Greg Kyro

An exciting milestone for AI in science: Our C2S-Scale 27B foundation model, built with @Yale and based on Gemma, generated a novel hypothesis about cancer cellular behavior, which scientists experimentally validated in living cells.  With more preclinical and clinical tests, this discovery may reveal a promising new pathway for developing therapies to fight cancer.

Finally had a chance to listen through this pod with Sutton, which was interesting and amusing. As background, Sutton's "The Bitter Lesson" has become a bit of biblical text in frontier LLM circles. Researchers routinely talk about and ask whether this or that approach or idea is sufficiently "bitter lesson pilled" (meaning arranged so that it benefits from added computation for free) as a proxy for whether it's going to work or worth even pursuing. The underlying assumption being that LLMs are of course highly "bitter lesson pilled" indeed, just look at LLM scaling laws where if you put compute on the x-axis, number go up and to the right. So it's amusing to see that Sutton, the author of the post, is not so sure that LLMs are "bitter lesson pilled" at all. They are trained on giant datasets of fundamentally human data, which is both 1) human generated and 2) finite. What do you do when you run out? How do you prevent a human bias? So there you have it, bitter lesson pilled LLM researchers taken down by the author of the bitter lesson - rough! In some sense, Dwarkesh (who represents the LLM researchers viewpoint in the pod) and Sutton are slightly speaking past each other because Sutton has a very different architecture in mind and LLMs break a lot of its principles. He calls himself a "classicist" and evokes the original concept of Alan Turing of building a "child machine" - a system capable of learning through experience by dynamically interacting with the world. There's no giant pretraining stage of imitating internet webpages. There's also no supervised finetuning, which he points out is absent in the animal kingdom (it's a subtle point but Sutton is right in the strong sense: animals may of course observe demonstrations, but their actions are not directly forced/"teleoperated" by other animals). Another important note he makes is that even if you just treat pretraining as an initialization of a prior before you finetune with reinforcement learning, Sutton sees the approach as tainted with human bias and fundamentally off course, a bit like when AlphaZero (which has never seen human games of Go) beats AlphaGo (which initializes from them). In Sutton's world view, all there is is an interaction with a world via reinforcement learning, where the reward functions are partially environment specific, but also intrinsically motivated, e.g. "fun", "curiosity", and related to the quality of the prediction in your world model. And the agent is always learning at test time by default, it's not trained once and then deployed thereafter. Overall, Sutton is a lot more interested in what we have common with the animal kingdom instead of what differentiates us. "If we understood a squirrel, we'd be almost done". As for my take... First, I should say that I think Sutton was a great guest for the pod and I like that the AI field maintains entropy of thought and that not everyone is exploiting the next local iteration LLMs. AI has gone through too many discrete transitions of the dominant approach to lose that. And I also think that his criticism of LLMs as not bitter lesson pilled is not inadequate. Frontier LLMs are now highly complex artifacts with a lot of humanness involved at all the stages - the foundation (the pretraining data) is all human text, the finetuning data is human and curated, the reinforcement learning environment mixture is tuned by human engineers. We do not in fact have an actual, single, clean, actually bitter lesson pilled, "turn the crank" algorithm that you could unleash upon the world and see it learn automatically from experience alone. Does such an algorithm even exist? Finding it would of course be a huge AI breakthrough. Two "example proofs" are commonly offered to argue that such a thing is possible. The first example is the success of AlphaZero learning to play Go completely from scratch with no human supervision whatsoever. But the game of Go is clearly such a simple, closed, environment that it's difficult to see the analogous formulation in the messiness of reality. I love Go, but algorithmically and categorically, it is essentially a harder version of tic tac toe. The second example is that of animals, like squirrels. And here, personally, I am also quite hesitant whether it's appropriate because animals arise by a very different computational process and via different constraints than what we have practically available to us in the industry. Animal brains are nowhere near the blank slate they appear to be at birth. First, a lot of what is commonly attributed to "learning" is imo a lot more "maturation". And second, even that which clearly is "learning" and not maturation is a lot more "finetuning" on top of something clearly powerful and preexisting. Example. A baby zebra is born and within a few dozen minutes it can run around the savannah and follow its mother. This is a highly complex sensory-motor task and there is no way in my mind that this is achieved from scratch, tabula rasa. The brains of animals and the billions of parameters within have a powerful initialization encoded in the ATCGs of their DNA, trained via the "outer loop" optimization in the course of evolution. If the baby zebra spasmed its muscles around at random as a reinforcement learning policy would have you do at initialization, it wouldn't get very far at all. Similarly, our AIs now also have neural networks with billions of parameters. These parameters need their own rich, high information density supervision signal. We are not going to re-run evolution. But we do have mountains of internet documents. Yes it is basically supervised learning that is ~absent in the animal kingdom. But it is a way to practically gather enough soft constraints over billions of parameters, to try to get to a point where you're not starting from scratch. TLDR: Pretraining is our crappy evolution. It is one candidate solution to the cold start problem, to be followed later by finetuning on tasks that look more correct, e.g. within the reinforcement learning framework, as state of the art frontier LLM labs now do pervasively. I still think it is worth to be inspired by animals. I think there are multiple powerful ideas that LLM agents are algorithmically missing that can still be adapted from animal intelligence. And I still think the bitter lesson is correct, but I see it more as something platonic to pursue, not necessarily to reach, in our real world and practically speaking. And I say both of these with double digit percent uncertainty and cheer the work of those who disagree, especially those a lot more ambitious bitter lesson wise. So that brings us to where we are. Stated plainly, today's frontier LLM research is not about building animals. It is about summoning ghosts. You can think of ghosts as a fundamentally different kind of point in the space of possible intelligences. They are muddled by humanity. Thoroughly engineered by it. They are these imperfect replicas, a kind of statistical distillation of humanity's documents with some sprinkle on top. They are not platonically bitter lesson pilled, but they are perhaps "practically" bitter lesson pilled, at least compared to a lot of what came before. It seems possibly to me that over time, we can further finetune our ghosts more and more in the direction of animals; That it's not so much a fundamental incompatibility but a matter of initialization in the intelligence space. But it's also quite possible that they diverge even further and end up permanently different, un-animal-like, but still incredibly helpful and properly world-altering. It's possible that ghosts:animals :: planes:birds. Anyway, in summary, overall and actionably, I think this pod is solid "real talk" from Sutton to the frontier LLM researchers, who might be gear shifted a little too much in the exploit mode. Probably we are still not sufficiently bitter lesson pilled and there is a very good chance of more powerful ideas and paradigms, other than exhaustive benchbuilding and benchmaxxing. And animals might be a good source of inspiration. Intrinsic motivation, fun, curiosity, empowerment, multi-agent self-play, culture. Use your imagination.

