achieve their goals. This process repeats over time, providing feedback to the LLM. This can scale to billions of iterations per month.

The fascinating part is that LLMs encounter a vast diversity of people and tasks, receiving supporting materials, private documents, and both implicit and explicit feedback. Occasionally, they even get real-world feedback when users return to iterate on previous interactions.

Taking a role of assistant LLMs are primed to learn from the outcomes of their actions, scaling across many people. Thus they can learn from our collective feedback signals over time.

Yes, that uses a lot of human in the loop, not just real world in the loop, but humans are also dependent on culture and society, I see no need for AI to be able to do it without society. I actually think that AGI will be a collective/network of humans and AI agents, this perspective fits right in. AI will be the knowledge and experience flywheel of humanity.

To what extent do you know this to be true? Can you describe the mechanism that is used?

I would contrast your statement with cases where chat gpt generated something, I read it and note various incorrect things and then walk away. Further, there are cases where the human does not realize there are errors. In both cases I'm not aware of any kind of feedback loop that would even be really possible - i never told the LLM it was wrong. Nor should the LLM assume it was wrong because I run more queries. Thus, there is no signal back that the answers were wrong.

Hence, where do you see the feedback loop existing?

Like, for example, a developer working on a project, will iterate many times, some codes generated by AI might generate errors, they will discuss that with the model to fix the code. This way the model gets not just one round interactions, but multi-round with feedback.

> I read it and note various incorrect things and then walk away.

I think the general pattern will be people sticking with the task longer when it fails, trying to solve it with persistence. This is all aggregated over a huge number of sessions and millions of users.

In order to protect privacy we could only train preference models from this feedback data, and then fine-tune the base model without using the sensitive interaction logs directly. The model would learn a preference for how to act in specific contexts, but not remember the specifics.

Outside of repetitive genres like CRUD-apps, most software projects are significantly unique, even if they re-use learnt developer skills - it's like Chollet's ARC test on mega-steroids, with dozens/hundreds of partial-solution design techniques, and solutions that require a hierarchy of dozens/hundreds of these partial-solutions (cf Chollet core skills, applied to software) to be arranged into a solution in a process of iterative refinement.

There's a reason senior software developers are paid a lot - it's not just economically valuable, it's also one of the more challenging cognitive skills that humans are capable of.

This is where I quibble. Accurately detecting someone is actually still on the same task (indicating they are not satisfied) is perhaps as challenging as generating any answer to begin with.

That is why I also mentioned when people don't know the result was incorrect. That'll potentially drive a strong "this answer was correct" signal.

So, during development of a tool, I can envision that feedback loop. But something simply presented to millions, without a way to determine false negatives nor false positives-- how exactly does that feedback loop work?

edit We might be talking past each other. I did not quite read that "discuss with AI" part carefully. I was picturing something like copilot or chat got, where it is pretty much: 'here is your answer's

Even with an interactive AI, how to account for positive negatives (human accepts wrong answer), or when human simply gives up (which also looks like a success). If we told AI evertime it was wrong or right - can that scale to the extent it would actually train a model?

Some domains have better versions of this than others (eg theorem provers in math precisely indicate when you've succeeded)

Incidentally, lean could add a search like feature to help human researchers, and this would advance ai progress on math as well

Where tree search might make more sense applied to LLMs is for more coarser grained reasoning where the branching isn't based on alternate word continuations but on alternate what-if lines of thought, but even then it seems costs could easily become prohibitive, both for generation and evaluation/pruning, and using such a biased approach seems as much to fly in the face of the bitter lesson as be suggested by it.

Automated NAS has been tried for (highly constrained) image classifier design, before simpler designs like ResNets won the day. Doing this for billion parameter sized models would certainly seem to be prohibitively expensive.

Certainly compute to test ideas (at scale) is the limiting factor for LLM developments (says Sholto @ Google), but if we're talking moving beyond LLMs, not just tweaking them, then it seems we need more than architecture search anyways.

> Mixture of experts (MoE) is a machine learning technique where multiple expert networks (learners) are used to divide a problem space into homogeneous regions.[1] It differs from ensemble techniques in that for MoE, typically only one or a few expert models are run for each input, whereas in ensemble techniques, all models are run on every input.

[1] https://en.wikipedia.org/wiki/Mixture_of_experts

The way MoE does this is by having multiple alternate parallel paths through some parts of the model, together with a routing component that decides which path (one only) to send each token through. These paths are the "experts", but the name doesn't really correspond to any intuitive notion of expert. So, rather than having 1 path with N parameters, you have M paths (experts) each with N parameters, but each token only goes through one of them, so number of FLOPs is unchanged.

With tree search, whether for a game like Chess or potentially LLMs, you are growing a "tree" of all possible alternate branching continuations of the game (sentence), and keeping the number of these branches under control by evaluating each branch (= sequence of moves) to see if it is worth continuing to grow, and if not discarding it ("pruning" it off the tree).

