Luis Lamb

#AI #Tech; PhD @imperialcollege; @MIT MBA;@mitsloan; CS @inf_ufrgs #neurosymbolicAI pioneer #AIStrategy Personal tweets

[Luis Lamb @luislamb]

On neural-symbolic computing: suggested readings on foundations of the field - by @AvilaGarcez @pascalhitzler @GaryMarcus Barbara Hammer and colleagues

[Luis Lamb @luislamb]

@NeurIPSConf A move in the right direction. Congratulations to the Track PC chairs and organisers. @NeurIPSConf

[NeurIPS Conference @NeurIPSConf]

This year, the NeurIPS 2026 Position Paper Track made the decision to require that all papers be substantially human-written, with AI used for only copy-editing or similar peripheral changes to the main text!

For more details, please check our blogpost: https://t.co/wrWuMQJwrx

[MIT CSAIL @MIT_CSAIL]

What really happens inside a computer chip?

MIT CSAIL built an operating system kernel called "Fractal" that gives researchers a clearer view. It revealed that the Apple M1 could be vulnerable to a class of speculative attack known as "Phantom": https://t.co/8TsVfIpXCC

[Association for Computing Machinery @TheOfficialACM]

Today in 1954, Turing Award recipient E. Allen Emerson was born. With Edmund Clarke & Joseph Sifakis, Emerson received the 2007 #ACMTuringAward for developing Model-Checking into a highly effective verification technology. https://t.co/YV2CzYfaRl

[Steven Strogatz @stevenstrogatz]

This terrific article by @sioroberts will help elevate the public discussion of AI in mathematics. The interview about the Leiden Declaration is especially helpful. https://t.co/u4SYxQVXZ2

[Luis Lamb @luislamb]

In Göd(el) we trust. VERITAS·INVICTA·LATET·IPSE·DETEXIT

[Richard Sutton @RichardSSutton]

A new and possibly controversial perspective:
In this video, I explain the sense in which generative AI trained by supervised learning is incapable of making novel discoveries.
https://t.co/zin5QbbT9N

The text of the speech:

AI Creativity and Discovery

Good day ladies and gentlemen. I regret that I am unable to be with you all today to engage in a back-and-forth discussion, but I am nevertheless pleased to be able to share with you, via this recording, some high-level thoughts about the current and future state of artificial intelligence, and in particular about AI’s relationship to science and mathematics, which is, as I understand it, the central focus of this meeting and of the SAIR Foundation.

I would like to start with an old joke; I am sure you have heard it before. It is the one about the researcher whose work is being evaluated, and the review comes back, and says “This work is both novel and good. Unfortunately, the parts that are good are not novel, and the parts that are novel are not good.”

My first point about AI is that this assessment applies exactly to large parts of AI as we know it today. Not all of today’s AI, but a large part of it. Pretty much all of what we mean by “Generative AI”---which includes large language models, and the images and video models, and even the new methods for learning world models. All of these AIs take large numbers of examples and produce a “model” which behaves similar to the examples, that is, which generates text like people, or images like artists or nature, and videos like we find on the internet. Don’t get me wrong, Generative AI can be extremely useful. No doubt about that. But the assessment of the joke still applies. These systems can produce output that is both novel and good, but not at the same time.

In many ways this is just absolutely not a problem. When we ask an AI for an answer from the internet, or to summarize a document, we don’t want it to be novel. We are happy if the quality of the answer, the goodness, comes from the source material—from the people who wrote the document or the articles on the internet. If the AI’s answer is novel it means it is going beyond the source material, adding something beyond it. This is what we call “hallucinations”. In most cases, we don’t like it when the AI makes something up, when it adds something novel.

One exception, of course, is when we are looking not for facts or reality, but for fiction and entertainment. We might ask for a bedtime story for a child, or an image based on existing images on the internet but which is nevertheless different and distinct from them. In these cases, it is never easy for us to know how creative the AI is actually being, as we do not know how close the AI’s story, poem, or image is to the source material. In a real practical sense we can not know this because the internet is too big, the possible sources that the AI may draw upon are too numerous.

