Can self-improving AI predict behaviors in emergent environments? Will they?
Is Go strategy not an emergent property of the rules of Go?
AlphaGo Zero: Learning from scratch
291–300 of 324 posts
Re: AlphaGo Zero: Learning from scratch
#292Earlier quoted context omitted.
Sorry but I don't really see Yudkowski's contributions as "fundamental research into AI safety". More like navel-gazing without any practical implications. At best, listening to him is just a waste of time. At worse, AGI is a real imminent threat and having people like him generating useless noise like he does will make it harder for legitimate concerns to be heard, when the time comes.
So you don't think they're doing anything worthwhile at https://intelligence.org/ . I'm going to guess you actually have no idea and are just extrapolating from some articles you read. What are _you_ doing to make legitimate concerns heard then? Did you even read the article you were complaining about?
Yes, I did and it's very bad form to go around asking people if they read the article. Try to remember that different people form different opinions from similar information.
Re: AlphaGo Zero: Learning from scratch
#293The catch is that this isn't quite zero human knowledge, since the tree search algorithm is a human discovery, and not one that came easily to humans. It also massively cuts down on the search space for an appropriate policy function. That means that this setup isn't necessarily general. How applicable is MCTS to games with asymmetric information, a la Starcraft? What about games that can't quite be modeled with an a…
There's a Dota 2 bot by OpenAI that played games with itself and managed to beat a lot of pros in the scene. It's still SF mid only no runes and some restricted items, but it shows that there is also potential for Starcraft. https://blog.openai.com/dota-2/
Maybe you know something we don't...
"We’re not ready to talk about agent internals"
What makes you think it uses a tree search?
Re: AlphaGo Zero: Learning from scratch
#294Earlier quoted context omitted.
At this point, there is no evidence that the limiting factor in these cases is AI/software. The limiting factor with the neighbors cat is the robotics of having a robust body and arm attachment. We know that the scope of current AI can: 1) Identify a request to feed a cat 2) Identify the cat, cat food and cat's bowl from camera data 3) Navigate an open space like a house Being able to clean an arbitrary building is a…
I remember seeing a demonstration by a deep learning guy of a commercially available robot cleaning a house under remote control. You are seriously underestimating the difficulty of developing software to solve these problems in an integrated way.
This. It is a lot like the business guy thinking it is trivial to program a 'SaaS business' because he has a high level idea in his mind. Like all things programming the devil is in the detail.
Re: AlphaGo Zero: Learning from scratch
#295Earlier quoted context omitted.
If all their methods for success are in the paper the Chinese competitor can just copy that.
Theoretically, yes. But anyone who has tried to implement scientific papers will tell you it's far harder. The papers often lack critical details and implementation hacks: all the little rough edges that go into making a production system work. They also lack context in many cases, so you spend more time reverse engineering the paper than figuring out how to make it work.
It seems odd they make a big deal about tabula rasa, but won't release the source for verification.
Re: AlphaGo Zero: Learning from scratch
#296Earlier quoted context omitted.
Just like Lee Se-dol is a Go grandmaster, beats Gary Kasparov at chess and can also get a perfect score in Pac-Man, right? I mean, if you can't do all of those things then are you even a human-level intelligence?
This just illustrates that surpassing "human level" performance is a silly and arbitrary benchmark, because there is no such thing as general human level performance. But I bet Kasparov would be pretty good at Go, and Sedol would be pretty good at chess. Universality is the real hard problem of AI. In the long run, a mediocre AI that does a lot of different things is far more useful that most targeted "superhuman" AI…
Agreed. It's great that we have domain-specific approaches that can beat humans in their domain (and that we're learning how to make these approaches more generic so that, with re-training, they can adapt to new domains), but the real "oh snap" moment will be when we build something that's barely-adequate but widely adaptable. Something with the adaptability of a corvid or an octopus, say. If we get to that level, it'll mean we've discovered the "universal glue" that joins specialist networks together into a conscious entity.
Re: AlphaGo Zero: Learning from scratch
#297Earlier quoted context omitted.
Please don't refer to them as black boxes. The internals are fully observable.
Fully observable and we still have no idea what the hell it's doing. Makes neuroscience seem kinda bleak doesn't it? There has been a lot of great work lately building up a theory of how these things work, but it is very much still in the early stage. Jascha Sohl-Dickstein in particular has been doing some great work on this. We don't even have answers to the most basic questions. For instance (pedagogically), how th…
> Fully observable and we still have no idea what the hell it's doing.
Of course we do. It's matching a smooth multi-dimensional curve to sample data.
Re: AlphaGo Zero: Learning from scratch
#298I'm reminded of Eliezer Yudkowski's article "There is no fire Alarm for Artificial General Intelligence." Is this smoke? https://intelligence.org/2017/10/13/fire-alarm/ Yes, this is not an AGI. But the hockey-stick takeoff from defeats some players, to defeats an undefeated world-champion, to defeats the version of itself that beat the world champion 100% of the time is nuts . If this happens in other domains, like f…
... yeah, before reading that link my position was "Wow, that's super neat, but Go is a pretty well-defined game," and after reading it I remembered that my position maybe a year or two ago was "Chess is a well-defined game that's beatable by AI techniques but Go is acknowledged to be much harder and require actual intelligence to play and won't be solved for a long while" and now I'm worried. Thanks for posting that…
> Go is acknowledged to be much harder and require actual intelligence to play
No, Go is a much less intelligent[1] game. It has a huge decision tree and requires massive amounts of computation to play, but walking trees and counting is exactly what computers do well and what humans do poorly.
[1] 'Intelligence' here means exactly that which differentiates humans from calculators: the ability to infer new rules from old ones.
Re: AlphaGo Zero: Learning from scratch
#299Earlier quoted context omitted.
That's probably true, but that's very much not what people were saying about Go a couple years ago. There were a lot of people talking about how there isn't a straightforward evaluation function of the quality of a given state of the board, how things need to be planned in advance, how there's much more combinatorial explosion than in chess, etc., to the point where it's a qualitatively different game. For me, as som…
About those claims- this is from Russel and Norvig, 3d ed. (from 2003, so a way back): Go is a deterministic game, but the large branching factor makes it challeging. The key issues and early literature in computer Go are summarized by Boozy and Cazenave (2001) and Muller (2002). Up to 1997 there were no competent Go programs. Now the best programs play most of their moves at the master level; the only problem is tha…
> the last holdout where humans can still beat computers in board games was GO
False, because nobody ever bothered to study modern boardgames rigorously.
Modern boardgames have small decision trees but very difficult evaluation functions. (Exactly opposite from computational games like Go.)
Modern boardgames can probably be solved by pure brute force calculation of all branches of the tree, but nobody knows if things like neural networks are any good for playing them.
Re: AlphaGo Zero: Learning from scratch
#300Earlier quoted context omitted.
At this point, there is no evidence that the limiting factor in these cases is AI/software. The limiting factor with the neighbors cat is the robotics of having a robust body and arm attachment. We know that the scope of current AI can: 1) Identify a request to feed a cat 2) Identify the cat, cat food and cat's bowl from camera data 3) Navigate an open space like a house Being able to clean an arbitrary building is a…
We have have rockets that can go to orbit, and we have submersibles that can visit the ocean floor. That does not mean the rocket-submarine problem is solved, doing both together is not the same problem as doing both separately.
It also doesn't mean that a rocket-submarine is the way to go.