271–280 of 324 posts
So this is fun: "AlphaGo Zero is the program described in this paper. It learns from self-play reinforcement learning, starting from random initial weights, without using rollouts, with no human supervision, and using only the raw board history as input features. It uses just a single machine in the Google Cloud with 4 TPUs (AlphaGo Zero could also be distributed but we chose to use the simplest possible search algor…
Stunning.
Comparing the top player's ELO with Zero's ELO (assuming numbers are accurate, etc): Your rating: 3664 Opponent's rating: 5000 Probability of winning: 0.000456879355457417 So 1 in 2,200 games... ouch
I don't think you can apply this to alphago. I think probability for a human to beat alphago now is zero. Lee Sedol's single victory is the first and the last.
On the bright side, it means the several thousand years of humans playing Go, we were actually going "in the right direction" in terms of optimal strategy, despite not having reduced the game down into provably-optimal mathematical theorems.
We built damn good boats, even before we knew anything formal about fluid dynamics, or even AIR.
Slightly scary how it went from zero to superhuman play in three days. I wonder if general AI will go that way one day.
If you multiply X by the amount of time it takes, on average, for a human to make a move... How many human lifetimes did Zero take to get to superhuman?
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Well, I'd love to see NN solution beating top chess engines. It might also introduce novelty to the game, just as regular engines did
I have some idea how it MIGHT work, but it would be a very boring solution involving 'learning' Stockfish's parameters and HOPING to find improvements to something like integrating time management and search/pruning into it. I wouldn't bet on it though. SMP is notoriously hard to work with alpha-beta search and there are a lot of clever tricks (which is probably still not perfect). Maybe with ASICs, you could make it…
I'm talking about something similar to the described in the paper, 100% self-learned solution without using human heuristics, based on NNs. That could bring a totally new ideas into chess.
> Previous versions of AlphaGo initially trained on thousands of human amateur and professional games to learn how to play Go. AlphaGo Zero skips this step and learns to play simply by playing games against itself, starting from completely random play. So technically this version has lost every game it's ever won. Jokes aside, it's pretty interesting to note that they were able to combine the "policy" and "value" net…
In addition to numerous journal articles, the creators wrote a lay-person book on their creation: Blondie24: playing at the edge of AI, by David B. Fogel [2].
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I spent an hour of my life that I'll never get back reading Yudkowski's overly-long article and I believe I can summarise it thusly: "We don't know how AGI will arise; we don't know when; we don't know why; we don't know anything at all about it and we won't know anything about it until it's too late to do anything anyway; We must act now!!" The question is- if we don't know anything about this unknowable threat, how…
So, in your view, starting MIRI, doing fundamental research into AI safety and advocating for it, is not trying to find the damn light? You exemplify exactly the attitude he's trying to combat. "Oh, nobody knows anything, let's not care about consequences and do whatever."
Earlier quoted context omitted.
I spent an hour of my life that I'll never get back reading Yudkowski's overly-long article and I believe I can summarise it thusly: "We don't know how AGI will arise; we don't know when; we don't know why; we don't know anything at all about it and we won't know anything about it until it's too late to do anything anyway; We must act now!!" The question is- if we don't know anything about this unknowable threat, how…
So, in your view, starting MIRI, doing fundamental research into AI safety and advocating for it, is not trying to find the damn light? You exemplify exactly the attitude he's trying to combat. "Oh, nobody knows anything, let's not care about consequences and do whatever."
Earlier quoted context omitted.
I spent an hour of my life that I'll never get back reading Yudkowski's overly-long article and I believe I can summarise it thusly: "We don't know how AGI will arise; we don't know when; we don't know why; we don't know anything at all about it and we won't know anything about it until it's too late to do anything anyway; We must act now!!" The question is- if we don't know anything about this unknowable threat, how…
>The question is- if we don't know anything about this unknowable threat, how can we protect ourselves against it? In fact, since we're starting from 0 information, anything we do has equal chances of backfiring and bringing forth AGI as it has of actually preventing it. Yudkowski is calling for random action, without direction and without reason. Are you sure you read the essay? That's literally the question he answ…
My reading of the article is that he keeps calling for action without specifying what that action should be and trying to justify it by saying he can't know what AGI would look like (so he can't really say what we can do to prevent it).
>> if you make an honest effort to think of what to do you can likely come up with better than 'random actions' for helping (as many have).
Sure. If my research gets up one day and starts self-improving at exponential rates I'll make sure to reach for th
One idea occurs to me is to now evolve the Go game itself in a direction that adds more challenges for an AI to solve, and then solve those problems. How about being able to handle different and randomized board shapes? How about being allowed to say one move the opponent cannot take when you play a piece? It would be interesting to keep track of what variations the algorithm handles well automatically, and which it…
[1]. https://en.wikipedia.org/wiki/Havannah#Computer_Havannah
[2]. https://news.yale.edu/1999/06/01/successor-chess-new-game-st...