When we ask for a fiction or novelty, the AI can give it to us because its processing is in part stochastic. Every decision can go multiple ways and will go different ways and produce a different trajectory every time. The trajectory can be random—and thus novel—or it can be based on the training data—and thus “good” because the training data is good, sourced from people or reality. Thus, the trajectory is either novel or good—based on randomness or based on data—but never both at the same time.

Really, I think it is okay if the output of Generative AI is never good and novel at the same time. For the researcher in the joke this is a devastating criticism, but for most things it is not, and for Generative AI it is not. Generative AI is meant to be a mimic. This is what supervised learning is for. Generative AI can be extremely useful, even when it just mimics, if it is faster, or cheaper, or smaller, or more customizable, or more copy-able, than the thing being mimicked. It is okay if Generative AI cannot be both novel and good at the same time. It is still a transformative technology.

But it is a limitation. And remember we are here to use AI for science and mathematics, and for these areas the assessment of the reviewer in the joke is devastating. For these areas we need true creativity and discovery. Generative AI—or Mimicking AI—will never get where us there. For these we need something more, and indeed we have something more in other parts of AI. We have many AI systems which can give us more. We have AlphaGo with its world-changing move 37, or AlphaZero with its brilliant original chess-playing style. We have GT-Sophy that drives simulated racecars better than any human. We have AlphaFold and AlphaProof and Claude-Code, which have brought true advances in science, mathematics, and programming. We have RL-Lyft which optimizes the assignment of cars to passengers in the ride-hailing business. All these systems have found things that are both novel and good. And, truth be told, some language models have been augmented in ways that make them more than Generative AI based on supervised learning.

All these systems have some additional features that make them capable of true creativity and true discovery. It is important for us to recognize what this is—and that it is not present in ordinary, garden-variety Generative AI. It is something that can not come from just supervised learning, from learning from examples. What is it? Well, it is a simple thing, a commonsense thing. It is not new. We have many names for it, but unfortunately none of them are very good names. I will call it Discovery. Basically, Discovery is just the idea of trying many things and seeing which of them work, then keeping those that worked the best. Evolution by natural selection works this way. The scientific method works this way. And just ordinary life and learning works this way. We try things and remember what works. What could be more obvious? In this behavioral case, psychology has two names for it— “instrumental learning” and “operant conditioning”—and in machine learning it is what we mean by “reinforcement learning”. We also see the idea of Discovery in planning and combinatorial search—anything that involves the idea of “generate and test”.

The essence of Discovery is to combine three steps:
1. Variation,
2. Evaluation, and
3. Selective retention.

Of course, I am not the first to say this. I am not the first to point out that this combination of steps is key to science, to evolution by natural selection, and to animal behavior. I think particularly of papers by Donald Campbell, by Daniel Dennett, and by Gary Cziko. What is new in my remarks is to directly relate the idea of Discovery to modern AI to help us see that it is not present in supervised learning or Generative AI—in particular, that Discovery is not present in backpropagation or gradient descent.

Let me say explicitly what is missing from Generative AI. As we have remarked, these systems do have a stochastic aspect, so they do generate a variety of trajectories and behavior. What is missing is the Evaluation step. The generator was pre-trained by supervised learning, leaving no way at runtime to Evaluate what it generates. And of course without Evaluation there can be no Selective retention, and thus no Discovery. The variation can bring novelty, but without evaluation there is no Discovery, and arguably, no creativity. That is, I would say that creativity requires that the new things generated be Evaluated. Without evaluation, and retention of the best, there is nothing created. The novelty flickers into existence but, if its value is unrecognized, it flickers away and is lost.