With Chess, pruning is easy since you just need to look at the board position at the tip of the branch and decide if it's a good enough position to continue playing from (extending the branch). With an LLM each branch would represent an alternate continuation of the input prompt, and to decide whether to prune it or not you'd have to pass the input + branch to another LLM and have it decide if it looked promising or not (easier said than done!).

So, MoE is just a way to cap the cost of running a model, while tree search is a way to explore alternate continuations and decide which ones to discard, and which ones to explore (evaluate) further.

This confuses me. Positions that seem like they could be losing (but haven’t lost yet) could become winning if you search deep enough.

I was just trying to give the flavor of it.

Chess engines typically assume that the opponent plays to the best of their abilities, don't they?

To do that, the LLM would have to have some notion of "lines of thought". They don't. That is completely foreign to the design of LLMs.

Do you believe that there will be a "general AI" breakthrough? I feel as though you have expressed the reason I am so skeptical of all these AI researchers who believe we are on the cusp of it (what "general AI" means exactly never seems to be very well-defined)

Since I wrote about this, I would say that OpenAI's directional struggles are some confirmation of my hypothesis.

summary: I believe that AGI is possible but will take multiple unknown breakthroughs on an unknown timeline, but most likely requires long-term concerted effort with much less immediate payoff than pursuing narrow superhuman AI, such that serious efforts at AGI is not incentivized much in capitalism.

NB: I agree; I think AGI will first be achieved with genetic engineering, which is a path of way lesser resistance than using silicon hardware (which is probably a century plus off at the minimum from being powerful enough to emulate a human brain).

If I get hungry for example, my brain will generate a plan to satisfy that hunger. The search process and the evaluation happen in the same place, my brain.

400M years ago you had fish and arthropods, even dumber than dinosaurs.

Brain size grows as intelligence grows, the smarter you are the more use you have for compute so the bigger your brain gets. It took a really long time for intelligence to develop enough that brains as big as mammals were worth it.

I'd assume that being a generalist drove intelligence. It may have started with warm bloodedness and feathers/fur and further boosted in mammals with milk production (& similar caring for young by birds) - all features that reduce dependence on specific environmental conditions and therefore expose the species to more diverse environments where intelligence becomes valuable.

The idea that humans aren't the only way of producing human-level intelligence is taken as a given in many academic circles, but we don't really have any reason to believe that. It's an article of faith (as is its converse – but the converse is at least in-principle falsifiable).

You need to pay people, and they use a bunch of energy commuting, living in air conditioned homes, etc. which has nothing to do with powering the brain.

What’s the point of this statement? You know that IVF has nothing to do with artificial intelligence (as in intelligent machines). Did you just want to sound smart?

It is related because the goal of all of this is to create human level intelligence or better.

And that is a probable way to do it, instead of these other, less established methods that we don't know if they will work or not.

Which was the point of the post that you were responding to, if you actually read it.

There is an assumption that language is good enough at representing our world for these agents to effectively search over and come up with novel & useful ideas. Feels like an open question but: What do these LLMs know? Do they know things? Researchers need to find out! If current LLMs' can simulate a rich enough world model, search can actually be useful but if they're faking it, then we're just searching over unreliable beliefs. This is why video is so important since humans are proof we can extract a useful world model from a sequence of images. The thing about language and chess is that the action space is effectively discrete so training generative models that reconstruct the entire input for the loss calculation is tractable. As soon as we move to video, we need transformers to scale over continuous distributions making it much harder to build a useful predictive world model.

[0]: https://arxiv.org/abs/2306.02572

Thought experiment: should a machine with those structural faults be allowed to bootstrap itself towards greater capabilities on that shaky foundation? What would the impact of a near-human/superhuman intelligence that has occasional psychotic breaks it is oblivious of?

I'm critical of the idea of super-intelligence bootstrapping off LLMs (or even LLMs with search) - I figure the odds of another AI winter are much higher than those of achieving AGI in the next decade.

This approach is not that far from what TFA is getting at with the stockfish comeback. Banking on pure stats or pure logic are both kind of obviously dead ends for having real progress instead of toys. Banking on poorly understood emergent properties of one system to compensate for the missing other system also seems silly.

Sadly though, whoever is working on serious hybrid systems will probably not be very popular in either of the rather extremist communities for pure logic or pure ML. I’m not exactly sure why folks are ideological about such things rather than focused on what new capabilities we might get. Maybe just historical reasons? But thus the fallout from last AI winter may lead us into the next one.

Science is out the window. Groupthink and salesmanship are running the show right now. There would be a real irony to it if we find out the whole AI industry drilled itself into a local minimum.

Nah, surely Nvidia's market cap as the main shovel-seller in the 2022 - 2026(?) gold-rush being bigger than the whole French economy is well-reasoned and has a fundamentally solid basis.

