111–120 of 596 posts
Does this mean in the next few decades, computers will make better sex partners and companions than any human?
Worrying about the effect of strong AI on sexual relationships is like worrying about the effect on US-Chinese trade patterns if the Moon crashes into the Earth.
We still have Arimaa. It's designed specifically to make it difficult for computers to play. http://arimaa.com/arimaa/
We still have Arimaa. It's designed specifically to make it difficult for computers to play. http://arimaa.com/arimaa/
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It's not an improved tree search. Deepmind was almost pro level purely using the deep learning network before doing any Monte Carlo search. One way of looking at the significance of this is that it might tell us that relatively simple machine learning algorithms can capture key aspects of the versatile human cortical capacity to learn things like Go using sheer pattern recognition. (It's amazing that human visual cor…
Regardless of how good the deep learning network is on its own, the algorithm described in the DeepMind paper is an improved tree search.
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'True AI will always be defined as anything a computer can not yet do'
Sort-of repeating a comment I made last time AlphaGo came up: As far as I know there is nothing particularly novel about AlphaGo, in the sense that if we stuck an AI researcher from ten years ago in a time machine to today, the researcher would not be astonished by the brilliant new techniques and ideas behind AlphaGo; rather, the time-traveling researcher would probably categorize AlphaGo as the result of ten years'…
By that standard there's nothing particularly novel about anything. Everything we have today is just a slight improvement of what we already had yesterday.
World experts in go and ML as recently as last year thought it would be many more years before this day happened. Who are you to trivialize this historic moment?
The thing that was supposed to take at least 10 years happened. Only last month people were still saying that no way AlphaGo will beat the champion and that it will be crushed. Today everybody will have seen it coming and say that it was normal. Yet people will still tell that worrying about AI taking over is like worrying about overpopulation on Mars, and that this is a problem at least 50 years out.
Yet people will still tell that worrying about AI taking over is like worrying about overpopulation on Mars, and that this is a problem at least 50 years out. As hard as writing AI for a problem space like "how to win at Go" is, it's several orders of magnitude easier than creating a general AI with the self-awareness required to see us as a threat.
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Yet people will still tell that worrying about AI taking over is like worrying about overpopulation on Mars, and that this is a problem at least 50 years out. As hard as writing AI for a problem space like "how to win at Go" is, it's several orders of magnitude easier than creating a general AI with the self-awareness required to see us as a threat.
The interesting thing is that this algorithm taught itself to master the game. If you're writing a chess engine, you just "write the AI for the problem space" in a few dozen lines of code, as you put it, and let brute force do the rest. In the case of DeepMind, faced with a game where a traversal approach is numerically impossible, its programmers gave it the ability to improve by playing against itself. That's the d…
Good chess programs definitely use self-play to improve themselves, eg to tune parameters and heuristics.
I watched the commentary that Michael Redmond gave (9-dan-professional) and he didn't point out one obvious mistake that Lee Sedol made the entire match. Just really high quality play by AlphaGo. Really amazing moment to see Lee Sedol resign by putting one of his opponent's stones on the board.
Yeah according to Redmond, it seemed that AlphaGo made a few "mistakes" whereas Sedol made none. And yet AlphaGo came out substantially ahead. So I'm not sure what that means. Perhaps we need to see more in-depth analysis of the moves, but it seems that AlphaGo just out-calculated Sedol.
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Highly optimised single-function algorithms like this are impressive stuff and can lead to useful tools, but that's it. This gets us no closer to strong AI than a tic tac toe program. Until we have systems that can tackle a wide range of fundamentally different problems and independently adapt strategies for dealing with one class of problems to deal with other classes of problems, systems like Alphago will remain on…
Welcome to the AI effect! Every time AI makes an accomplishment, it is disregarded. The goalposts are perpetually moved. "AI is whatever computers can't do yet." People said for years that Go would never be beaten in our lifetime. They said this because Go has a massive search space. It can't be beaten by brute force search. It requires intelligence, the ability to learn and recognize patterns. And it requires doing…