In many cases, Evaluation is done by people to make a discovery. As when we have Generative AI make many pictures for us, and then we pick the one that we like the best. The human+AI system completes the discovery.
In many other cases, the Evaluation comes from a clear objective. Some moves lead to checkmate, some steps lead to a proof, some actions result in high reward, some genotypes make more copies, some theories explain the data better.

Some prefer the Variation step to be called Blind variation, where “blind” here means that it is uninformed, a shot in the dark. It does not need to be completely uninformed; a good scientist does not select theories to test at random. But neither can it be completely informed and determined. There must be some uncertainty about where the answer lies in order for there to be a discovery. In practice, the variation is partly informed and partly blind, but it is the blind part that corresponds to the discovery.

Now let us briefly go all the way to modern deep learning, to the backpropagation algorithm. At first it might seem that backpropagation is incapable of discovery because it is deterministic and thus incapable of variation. But this is not correct. The weight updates of backprop are deterministic, but the weights are initialized to small random values. The random initialization is often downplayed, but in fact it is a necessary form of variation; it must be done properly to get good performance. In backprop this Variation is done once, at network initialization, so its effect is temporary, and later the network may lose its ability to learn. This is the weakness of deep learning that is alleviated with a new algorithm that my group presented in Nature a couple of years ago. Our “continual backpropagation” made one small change: every so often a less-used neuron would be re-initialized to small random weights. This allows the variation to continue and plasticity to be retained.

Although there is much more to be said about Creativity and Discovery, this is the key point: they are more than supervised learning, more than pattern recognition, more than prediction, and more than world modeling. Those things are important, but they alone will not bring us to discovery. Discovery requires Evaluation from a person or from an explicit goal, and only in the latter case will we attain full autonomy.

So that is my call to arms. If we want the full power of AI scientists, then we should share the goals with them so they can create, evaluate, discover, and in these ways fully participate in achieving the goals. Let’s be bold! Let’s fully automate Creativity and Discovery!

[MONTREAL.AI @Montreal_AI]

The data-efficiency gap between machines and children may not be solved by “more tokens.”

It may be solved by changing what the model is asked to predict.

A beautiful new paper by Daniel J. Korchinski, Alessandro Favero, and Matthieu Wyart gives a sample-complexity theory for a major alternative to token-level learning:

Learn from your own latents and not from tokens.

The premise is striking.

Modern generative models learn by predicting raw surface fragments:

next tokens
masked tokens
pixels
noise
patches

This works spectacularly.

But it is brutally data-hungry.

Biological learners do not see 10¹³–10¹⁴ tokens before acquiring rich language competence. So perhaps the bottleneck is not only architecture, scale, or optimization.

Perhaps it is the prediction target.

Instead of predicting tokens, methods like data2vec and JEPA train networks to predict their own latent representations of related views or masked regions.

The model is not asked:

“Can you reconstruct the surface?”

It is asked:

“Can you predict the abstraction your own system would form?”

That difference may be enormous.

The authors study a tractable hierarchical grammar that generates visible tokens from hidden latent trees of depth L — a stylized model of the compositional structure of language and images.

For this data, supervised learning and token-level self-supervised learning require samples exponential in L to recover the hidden hierarchy.

But latent prediction recovers the hierarchy with sample complexity essentially constant in L, up to logarithmic factors.

That is the whole paper in one line:

predicting tokens makes hierarchy expensive;
predicting latents makes hierarchy recursive.

Why?

Because token-level objectives keep forcing supervision through the visible surface. The deeper the hidden structure, the weaker and more indirect the signal becomes.

Latent prediction removes that bottleneck.

Once one level of abstraction is recovered, the model can use its own learned latents as both context and target for the next level. Every level becomes statistically like the first.

The paper confirms this three ways:

a hierarchical clustering algorithm

an end-to-end neural architecture trained by gradient descent

a sample-complexity analysis of data2vec, showing that it implicitly performs hierarchical latent prediction

The last point is especially interesting.

If data2vec already discovers hierarchy implicitly, then explicitly stacking methods like H-JEPA may be partly redundant.

