Adam Marblestone: evaluation

Transcript context

…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? 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? This might be the quadrillion-dollar question or something like that. You can make an argument that this is the most important question in science. I don’t claim to know the answer. I also don’t 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. Maybe the way that we would think about this now with modern AI, neural nets, deep learning, is that there are certain key components of that. There’s the architecture. There’s maybe hyperparameters of how many layers 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? 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. 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 these very specific loss functions, very specific cost functions. Machine learning tends to like mathematically simple loss functions. Predict the next token, cross-entropy, these simple computer scientist loss functions. I think evolution may have built a lot of complexity into the loss functions actually, many different loss functions for 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. 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? People say the cortex has got the universal human learning algorithm, the special sauce that humans have. What’s up with that?…

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