Adam Marblestone: evaluation

Transcript context

…Okay, two sub-questions. One, it makes you wonder whether the thing that is lacking in artificial neural networks is less about the reward function and more about the encoder or the embedding… Maybe the issue is that you’re not representing video and audio and text in the right latent abstraction such that they could intermingle and conflict. Maybe this is also related to why LLMs seem bad at drawing connections between different ideas. Are the ideas represented at a level of generality at which you could notice different connections? Well, the problem is these questions are all commingled. If we don’t know if it’s doing a backprop-like learning, and we don’t know if it’s doing energy-based models, and we don’t know how these areas are even connected in the first place, it’s very hard to really get to the ground truth of this. But yeah, it’s possible. I think that people have done some work. My friend Joel Dapello actually did something some years ago where he put a model—I think it was a model of V1, specifically how the early visual cortex represents images—as an input into a convnet and that improves some things. It could be differences. The retina is also doing motion detection and certain things are getting filtered out. There may be some preprocessing of the sensory data. There may be some clever combinations of which modalities are predicting which or so on, that lead to better representation. There may be much more clever things than that. Some people certainly do think that there’s inductive biases built in the architecture that will shape the representations differently or that there are clever things that you can do. Astera, which is the same organization that employs Steve Byrnes, just launched this neuroscience project based on Doris Tsao’s work. She has some ideas about how you can build vision systems that basically require less training. They build into the assumptions of the design of the architecture things like objects are bounded by surfaces and surfaces have certain types of shapes and relationships of how they occlude each other and stuff like that. It may be possible to build more assumptions into the network. Evolution may have also put some changes of architecture. It’s just I think that also the cost functions and so on may be a key thing that it does. I want to talk about this idea that you just glanced off of which was amortized inference. Maybe I should try to explain what I think it means, because I think it’s probably wrong and this will help you correct me.…

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