This is not “tokens are dead.”

Token prediction remains one of the most productive ideas in AI.

But this paper gives a precise reason why token-level learning may be an inefficient path to latent structure.

The deeper lesson:

the model should not only learn from the world’s surface.

It should learn from the abstractions it is already beginning to form.

Full credit to the authors:
Daniel J. Korchinski, Alessandro Favero, Matthieu Wyart.

Paper:
Learn from your own latents and not from tokens: A sample-complexity theory
https://t.co/tzgnTvybm9

I’m attaching the first page because the abstract is worth reading closely.

The future of data-efficient AI may not be more reconstruction.

It may be recursive self-prediction in latent space.

#AIResearch #MachineLearning #SelfSupervisedLearning #RepresentationLearning #LLM #DataEfficiency

[The Scientific Lens @LensScientific]

The Road to Reality

"A Complete Guide to the Laws of the Universe"

By Roger Penrose

[Martin Ford @MFordFuture]

@MartinShkreli New edition publishing June 2

[Steven Strogatz @stevenstrogatz]

If you like thinking about what math can do for biology and vice versa, you might like this public talk I gave a year ago. It contains a lot of stories from my own life. "From Math to Bio and Back: Reflections on a Two Way Street" https://t.co/TzKE3jnK29

[MIT CSAIL @MIT_CSAIL]

"Anyone who has lost track of time when using a computer knows the propensity to dream, the urge to make dreams come true, & the tendency to miss lunch." — Tim Berners-Lee

[The Scientific Lens @LensScientific]

In 1961, meteorologist Edward Lorenz was tweaking a weather simulation on a bulky early computer at MIT. To save a bit of time on a rerun, he rounded one harmless-looking number in the printout from 0.506127 down to 0.506.

When he returned later, the forecast had gone totally insane. The two runs, which should have been almost identical, had completely diverged after just a few simulated days. That tiny rounding error had snowballed into chaos.

Lorenz had just discovered sensitive dependence on initial conditions, the heart of what we now call chaos theory.

The idea exploded into the butterfly effect and reshaped entire fields from weather forecasting to physics, biology, stock markets, and even why long-term predictions are so hard.

[Luis Lamb @luislamb]

Towards a Neural Lambda Calculus: Neurosymbolic AI Applied to the Foundations of Functional Programming - now formally published. w/ João Flach @afmoreira @luislamb Based on João's MS thesis.
att. @vardi @AvilaGarcez @GaryMarcus
https://t.co/GCpMlYIWWk

[Luis Lamb @luislamb]

Google DeepMind CEO Sir Demis Hassabis:

“Today’s systems, are nowhere near [AGI]. Doesn’t matter how many Erdős problems you solve… I think it’s far, far from what a true invention or someone like a Ramanujan would have been able to do”.

[François Chollet @fchollet]

Thinking of AI as a productivity booster for prior workflows is the wrong framing. Like all of the previous waves of computerization/softwarization, AI is a tool that lets you do new things in new ways.

[Luis Lamb @luislamb]

Google DeepMind CEO Sir Demis Hassabis:

“Today’s systems, are nowhere near [AGI]. Doesn’t matter how many Erdős problems you solve… I think it’s far, far from what a true invention or someone like a Ramanujan would have been able to do”.

[Dr Kareem Carr @kareem_carr]

I'm expecting automated theorem provers to be common in the near future, but you have to think about the economics of it, not just the raw technology.

[Ian Weissman, DO @DrIanWeissman]

In a study published in April, researchers in the U.S. and U.K. found that when people spent just 10 or so minutes using AI to help them solve math or reading-comprehension problems, their own unaided performance on the same types of problems diminished.
https://t.co/GN6tckqDP3

[Ash Paul @pash22]

Generative AI without guardrails can harm learning: Evidence from high school mathematics https://t.co/QnZEDgeYQ2 via @hamsabastani et al