Important to clarify that he’s not saying computation itself is the source of discoveries in AI. He’s saying that the discoveries in AI that stand the test of time are “general methods that leverage computation”.
I think Sutton is in agreement when he writes:
…we should stop trying to find simple ways to think about the contents of minds, such as simple ways to think about space, objects, multiple agents, or symmetries. All these are part of the arbitrary, intrinsically-complex, outside world. They are not what should be built in, as their complexity is endless; instead we should build in only the meta-methods that can find and capture this arbitrary complexity. Essential to these methods is that they can find good approximations, but the search for them should be by our methods, not by us. We want AI agents that can discover like we can, not which contain what we have discovered. Building in our discoveries only makes it harder to see how the discovering process can be done.
He is arguing that AI researchers need to develop new ways for AI to learn — not simply scale up existing approaches. His point, as I understand it, is AI researchers shouldn’t try to encode in AI things that humans know about the world. Instead, researchers should try to encode as little as possible and develop methods that can learn those things that humans know.
It’s funny, I had the opposite thought when reading Alex Irpan’s essay on reinforcement learning. The most general possible learning agent would be completely agnostic about the laws of physics — whether nested infinite physical objects like Hilbert’s hotel exist, whether time only moves forward or also backward, how many physical dimensions there are, and so on. Humans have all kinds of inductive biases specific to our world. We are biased toward seeing faces. Our visual system “expects” to see objects lit from above, rather than below, because it evolved with the Sun, not floor lighting.
Could progress in artificial learning be, in part, the process of making learning agents less general, and more biased toward the world we actually live in?
I’m sure these two thoughts are not incompatible and not actually opposites. We want a learning agent to have as much innate inductive bias as a human with regard to things like space, objects, multiple agents, symmetries, and so on, but we don’t want learned, explicit human knowledge about these things inserted into the agent at the beginning, before the learning process starts.
Have you read any interesting responses you can share?
I think food for thought is that the use of human annotation in deep supervised learning is a way to get knowledge from human heads into a neural network. At a certain point, supervised learning ceases to scale with computation and gets bottlenecked by data collection and annotation.
Maybe in the future we will do away with supervised learning and just do end-to-end hierarchical reinforcement learning.
A thought experiment is to imagine you have a computer the size of Jupiter. Can you solve the problem with that much computation? With supervised learning, the answer is no, unless you also have enough supervisory signal — i.e. enough human knowledge in neural network-consumable format. (Potential slogan for this idea: all supervised learning is imitation learning?)