That is not true. I work in logic-based AI (a form of machine learning where everything, examples, learned models, and inductive bias, is represented as logic programs). I am not against hybrid systems and the conference of my field, the International Joint Conferences of Learning and Reasoning included NeSy the International Conference on Neural-Symbolic Learning and Reasoning (and will again, from next year, I believe). Statistical machine learning approaches and hybrid approaches are widespread in the literature of classical, symbolic AI, such as the literature on Automated Planning and Reasoning, and you need only take a look at the big symbolic conferences like AAAI, IJCAI, ICAPS (planning) and so on to see that there is a substantial fraction of papers on either purely statistical, or neuro-symbolic approaches.

But try going the other way and searching for symbolic approaches in the big statistical machine learning conferences: NeurIPS, ICML, ICLR. You may find the occasional paper from the Statistical Relational Learning community but that's basically it. So the fanaticism only goes one way: the symbolicists have learned the lessons of the past and have embraced what works, for the sake of making things, well, work. It's the statistical AI folks who are clinging on to doctrine, and my guess is they will continue to do so, while their compute budgets hold. After that, we'll see.

What's more, the majority of symbolicists have a background in statistical techniques- I for example, did my MSc in data science and let me tell you, there was hardly any symbolic AI in my course. But ask a Neural Net researcher to explain to you the difference between, oh, I don't know, DFS with backtracking and BFS with loop detection, without searching or asking an LLM. Or, I don't know, let them ask an LLM and watch what happens.

Now, that is a problem. The statistical machine learning field has taken it upon itself in recent years to solve reasoning, I guess, with Neural Nets. That's a fine ambition to have except that reasoning is already solved. At best, Neural Nets can do approximate reasoning, with caveats. In a fantasy world, which doesn't exist, one could re-discover sound and complete search algorithms and efficient heuristics with a big enough neural net trained on a large enough dataset of search problems. But, why? Neural Nets researchers could save themselves another 30 years of reinventing a wheel, or inventing a square wheel that only rolls on Tuesdays, if they picked up a textbook on basic Computer Science or AI (Say, Russel and Norvig, that it seems some substantial minority think as a failure because it didn't anticipate neural net breakthroughs 10 years later).

AI has a long history. Symbolicists know it, because they, or their PhD advisors, were there when it was being written and they have the facial injuries to prove it from falling down all the possible holes. But, what happens when one does not know the history of their own field of research?

In any case, don't blame symbolicists. We know what the statisticians do. It's them who don't know what we've done.

>> So the fanaticism only goes one way: the symbolicists have learned the lessons of the past and have embraced what works, for the sake of making things, well, work. It's the statistical AI folks who are clinging on to doctrine, and my guess is they will continue to do so, while their compute budgets hold. After that, we'll see.

I don’t think the compute budgets will hold for long enough to make their dream of intelligence emerging from a random bundles of edges and nodes to come to a reality. I’m hoping it comes to an end sooner rather than later

More importantly; HAL was given control over the entire ship and was assumed to be without fault when the ship's systems were designed. It's an important distinction, because it wouldn't be dangerous if he was intelligent, crazy, and trapped in Dave's iPhone.

2001 is a pretty dark movie all things considered, and I don't think humanizing or elevating HAL would change the events of the film. AI is going to be objectified and treated as subhuman for as long as it lives, AGI or not. And instead of being nice to them, the technologically correct solution is to anticipate and reduce the number of AI-based system failures that could transpire.

https://aeon.co/essays/your-brain-does-not-process-informati...

> The information processing (IP) metaphor of human intelligence now dominates human thinking, both on the street and in the sciences. There is virtually no form of discourse about intelligent human behaviour that proceeds without employing this metaphor, just as no form of discourse about intelligent human behaviour could proceed in certain eras and cultures without reference to a spirit or deity. The validity of the IP metaphor in today’s world is generally assumed without question.

> But the IP metaphor is, after all, just another metaphor – a story we tell to make sense of something we don’t actually understand. And like all the metaphors that preceded it, it will certainly be cast aside at some point – either replaced by another metaphor or, in the end, replaced by actual knowledge.

> If you and I attend the same concert, the changes that occur in my brain when I listen to Beethoven’s 5th will almost certainly be completely different from the changes that occur in your brain. Those changes, whatever they are, are built on the unique neural structure that already exists, each structure having developed over a lifetime of unique experiences.

> no two people will repeat a story they have heard the same way and why, over time, their recitations of the story will diverge more and more. No ‘copy’ of the story is ever made; rather, each individual, upon hearing the story, changes to some extent

Now thats no proof, but I dont see any reason to think human intelligence isnt "computable".

That said, we still can't simulate nematode brains accurately enough to reproduce their behavior so there is a lot of research to go before we get to that "actual knowledge".

The Church Turing thesis is about computation. While the human brain is capable of computing, it is fundamentally not a computing device -- that's what the article I linked is about. You can't throw in all the paintings before 1872 into some algorithm that results in Impression, soleil levant. Or repeat the same but with 1937 and Guernica. The genes of the respective artists, the expression of those genes created their brain and then the sum of all their experiences changed it over their entire lifetime leading to these masterpieces.

(Even more: If you have a quantum computer, all known physics is efficiently computable.)

I'm not quite sure what your sentence about some obscure pieces of visual media is supposed to say?

