AlphaGo AI Defeats Sedol Again, With 'Near Perfect Game'

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So, where do we go from here? AlphaGo gets so good because it keeps playing the same game with known constraints against opponents working within those same constraints. It can even play itself to get better.

AlphaGo can play Go, but could it play a different game? Chess? Checkers? Settlers of Catan?
 
So, where do we go from here? AlphaGo gets so good because it keeps playing the same game with known constraints against opponents working within those same constraints. It can even play itself to get better.

AlphaGo can play Go, but could it play a different game? Chess? Checkers? Settlers of Catan?
AlphaGo is just a Go program, but I don't see why they couldn't make a Chess or Checkers program that uses the same DeepMind system.
They probably wouldn't bother is my guess as the number of permutation for chess and checkers are small enough brute force. The unique thing about Go is the unbelievably massive number of possible moves makes the brute force approach untenable.
 
AlphaGo is just a Go program, but I don't see why they couldn't make a Chess or Checkers program that uses the same DeepMind system.
They probably wouldn't bother is my guess as the number of permutation for chess and checkers are small enough brute force. The unique thing about Go is the unbelievably massive number of possible moves makes the brute force approach untenable.
Reminds me of the concept behind "Virtual Intelligence" in Mass Effect. Intelligences with pre-programmed limitations on knowledge, performance, etc... but which are free to develop within those parameters.

I'm not sure that's a distinction the real world makes when it comes to "AI". But I'm not going to worry about SkyNet until a computer can master both Go AND Go Fish on it's own.
 
Obviously. It's just machine learning implemented algorithms using neural nets. For a different problem, you'd use a slightly different architecture and a different training set. They merely chose Go because it is an EXPTIME-complete problem with a huge game tree (way bigger than that of chess, which is why it has been problematic).
 
Fascinating stuff. Even if it is just Go, it is pretty clear that AlphaGo is adjusting to its mistakes and making rapid improvements. With all the Neuromorphic chips starting to hit the market, machine learning is improving very quickly.
 
So, where do we go from here? AlphaGo gets so good because it keeps playing the same game with known constraints against opponents working within those same constraints. It can even play itself to get better.

AlphaGo can play Go, but could it play a different game? Chess? Checkers? Settlers of Catan?
ALphago is a generic AI, which happened to have been trained at playing go. It could just as well have been trained in medical diagnosing, daytrading or writing lyrics.
 
So, where do we go from here? AlphaGo gets so good because it keeps playing the same game with known constraints against opponents working within those same constraints. It can even play itself to get better.

AlphaGo can play Go, but could it play a different game? Chess? Checkers? Settlers of Catan?

The goal of DeepMind isn't to show versatility of their AI against games that can be bruteforced, but rather to show the potential of the AI in a game/environment where possibilities are simply too high for the right move to be calculated that way.
 
My question is this: What does it mean for humanity when computers are better than we are at every "intellectual" pursuit? Will it illuminate the dividing line between "art" and "science"? Will it destroy all human motivation to compete in fields that are dominated by computers? We need to think hard about these questions, because such days are no longer far away in the future.
 

...or writing lyrics. Not so sure on that last one.
 
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