Negative energy seems to (1) spread through human networks much more spontaneously than positive energy, and (2) increase social fragmentation — both of which are commensurate with an increase in Shannon entropy. I wonder if this has to do with the 2nd law of thermodynamics. Of course physics isn’t the proper level of resolution with which to interpret information propagation through human networks — but if you concede that information is more fundamental than physical laws, the idea becomes slightly less ridiculous.

There isn’t a lack of biologists interested in entering the AI x Bio space — it’s generally easier for a computer scientist to learn enough about a biological problem space to formalize it into a model than for a biologist to learn the mathematical and computational abstractions of ML

At some point over the last few hundred years, wisdom and intelligence decoupled. Modern-day intelligence seems to be largely IQ-dependent — raw processing speed, pattern recognition, etc. Socrates, Marcus Aurelius, Plato, Aristotle, Kierkegaard, Tolstoy, Dostoevsky — likely had high IQs, but IQ alone doesn’t capture, or seem to be the primary driver of, their most significant mental outputs. This separate form of intelligence — the kind responsible for their most enduring insights — seems to have something to do with radical self-reflection and recognition of a higher power. They were aligned. Aspiration toward that form of alignment seems rare today.

I’ve noticed that while sleep deprived, my cognition is much sharper if I’ve taken creatine (5-10g) — an effect supported by this study: nature.com/articles/s4159… Curious whether the effect compounds with caffeine — a 2x2 factorial study would be valuable: control vs. creatine vs. caffeine vs. both. Have you ever experimented with the combination?

T-ALPHA: A Hierarchical Transformer-Based Deep Neural Network for Protein-Ligand Binding Affinity Prediction With Uncertainty-Aware Self-Learning for Protein-Specific Alignment 1. Introducing T-ALPHA: A state-of-the-art hierarchical transformer designed to predict protein-ligand binding affinities with unprecedented accuracy. It integrates multimodal data and captures complex biochemical interactions for real-world drug discovery. 2. Major innovation: T-ALPHA achieves high performance even with predicted protein structures, eliminating dependency on experimental crystallography and broadening its applicability to challenging datasets. 3. Real-world impact: By focusing on protein-specific alignment using uncertainty-aware self-learning, T-ALPHA enhances target-specific binding predictions without requiring additional experimental data, saving time and resources. 4. Benchmark excellence: T-ALPHA outperforms all reported models on benchmarks like CASF 2016, LP-PDBbind, and newly designed test sets. It excels in ranking compounds for critical targets such as SARS-CoV-2 protease and EGFR. 5. Robust architecture: T-ALPHA processes protein, ligand, and protein-ligand complex data through three distinct channels using advanced techniques like E(n)-equivariant GNNs, quasi-geodesic convolutions, and sequence-based embeddings. 6. Multimodal integration: Leveraging cross-attention in transformers, T-ALPHA synthesizes diverse biochemical and spatial information, offering a unified and hierarchical representation for binding affinity prediction. 7. Scalable and efficient: T-ALPHA's hierarchical design supports distributed training, making it suitable for large datasets. Its self-learning mechanism aligns with proteins dynamically, adapting predictions to specific targets. 8. Future outlook: T-ALPHA sets a foundation for developing universal models for protein-ligand interactions, advancing precision medicine and accelerating drug discovery pipelines. @Gregory_Kyro 💻Code: github.com/gregory-kyro/T… 📜Paper: biorxiv.org/content/10.110… #AI #Bioinformatics #DrugDiscovery #ProteinStructure #TransformerModel