Adam Marblestone: uncertainty

Transcript context

…This framework helps explain this mystery that people have pointed out and I’ve asked a few guests about, which is that if you want to analogize evolution to pretraining, well how do you explain the fact that so little information is conveyed through the genome? So 3 gigabytes is the size of the total human genome. Obviously a small fraction of that is actually relevant to coding the brain. Previously people made this analogy, that actually evolution has found the hyperparameters of the model, the numbers which tell you how many layers there should be, the architecture, basically, how things should be wired together. But if a big part of the story is that increased sample efficiency aids learning, generally makes systems more performant, is the reward function, is the loss function—and if evolution found those loss functions that aid learning—then it actually makes sense how you can build an intelligence with so little information. Because the reward function, in Python the reward function is literally a line. So you just have a thousand lines like this, and that doesn’t take up that much space. Yes. It also gets to do this generalization thing with the thing I was describing where we were talking about the spider, where it learns just the word “spider” which triggers the spider reflex or whatever. It gets to exploit that too. It gets to build a reward function that actually has a bunch of generalization in it just by specifying these innate spider stuff and the Thought Assessors, as Steve calls them, that do the learning. That’s potentially a really compact solution to building up these more complex reward functions too, that you need. It doesn’t have to anticipate everything about the future of the reward function. It just has to anticipate what variables are relevant and what are heuristics for finding what those variables are. And then it has to have a very compact specification for the learning algorithm and basic architecture of the Learning Subsystem. And then it has to specify all this Python code of all the stuff about the spiders and all the stuff about friends, and all the stuff about your mother, and all the stuff about mating and social groups and joint eye contact. It has to specify all that stuff. So is this really true? I think that there is some evidence for it. Fei Chen and Evan Macosko and various other researchers have been doing these single-cell atlases. One of the things that scaling up neuroscience technology—again, this is one of my obsessions—has done through the BRAIN Initiative, a big neuroscience funding program, is they’ve basically gone through different areas, especially of the mouse brain, and mapped where the different cell types are? How many different types of cells are there in different areas of cortex? Are they the same across different areas? Then you look at these subcortical regions, which are more like the Steering Subsystem or reward-function-generating regions. How many different types of cells do they have? And which neuron types do they have? We don’t know how they’re all connected and exactly what they do or what the circuits are or what they mean, but you can just quantify how many different kinds of cells there are with sequencing the RNA. And there are a lot more weird and diverse and bespoke cell types in the Steering Subsystem, basically, than there are in the Learning Subsystem. Like the cortical cell types, it seems like there’s enough to build a learning algorithm up there and specify some hyperparameters. And in this Steering Subsystem, there’s like a gazillion, thousands of really weird cells, which might be like the one for the spider flinch reflex and the one for I’m-about-to-taste-salt. Why would each reward function need a different cell type?…

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