Adam Marblestone — AI is missing something fundamental about the brain

**Dwarkesh Patel** (0:00)
The big million-dollar question that I have that I've been trying to get the answer to through all these interviews with AI researchers, how does the brain do it, right? We're throwing way more data at these LLMs, and they still have a small fraction of the total capabilities that a human does. So what's going on?

**Adam Marblestone** (0:14)
Yeah, I mean, this might be the quadrillion-dollar question or something like that. It's arguably, you can make an argument this is the most important question in science. I don't claim to know the answer. I also don't really think that the answer will necessarily come even from a lot of smart people thinking about it as much as they are. My overall meta-level take is that we have to empower the field of neuroscience to just make neuroscience a more powerful field technologically and otherwise to actually be able to crack a question like this. But maybe the way that we would think about this now with modern AI, neural nets, deep learning, is that there are these certain key components of that. There is the architecture, there is maybe hyperparameters of the architecture, how many layers do you have, or properties of that architecture. There is the learning algorithm itself, how do you train it? Backprop, gradient descent, is it something else? There is, how is it initialized? If we take the learning part of the system, it still may have some initialization of the weights.
And then there are also cost functions, there's like, what is it being trained to do? What's the reward signal? What are the loss functions? Supervision signals. My personal hunch within that framework is that the field has neglected the role of this very specific loss functions, very specific cost functions. Machine learning tends to like mathematically simple loss functions, right? Predict the next token.
You know, cross entropy, these simple kind of computer scientists' loss functions. I think evolution may have built a lot of complexity into the loss functions. Actually, many different loss functions were different areas, turned on at different stages of development. A lot of Python code basically generating a specific curriculum for what different parts of the brain need to learn. Because evolution has seen many times what was successful and unsuccessful and evolution could encode the knowledge of the learning curriculum. So in the machine learning framework, maybe we can come back and we can talk about where do the loss functions of the brain come from? Can different loss functions lead to different efficiency of learning?

**Dwarkesh Patel** (2:31)
People say the cortex has got the universal human learning algorithm, the special status that humans have. What's up with that?

**Adam Marblestone** (2:37)
This is a huge question and we don't know. I've seen models where the cortex has typically this like six layered structure layers in a slightly different sense than layers of a neural net. It's like any one location in the cortex has six physical layers of tissue as you go in layers of the sheet and then those areas then connect to each other and that's more like the layers of a network. I've seen versions of that where what you're trying to explain is actually just how does it approximate back prop. What is the cost function for that? What is the network being asked to do? If you are trying to say it's something like back prop, is it doing back prop on next token prediction? Is it doing back prop on classifying images? Or what is it doing?
No one knows. But I think one thought about it, one possibility about it is that it's just this incredibly general prediction engine. So any one area of cortex is just trying to predict any, basically can it learn to predict any subset of all the variables it sees from any other subsets. So like omni-directional inference or omni-directional prediction. Whereas an LLM is just you see everything in the context window and then it computes a very particular conditional probability which is given all the last thousands of things, what is the very probabilities for all the next token.
But it will be weird for a large language model to say, the quick brown fox blank blank, the lazy dog and fill in the middle versus do the next token. If it's doing just forward, it can learn how to do that stuff in this emergent level of in-context learning, but natively is just predicting the next token. What if the cortex is just natively made so that it can, you know, any area of cortex can predict any pattern in any subset of its inputs given any other missing subset? That is a little bit more like, quote unquote, probabilistic AI. I think a lot of things I'm saying, by the way, are extremely similar to like what Yann LeCun would say. Yeah. He's really interested in these energy-based models. Something like that is like the joint distribution of all the variables. What is the likelihood or unlikelihood of just any combination of variables? If I clamp some of them, I say, well, definitely these variables are in these states, then I can compute with probabilistic sampling, for example, I can compute, okay, conditioned on these being set in this state, and these could be any arbitrary subset of variables in the model. Can I predict what any other subset is going to do and sample from any other subset given clamping this subset? And I could choose a totally different subset and sample from that subset. So it's omni-directional inference. And so that could be, there's some parts of cortex that might be like association areas of cortex, predict vision from audition.