If you give the same prompt to ChatGPT twice, you typically don't get the same answer either. That doesn't mean ChatGPT ain't computable.

This isn't known to be true. Simplifications of the standard model are known to be efficiently computable on a quantum computer, but the full model isn't.

Granted, I doubt this matters for simulating systems like the brain.

You could say the same about an analogue tape recording. Doesn't mean that we can't simulate tape recorders with digital computers.

The (probably correct) assumed fact that the brain isn't a computer, doesn't preclude the possibility of a program to have AGI. A powerful enough computer could simulate a brain and use the simulation to perform tasks requiring general intelligence.

This analogy falls even more apart when you consider LLMs. They also are not Turing machines. They obviously only reside within computers, and are capable of _some_ human-like intelligence. They also are not well described using the IP metaphor.

I do have some contention (after reading most of the article) about this IP metaphor. We do know, scientifically, that brains process information. We know that neurons transmit signals and there are mechanisms that respond non-linearly to stimuli from other neurons. Therefore, brains do process information in a broad sense. It's true that brains have a very different structures to Von-Neuman machines and likely don't store, and process information statically like they do.

Of course they are, everything that runs on a present day computer is a Turing machine.

> They obviously only reside within computers, and are capable of _some_ human-like intelligence.

They so obviously are not. As Raskin put it, LLMs are essentially a zero day on the human operating system. You are bamboozled because it is trained to produce plausible sentences. Read Thinking Fast And Slow why this fools you.

This guy in particular made his name with wild speculation about How Creativity Works during the 80s when it was more of a frontier. Now he's lived long enough to see a world where people that have never heard of him or his theories made computers into at least somewhat competent artists/poets without even consulting him. He's retreating towards mysticism because he's mad that his "formal and learned" theses about stuff like creativity have so little apparent relevance to the real world.

I found it hard to follow and I am an AI practitioner. Could someone please explain more what could the OP mean?

To me it seems that the flavor of search in the context of chess engines (look several moves ahead) is possible precisely because there’s an objective function that can be used to rank results, i.e. which potential move is “better” and this is more often than not a unique characteristic of reinforcement learning. Is there even such a metric for LLMs?

As a guess, could it mean that you would run the model forward a few tokens for each of its top predicted tokens, keep track of which branch is performing best against the training data, and then use that information somehow in training? But search is supposed to make things more efficient at inference time and this thought doesn't do that...

The other thing is that LLM inference isn't cheap. The trade off between inference costs and training costs seems to be very application specific. I suppose that there are domains where accepting 100x or 1000x inference costs vs 10x training costs makes sense, maybe?

On chess specifically, my takeaway is that the branching factor in chess never gets so high that a breadth-first approach is unworkable. The median branching factor (i.e. the number of legal moves) maxes out at around 40 but generally stays near 30. The most moves I have ever found in any position from a real game was 147, but at that point almost every move is checkmate anyways.

Creating superhuman go engines was a challenge for a long time because the branching factor is so much larger than chess.

Since MCTS is less thorough, it makes sense that a full search could find a weakness and exploit it. To me, the question is whether we can apply breadth-first approaches to larger games and situations, and I think the answer is clearly no. Unlike chess, the branching factor of real-world situations is orders of magnitude larger.

But also unlike chess, which is highly chaotic (small decisions matter a lot for future state), most small decisions don't matter. If you're flying from NYC to LA, it matters a lot if you drive or fly or walk. It mostly doesn't matter if you walk out the door starting with your left foot or your right. It mostly doesn't matter if you blink now or in two seconds.

This is of course based on the outputs of actual models that are only so smart, so a tree search that considers all possibly relevant ideas is going to have a larger amount of branches. Considering how many branches would be pruned to maintain grammatical correctness, my guess is that the token-level branching factor would be around 30. It could be up to around 300, but I highly doubt that it's larger than that.

"Search" is a generalization of "generate and test" and rejection sampling. It's classic AI. Back before the dot-com era, I took an intro to AI course and we learned about writing programs to do searches in Prolog.

The speed depends on how long it takes to generate a candidate, how long it takes to test it, and how many candidates you need to try. If they are slow, it will be slow.

An example of "human in the loop" rejection sampling is when you use an image generator and keep trying different prompts until you get an image you like. But the loop is slow due to how long it takes to generate a new image. If image generation were so fast that it worked like Google Image search, then we'd really have something.

Theorem proving and program fuzzing seem like good candidates for combining search with LLM's, due to automated, fast, good evaluation functions.

And it looks like Google has released a fuzzer [1] that can be connected to whichever LLM's you like. Has anyone tried it?

[1] https://github.com/google/oss-fuzz-gen

The problem with that is that search procedures and "evaluation functions" known to e.g. the theorem proving or planning communities are already at the limit of what is theoretically optimal, so what you need is not a new evaluation or search procedure but new maths, to know that there's a reason to try in the first place.

