Stuart Russell gave his list of roadblocks, which is relevant as he (might) have just made a claim that was falsified by GPT3, in that same interview -
The first thing is that the Go board is fully observable. You can see the entire state of the world that matters. And of course in the real world there’s lots of stuff you don’t see and don’t know. Some of it you can infer by accumulating information over time, what we call state estimation, but that turns out to be quite a difficult problem. Another thing is that we know all the rules of Go, and of course in the real world, you don’t know all the rules, you have to learn a lot as you go along. Another thing about the Go board is that despite the fact that we think of it as really complicated, it’s incredibly simple compared to the real world. At any given time on the Go board there’s a couple of hundred legal moves, and the game lasts for a couple hundred moves.
And if you said, well, what are the analogous primitive actions in the real world for a human being? Well, we have 600 muscles and we can actuate them maybe about 10 times per second each. Your brain probably isn’t able to do that, but physically that’s what could be your action space. And so you actually have then a far greater action space. And you’re also talking about… We often make plans that last for many years, which is literally trillions of primitive actions in terms of muscle actuations. Now we don’t plan those all out in detail, but we function on those kinds of timescales. Those are some of the ways that Go and the real world differ. And what we do in AI is we don’t say, okay, I’ve done Go, now I’m going to work on suicide Go, and now I’m going to work on chess with three queens.
What we try to do is extract the general lessons. Okay, we now understand fairly well how to handle that whole class of problems. Can we relax the assumptions, these basic qualitative assumptions about the nature of the problem? And if you relax all the ones that I listed, and probably a couple more that I’ve got
So dealing with partial observability, discovering new action sets, managing mental activity (?) and some others. This seems close to the list in an older post I wrote:
Stuart Russell’s List
human-like language comprehension
cumulative learning
discovering new action sets
managing its own mental activity
For reference, I’ve included two capabilities we already have that I imagine being on a similar list in 1960
If AlphaStar is evidence that partial observability isn’t going to be a problem, is GPT3 similarly evidence that language comprehension isn’t going to be a problem, since GPT3 can do things like simple arithmetic? That leaves cumulative learning, discovering action sets and managing mental activity on Stuart’s list.
Stuart Russell gave his list of roadblocks, which is relevant as he (might) have just made a claim that was falsified by GPT3, in that same interview -
So dealing with partial observability, discovering new action sets, managing mental activity (?) and some others. This seems close to the list in an older post I wrote:
If AlphaStar is evidence that partial observability isn’t going to be a problem, is GPT3 similarly evidence that language comprehension isn’t going to be a problem, since GPT3 can do things like simple arithmetic? That leaves cumulative learning, discovering action sets and managing mental activity on Stuart’s list.