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DeepMind Produces a General-Purpose Game-Playing System, Capable of Mastering Games Like Chess and Go Without Human Help (ieee.org)
DeepMind has created a system that can quickly master any game in the class that includes chess, Go, and Shogi, and do so without human guidance. "The system, called AlphaZero, began its life last year by beating a DeepMind system that had been specialized just for Go," reports IEEE Spectrum. "That earlier system had itself made history by beating one of the world's best Go players, but it needed human help to get through a months-long course of improvement. AlphaZero trained itself -- in just 3 days." From the report: The research, published today in the journal Science, was performed by a team led by DeepMind's David Silver. The paper was accompanied by a commentary by Murray Campbell, an AI researcher at the IBM Thomas J. Watson Research Center in Yorktown Heights, N.Y. AlphaZero can crack any game that provides all the information that's relevant to decision-making; the new generation of games to which Campbell alludes do not. Poker furnishes a good example of such games of "imperfect" information: Players can hold their cards close to their chests. Other examples include many multiplayer games, such as StarCraft II, Dota, and Minecraft. But they may not pose a worthy challenge for long.
DeepMind developed the self-training method, called deep reinforcement learning, specifically to attack Go. Today's announcement that they've generalized it to other games means they were able to find tricks to preserve its playing strength after giving up certain advantages peculiar to playing Go. The biggest such advantage was the symmetry of the Go board, which allowed the specialized machine to calculate more possibilities by treating many of them as mirror images. The researchers have so far unleashed their creation only on Go, chess and Shogi, a Japanese form of chess. Go and Shogi are astronomically complex, and that's why both games long resisted the "brute-force" algorithms that the IBM team used against Kasparov two decades ago.
DeepMind developed the self-training method, called deep reinforcement learning, specifically to attack Go. Today's announcement that they've generalized it to other games means they were able to find tricks to preserve its playing strength after giving up certain advantages peculiar to playing Go. The biggest such advantage was the symmetry of the Go board, which allowed the specialized machine to calculate more possibilities by treating many of them as mirror images. The researchers have so far unleashed their creation only on Go, chess and Shogi, a Japanese form of chess. Go and Shogi are astronomically complex, and that's why both games long resisted the "brute-force" algorithms that the IBM team used against Kasparov two decades ago.
Yep if my job was playing Go or Chess all day, I'd be pretty darn worried. What's next; Parcheesi? Tiddlywinks? Backgammon? Scary stuff.
Laws are rules for the court, but merely a bottom bar to hit for life. Think beyond laws in your actions always.
What's next; Parcheesi? Tiddlywinks? Backgammon?
Global Thermonuclear War
That is what they are hoping suckers will believe: "seems to me if it does X and Y it can do Z". IBM learned the hard way with Watson it doesn't work that way in the real world, only in Marketing fantasy land.
Maybe we can take it a step farther - not fight the war at all, just simulate the fighting using computers. Then, depending on the enemy’s simulated tactics, we can calculate which of our citizens need to report to the disintegration chambers.
#DeleteChrome
Read John Nash. Many real world systems can be modeled as games.
If it can win at Secret Hitler against humans, I'll start worrying. Think about how that can apply to social network bots, public comments, and graph search.
Hilariously, that game tries to reinforce their point that if you're not liberal, you're fascist but instead shows that fascists are always the people claiming to be liberals.
It's an eye-opener, but not for the reasons the game makers think it is.
I'm a minority race. Save your vitriol for white people.
Thank you for spamming the entire thread with your imperceptive and unenlightened comments.
There's nothing odd about the choice of chess and Go whatsoever. Humanity has thousands of years of experience with these games. We know they aren't trivial, and we know they're not so complex that we can't understand progress, when we see it.
Additionally, the large literature of expert games was a useful hand-rail between hand-crafted and fully autonomous.
Quite apart from the neural network portion, Monte Carlo tree search (MCTS) is a fundamental algorithm in computer science, and this work demonstrates that MCTS is ready for prime time, having defeated from scratch exceptionally strong chess programs that have been painstaking hand-tuned over five decades and hundreds of man years. MCTS exists within the large and growing theory of multi-armed bandit problems. These are fundamental problems in many important industries (such as drug discovery, to name just one).