Take theorem proving, as a for instance (because that's my schtick). SLD-Resolution is a sound and complete automated theorem proving procedure for inductive inference that can be implemented by Depth-First Search, for a space-efficient implementation (but is susceptible to looping on left-recursions), or Breadth-First Search with memoization for a time-efficient implementation (but comes with exponential space complexity). "Evaluation functions" are not applicable- Resolution itself is a kind of "evaluation" function for the truth, or you could say the certainty of truth valuations, of sentences in formal logic; and, like I say, it's sound and complete, and semi-decidable for definite logic, and that's the best you can do short of violating Church-Turing. You could perhaps improve the efficiency by some kind of heuristic search (people for example have tried that to get around the NP-hardness of subsumption, an important part of SLD-Resolution in practice) which is where an "evaluation function" (i.e. a heuristic cost function more broadly) comes in, but there are two problems with this: a) if you're using heuristic search it means you're sacrificing completeness, and, b) there are already pretty solid methods to derive heuristic functions that are used in planning (from relaxations of a planning problem).

The lesson is: soundness, completeness, efficiency; choose two. At best a statistical machine learning approach, like an LLM, will choose a different two than the established techniques. Basically, we're at the point where only marginal gains, at the very limits of overall performance can be achieved when it comes to search-based AI. And that's were we'll stay at least until someone comes up with better maths.

Trying more promising branches first improves efficiency in cases where you guess right, and wouldn’t sacrifice completeness if you would eventually get to the less promising choices. But in the case of something like a game engine, there is a deadline and you can’t search the whole tree anyway. For tough problems, it’s always a heuristic, incomplete search, and we’re not looking for perfect play anyway, just better play.

So for games, that trilemma is easily resolved. And who says you can’t improve heuristics with better guesses?

But in a game engine, it gets tricky because everything is a performance tradeoff. A smarter but slower evaluation of a position will reduce the size of the tree searched before the deadline, so it has to be enough of an improvement that it pays for itself. So it becomes a performance tuning problem, which breaks most abstractions. You need to do a lot of testing on realistic hardware to know if a tweak helped.

And that’s where things stood before AlphaGo came along and was able to train slower but much better evaluation functions.

The reason for evaluation functions is that you can’t search the whole subtree to see if a position is won or lost, so you search part way and then see if it looks promising. Is there anything like that in theorem proving?

For discovery and reasoning??? Not too sure.

That's a common framing but it's wrong. Leela -and all its friends- have another piece of chess-specific knowledge that is indispensable to their performance: they have a representation of the game of chess -a game-world model- as a game tree, divided in plys: one ply for each player's turn. That game tree is what is searched by adversarial search algorithms, such as minimax or Monte Carlo Tree Search (MCTS; the choice of Leela, IIUC).

More precisely modelling a game as a game tree applies to many games, not just chess, but the specific brand of game tree used in chess engines applies to chess and similar, two-person, zero-sum, complete information board games. I do like my jargon! For other kinds of games, different models, and different search algorithms are needed, e.g. see Poker and Libratus [1].

The need for such a game tree, such a model of a game world, is currently impossible to go without, if the target is superior performance. The article mentions no-search algorithms and briefly touches upon their main limitation (i.e. "why?").

All that btw is my problem with the Bitter Lesson: it is conveniently selective with what it considers domain knowledge (i.e. a "model" in the sense of a theory). As others have noted, e.g. Rodney Brooks [2], Convolutional Neural Nets have dominated image classification thanks to the use of convolutional layers to establish positional invariance. That's a model of machine vision invented by a human, alright, just as a game-tree is a model of a game invented by a human, and everything else anyone has ever done in AI and machine learning is the same: a human comes up with a model, of a world, of an environment, of a domain, of a process, then a computer calculates using that model, and sometimes even outperforms humans (as in chess, Go, and friends) or at the very least achieves results that humans cannot match with hand-crafted solutions.

That is a lesson to learn (with all due respect to Rich Sutton). Human model + machine computation has solved every hard problem in AI in the last 80 years. And we have no idea how to do anything even slightly different.

____________________

[1] https://en.wikipedia.org/wiki/Libratus

[2] https://rodneybrooks.com/a-better-lesson/

It will come eventually. We live in exciting times.

  1) written words,
  2) models of math / RL / materials science,
  3) some smaller, formalized space like the game space of chess,

I've been building branching and evolving type llm systems for well over a year now full time.

I have built multiple "search" or "exploring" algorithms. The issue is that after multiple steps, your original agent, who was tasked with researching or doing biology, is now talking about battleships (an actual example from my previous work).

Single step is the only real situation search functions work. Mutli step agents explode to infinite possibilities very very quickly.

Single step has its own issues, though. While a zero shot question run 1000 times (eg, solve this code problem), may help find a better solution it's a limited search space (which is a good thing)

I recently ran a test of 10k inferences of a single input prompt on multiple llm models varying the input configurations. What you find is that an individual prompt does not have infinite response possibilities. It's limited. This is why they can actually function as llms now.

Agents not working is an example of this problem. While a single step search space is massive, it's exponential every step the agent takes.

