Adam Marblestone – AI is missing something fundamental about the brain

Core Question: How Does the Brain Achieve Superior Capabilities Despite Less Data?00:00

The big million-dollar question: How does the brain do it? We're throwing way more data at LLMs and they still have a small fraction of total human capabilities. This might be the quadrillion-dollar question, the most important question in science. The answer won't necessarily come from smart people thinking about it. Meta-level take: empower neuroscience technologically to crack this.

Key Components of Neural Systems00:30

Modern AI perspective: architecture, hyperparameters (e.g., number of layers), learning algorithm (backprop, gradient descent, or something else), initialization, cost functions (reward signal, loss functions, supervision signals).

Hunch: Neglected Role of Complex Loss Functions01:32

Field has neglected specific loss functions and cost functions. ML uses mathematically simple ones like predict next token, cross-entropy. Evolution built complexity into loss functions: many different ones for different brain areas, turned on at different development stages. Like Python code generating a specific curriculum for what different brain parts need to learn. Evolution encodes knowledge of successful/unsuccessful learning curricula.

Questions: Where do brain's loss functions come from? Can different loss functions lead to different learning efficiency?

Cortex as Universal Human Learning Algorithm02:31

Cortex has six-layered structure (physical layers of tissue, distinct from network layers). Areas connect to each other. Models attempt to explain how it approximates backprop. What cost function? Next token prediction, image classification, or what?