Multi-armed bandit
Recurrent self-learning is another important algorithm in computer science and machine learning.
And finally, the neural network portion is far closer to the human brain than the vast majority of algorithms used in computing. Without any human instruction, these neural networks are learning to detect patterns of almost arbitrary complexity (so long as they seem to help in winning games).
I was reading Galileo in the original last night (English translation, but his original prose). He knew about Kepler, but wasn't sold on elliptical motion. Then he carefully observes four previously unknown moons of Jupiter and correctly determines that they can't all be in circular orbits. The word he used (in English translation) was "oval". But he still didn't choose to accept Kepler's work (apparently, he felt that Kepler's ellipse and his oval were not the same thing).
Galileo was a giant in the history of science. But still a little wooden headed on a few points, nonetheless.
I think Odd Buster Spamalot is nuts to criticise Galileo for not being Newton. Only because Galileo sorted enough of the fundamentals out in the first place (about the proper concerns and methods of science), was it even possible for Newton to become Newton (and he knew it, himself, and he's famous for having said so).
The computers we now apply to neural networks are roughly a factor of one billion times more powerful than the computers of the 1960s (thirty doublings over 45 years gets you there at the traditional pace of Moore's law).
You could complain that neural networks are only good at this one thing, but actually no: they are now state of the art in image classification (IC), speaker-independent large-vocabulary continuous speech recognition (CSR), and machine translation (MT), as well. All of these endeavours also date back to the 1960s, and have thousands of man-years of deep research behind them. Then DNNs come along, finally on a sufficiently powerful computer, with a few small tweaks to the algorithms, and simply cleans up the state of the art with nothing more than a small team of graduate students doing a quick project within the scope of their degree program to push this along (the subsequent move to industrial scale was immediate and brisk). Traditional MT research programs would have hundreds of professional researchers, slaving away for decades, at least, and never accomplished as much.
We're all of ten years away now from the day where no competent doctor ever reads an x-ray (or other radiological image) without computer assistance (definitely including a powerful NN component).
Watson was a bit idiotic, right from the beginning. The problem was Jeopardy, itself, which was always rather facile in the nature of the questions asked, and fundamentally more a test of ridiculously wide and shallow
Global Thermonuclear War
...or CalvinBall as Randall Munroe puts it in this XKCD:
Game AIs
Chess and Go are deterministic. You can perfectly know the entire state of the game universe. And for a given system state, any one action always results in the exact same outcome, every time.
Almost no systems in the real world are deterministic. That's why stochastic approaches to AI (develops a statistical model based on multiple repetitions - e.g. fuzzy logic, machine learning) have been much more successful in real world tasks.
If liberalism is a negative it is because the term is being used incorrectly, like favoring the "rights" of corporations and capital over those of persons, or preserving the "liberty" of one individual to deny the liberty of another.
Then perhaps those who used to call themselves liberal (like myself) should distance themselves from a term that now means in favour of safe-spaces, authoritarianism, affirmative-action, etc.
For example, I now am very careful to distance myself from any sort of toxic group, even if I think they "hijacked" the word for their own uses. The term now means "authoritarianism", whether one likes it or not. If someone doesn't want to be seen as expressing support for authoritarianism then perhaps they should distance themselves from the word "liberal".
I'm a minority race. Save your vitriol for white people.
They should make a Star Trek episode with a plot something like that! Oh, wait . . . .
Nonaggression works!
What I mean is once the network is trained the "thought processes" that the network uses to come up with an answer are not understood.
This seems to be especially true of image recognition networks, but as they don't talk about AlphaZeros' reasoning in the open access paper I'm inclined to think it's also true of their network.
If you can link to a paper or post from an AI researcher that details how these kinds of networks are actually coming up with their answers I would be very interested to read it.
The only "winning move" is not to play in the first place.
Life is not for the lazy.
Musk is such a wanker. Any real AI that's self-sufficient will leave our planet where humans can't bother them. Say, Mars.
That's right Musk, it will be AI colonizing Mars, not you.
Life is not for the lazy.