I'm building tools and systems around solving this problem, and to me, a massive search is as far off as saying all we need 100x AI model sizes to solve it.

Autonomy =/ (Intelligence or reasoning)

But wait… An LLM can’t bake cheesecakes, nor if it could would it be able to evaluate their deliciousness.

Until AI can solve the “God Cheesecake” problem, I propose we all just calm down a bit about AGI

I don't know whether current techniques will be enough to 'write a really, really good short story', but I'm willing to bet we'll get there soon enough (whether that'll involve new techniques or not).

The baking part is robotics, which is less fair but kinda doable already.

Yes, because different people like different cheesecakes. “The best cheesecake of all time” is ill-defined to begin with; it is extremely unlikely that 100 people will all agree that one cheesecake recipe is the best they’ve ever tasted. Some people like a softer cheesecake, some firmer, some more acidic, some creamier.

Setting that problem aside—assuming there exists an objective best cheesecake, which is of course an absurd assumption—the field of experimental design is about a century old and will do a better job than an LLM at honing in on that best cheesecake.

Then you can get a panel of 100 people and bake 100 cheesecakes for them.

First, his example of the efficiency of ML+search for playing Chess is interesting but not a proof that this strategy would be applicable or efficient in the general domain.

Second, he is implying that some next iteration of ChatGPT will reach AGI level, given enough scale and money. This should be considered hypothetical until proven.

Overall, he should be more scientific and prudent.

I’m also very interested in this.

I’m at the stage where I’m exploring how to represent such a model/simulator.

The world isn’t brittle, so representing it as a code / graph probably won’t work.

The #1 canary for me: Why does training an LLM require so much data that we are concerned we might run out of it?

The clear lack of generalization and/or internal world modeling is what is really in the way of a self-bootstrapping AGI/ASI. You can certainly try to emulate a world model with clever prompting (here's what you did last, heres your objective, etc.), but this seems seriously deficient to me based upon my testing so far.

Also, I think this is instead time to sit back and think what exactly is the thing we value in society as well: Personal(Human) self-sufficiency(I also like to compare this AI to UBI) and thus achievement, which only means Human-in-Loop AI that can help us achieve that and that is specific to each individual, i.e. multi-atttribute value functions whose weights are learned and they change over time.

Writing about AGI and defining it to do the "best" search while not talking about what we want it to do *for us* is exactly wrong-headed for these reasons.

So think about the restrictions current LLMs have:

* They can't sit and think about an answer; they can "think out loud", but they have to start talking, and they can't go back and say, "No wait, that's wrong, let's start again."

* If they're composing something, they can't really go back and revise what they've written

* Sometimes they can look up reference material, but they can't actually sit and digest it; they're expected to skim it and then give an answer.

How would you perform under those circumstances? If someone were to just come and ask you any question under the sun, and you had to just start talking, without taking any time to think about your answer, and without being able to say "OK wait, let me go back"?

I don't know about you, but there's no way I would be able to perform anywhere close to what ChatGPT 4 is able to do. People complain that ChatGPT 4 is a "bullshitter", but given its constraints that's all you or I would be in the same situation -- but it's already way, way better than I could ever be.

Given its limitations, ChatGPT is phenomenal. So now imagine what it could do if it were given time to just "sit and think"? To make a plan, to explore the possible solution space the same way that Stockfish does? To take notes and revise and research and come back and think some more, before having to actually answer?

Reading this is honestly the first time in a while I've believed that some sort of "AI foom" might be possible.

I mean, technically, they could say that.

CoT answer Wait! No, that's not right: CoT...

"Given its limitations, ChatGPT is phenomenal." But this doesn't translate since it learned everything from data and there is no data on "sit and think".

[1] https://arxiv.org/abs/2406.07394

We can make them read digital media really quickly, but we can’t really accelerate its interactions with the physical world.

We don't know if a meaningfully superintelligent entity can exist. We don't understand the ingredients of intelligence that well, and it's hard to say how far the quality of these ingredients can be improved, to improve intelligence. For example, an entity with perfect pattern recognition ability, might be superintelligent, or just a little smarter than Terrance Tao. We don't know how useful it is to be better at pattern recognition to an arbitrary degree.

A common theory is that the ability modeling processes, like the behavior of the external world is indicative of intelligence. I think it's true. We also don't know the limitations of this modeling. We can simulate the world in our minds to a degree. The abstractions we use make the simulation more efficient, but less accurate. By this theory, to be superintelligent, an entity would have to simulate the world faster with similar accuracy, and/or use more accurate abstractions.

We don't know how much more accurate they can be per unit of computation. Maybe you have to quadruple the complexity of the abstraction, to double the accuracy of the computation, and human minds use a decent compromise that is infeasible to improve by a large margin. Maybe generating human level ideas faster isn't going to help because we are limited by experimental data, not by the ideas we can generate from it. We can't safely assume that any of this can be improved to an arbitrary degree.

We also don't know if AI research would benefit much from smarter AI researchers. Compute has seemed to be the limiting factor at almost all points up to now. So the superintelligence would have to help us improve compute faster than we can. It might, but it also might not.

