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I was really expecting Lee Sedol to win here. I'm very excited, and congratulations to the DeepMind team, but I'm a bit sad about the result, as a go player and as a human.
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.
Earlier quoted context omitted.
Not that terrifying? It's a very special purpose-built tool. We'll still always have Calvinball! https://xkcd.com/1002/
The way AlphaGo plays is more general purpose than other game playing systems. It doesn't have any heuristics, rules, or game rule books programmed into it, it learned to play Go like you would. The same technique could be applied to other areas, the same way DeepMind originally got super-human level Atari 2600 game performance purely by having it watch pixels.
Another way to look at is, is how fast they were able to make it a lot better in a few months.
I was at the 2003 match of Garry Kasparov vs Deep Junior -- the strongest chess player of all time vs what was at that point the strongest chess playing computer in history. Kasparov drew that match, but it was clear it was the last stand of homo sapiens in the man vs machine chess battle. Back then, people took solace in the game of Go. Many boldly and confidently predicted we wouldn't see a computer beat the Go wor…
I've never felt playing against what is suppose to be an entire room of machines (wether Deep Blue or Watson) to be fair. What would be fair is to limit the total mass of the computer to say 200kg and leave it at that. What is effectively happening is AlphaGo is running on a distributed system of many, many machines. Even Watson took an entire room. Google is paying a premium to push AlphaGo to win.
Here's one: how long until a computer can beat a human assisted by a computer?
More generally, scientists using AI for research will probably have to do research on the research, to understand what the AI discoveries mean. Maybe they mean something we can't grasp at all, in which case they go completely over our heads, like ants trying to learn about the finer points of financial markets. We will probably have to learn new concepts and even new languages designed by the AI to convey the meaning.
Here's one: how long until a computer can beat a human assisted by a computer?
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.
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.
DeepMind is an algorithm which clearly improves a lot on traditional tree search. But how will a better tree search algorithm lead to AI taking over? Why does winning at Go mean AI is closer to taking over the world than when it won at Chess?
This 10 years to beat go prediction shows that our time estimates are wildly ignorant.
What an incredible moment - I'm so happy to have experienced this live. As noted in the Nature paper, the most incredible thing about this is that the AI was not built specifically to play Go as Deep Blue was. Vast quantities of labelled Go data were provided, but the architecture was very general and could be applied to other tasks. I absolutely cannot wait to see advancements in practical, applied AI that come from…
The position evaluation heuristic was developed using machine learning, but it was also combined with more 'traditional' algorithms (meaning the monte-carlo algorithm). So it was built specifically to play go (in the same way deep blue used tree searching specifically to play chess.....though tree searching is applicable in other domains).