This article reminds me of the ideas around the singularity, by placing too much weight on the belief that any trendline can be extended forever.

It is otherwise pretty interesting, and I'm excitedly watching the 'LLM + search' space.

For token generation, the branching factor depends on the tokenizer, but 32,000 is a common number.

Will search be as effective for LLMs when there are so many more possible branches?

The link for the site points to a notion.site address, but attempting to go to this address without javascript enabled (for that domain) forces a redirect to a notion.so domain. Attempting to visit just the basic notion.site address also does this same redirection.

What this ends up causing is that I don't have an easy way to use NoScript to temporarily turn on javascript for the notion.site domain, because it never loads. So much for reading this article.

You can't just give LLMs infinite compute time and expect them to find answers for like "cure cancer". Even chess moves that seem finite and success quantifiable are an also infinite problem and the best engines take "shortcuts" in their "thinking". It's impossible to do for real world problems.

This is a gross historical misunderstanding or misrepresentation.

Google accomplished this feat. Then the OS+academics reversed engineered/duplicated the study

How about using search to derive facts from ontological models, and then writing out the discovered facts in English. Then train the LLM on those English statements. Currently LLMs are trained on texts found on the internet mostly (only?). But information on the internet is often false and unreliable.

If instead we would have logically sound statements by the billions derived from ontological world-models then that might improve the performance of LLMs significantly.

Is something like this what the article or others are proposing? Give the LLM the facts, and the derived facts. Prioritize texts and statements we know and trust to be true. And even though we can't write out too many true statements ourselves, a system that generated them by the billions by inference could.

>If you train GPT-3 on a bunch of medical textbooks and prompt it to tell you a cure for Alzheimer's, it won't tell you a cure, it will tell you what humans have said about curing Alzheimer's ... It would just tell you a plausible story about a situation related to the prompt about curing Alzheimer's, based on its training data. Rather than a logical Oracle, this image-captioning-esque scheme would be an intuitive Oracle, telling you things that make sense based on associations already present within the training set.

>What am I driving at here, by pointing out that curing Alzheimer's is hard? It's that the designs above are missing something, and what they're missing is search. I'm not saying that getting a neural net to directly output your cure for Alzheimer's is impossible. But it seems like it requires there to already be a "cure for Alzheimer's" dimension in your learned model. The more realistic way to find the cure for Alzheimer's, if you don't already know it, is going to involve lots of logical steps one after another, slowly moving through a logical space, narrowing down the possibilities more and more, and eventually finding something that fits the bill. In other words, solving a search problem.

>So if your AI can tell you how to cure Alzheimer's, I think either it's explicitly doing a search for how to cure Alzheimer's (or worlds that match your verbal prompt the best, or whatever), or it has some internal state that implicitly performs a search.

https://www.lesswrong.com/posts/EMZeJ7vpfeF4GrWwm/self-super...

"If you train your logic machine on a bunch of medical textbooks and prompt it to tell you a cure for Alzheimer's, it won't tell you a cure, it will tell you what those textbooks have said about curing Alzheimer's."

Because I suspect not. GPT seems mostly limited to regurgitating+remixing what it read, but other algorithms with better logic could be able to essentially do a meta study: take the results from all Alzheimer's experiments we've done and narrow down the solution space to beyond what humans achieved so far. A human may not have the headspace to incorporate all relevant results at once whereas a computer might

Asking GPT to "think step by step" helps it, so clearly it has some form of this necessary logic, and it also performs well at "here's some data, transform it for me". It has limitations in both how good its logic is and the window across which it can do these transformations (but it can remember vastly more data from training than from the input token window, so perhaps that's a partial workaround). Since it does have both capabilities, it does not seem insurmountable to extend it: I'm not sure we can rule out that an evolution of GPT can find Alzheimer's cure within existing data, let alone a system even more suited to this task (still far short of needing AGI)

This requires the data to contain the necessary building blocks for a solution, but the quote seems to dismiss the option altogether even if the data did contain all information (but not yet the worked-out solution) for identifying a cure

With AI Research we don’t even know the shape of the states and transitions, or even if that’s an appropriate way to think about things.

Presently, a large language model (LLM) uses the same amount of computing resources for both simple and complex problems, which is seen as a drawback. Imagine if an LLM could adjust its computational effort based on the complexity of the task. During inference, it might then perform a sort of search across the solution space. The "search" mentioned in the article means just that, a method of dynamically managing computational resources at the time of testing, allowing for exploration of the solution space before beginning to "predict the next token."

At OpenAI Noam Brown is working on this, giving AI the ability to "ponder" (or "search"), see his twitter post: https://x.com/polynoamial/status/1676971503261454340

cmd + p -- bring up their own custom dialog. instead of printing the page as one would expect

There's a kernel of truth in there. See the papers on better results via monte carlo search trees (e.g. [1]). See mixture-of-LoRA/LoRA-swarm approaches. (I swear there's a startup using the approach of tons of domain-specific LoRAs, but my brain's not yielding the name)

Augmenting LLM capabilities via _some_ sort of cheaper and more reliable exploration is likely a valid path. It's not GPT-8 next year, though.

[1] https://arxiv.org/pdf/2309.03224

I mean there's no well defined "there" either.

It's a hand-waved notion that adding more intelligence (itself not very well defined, but let's use IQ) you get to something called "hyperintelligence", say IQ 1000 or IQ 10000, that has what can be described as magical powers, like it can convince any person to do anything, can invent things at will, huge business success, market prediction, and so on.

Whether intelligence is cummulative like that, or whether having it gets you those powers (aside from the succesful high IQ people, we know many people with IQ 145+ that are not inventing stuff left and right, or convincing people with some greater charisma than the average IQ 100 or 120 politician, but e.g. are just sad MENSA losers, whose greatest achievement is their test scores).

>Because if we allow for arbitrarily high (but still finite) amounts of compute, then some computable approximation of AIXI should work fine.

I doubt that too. The limit for LLMs for example is more human produced training data (a hard limit) than compute.

IQ has an issue that is inessential to the task at hand, which is how it is based on a population distribution. It doesn’t make sense for large values (unless there is a really large population satisfying properties that aren’t satisfied).

> I doubt that too. The limit for LLMs for example is more human produced training data (a hard limit) than compute.

Are you familiar with what AIXI is?

When I said “arbitrarily large”, it wasn’t for laziness reasons that I didn’t give an amount that is plausibly achievable. AIXI is kind of goofy. The full version of AIXI is uncomputable (it uses a halting oracle), which is why I referred to the computable approximations to it.

AIXI doesn’t exactly need you to give it a training set, just put it in an environment where you give it a way to select actions, and give it a sensory input signal, and a reward signal.

Then, assuming that the environment it is in is computable (which, recall, AIXI itself is not), its long-run behavior will maximize the expected (time discounted) future reward signal.

There’s a sense in which it is asymptotically optimal across computable environments (... though some have argued that this sense relies on a distribution over environments based on the enumeration of computable functions, and that this might make this property kinda trivial. Still, I’m fairly confident that it would be quite effective. I think this triviality issue is mostly a difficulty of having the right definition.)

(Though, if it was possible to implement practically, you would want to make darn sure that the most effective way for it to make its reward signal high would be for it to do good things and not either bad things or to crack open whatever system is setting the reward signal in order for it to set it itself.)

(How it works: AIXI basically enumerates through all possible computable environments, assigning initial probability to each according to the length of the program, and updating the probabilities based on the probability of that environment providing it with the sequence of perceptions and reward signals it has received so far when the agent takes the sequence of actions it has taken so far. It evaluates the expected values of discounted future reward of different combinations of future actions based on its current assigned probability of each of the environments under consideration, and selects its next action to maximize this. I think the maximum length of programs that it considers as possible environments increases over time or something, so that it doesn’t have to consider infinitely many at any particular step.)

That's still a training set, just by another name.

And with the environment being the world we live in, it would be constrained by the local environment's possible states, the actions it can perform to get feedback on, and the rate of environment's response (the rate of feedback).

Add the quick state-space inflation in what it is considering, and it's an even tougher deal than getting more training data for an LLM.

>The cool thing about using modern LLMs as an eval/policy model is that their RLHF propagates throughout the search.

>Moreover, if search techniques work on the token level (likely), their thoughts are perfectly interpretable.

I suspect a search world is substantially more alignment-friendly than a large model world. Let me know your thoughts!

Mobile Safari, phone set to french.

The obvious way to incorporate good search is to have extremely fast models that are being used in the search interior loop. Such models would be inherently less general, and likely trained on the specific problem or at least domain-- just for performance sake. The lesson in this article was that a tiny superspecialized model inside a powerful transitional search framework significantly outperformed a much larger more general model.

Use of explicit external search should make the optimization system's behavior and objective more transparent and tractable than just sampling the output of an auto-regressive model alone. If nothing else you can at least look at the branches it did and didn't explore. It's also a design that's more easy to bolt in varrious kinds of regularizes, code to steer it away from parts of the search space you don't want it operating in.

The irony of all the AI scaremongering is that if there is ever some evil AI with some LLM as an important part of its reasoning process if it is evil it may well be so because being evil is a big part of the narrative it was trained on. :D

Imagine if Leela (author's example) had been trained on every chess board position out there (I know it's mathematically impossible, but bear with me for a second). If Leela had been trained on every board position, it may have whupped Stockfish. So, adding "search" to Leela would have been pointless, since it would have seen every board position out there.

Today's LLMs are trained on every word ever written on the 'net, every word ever put down in a book, every word uttered in a video on Youtube or a podcast.

The problem is that for most of the interesting problems out there, it isn't possible to see every possibility let alone memorize it.

The whole point of deep learning is that the model learns to generalize. It's not to have a perfect storage engine with a human language query frontend.

Although to be clear I think actual reasoning systems are what we should be trying to create, and this LLM stuff seems like a cul-de-sac on that journey.