Self-Teaching Chess Algorithms
This was posted yesterday.
From KurzweilAI:
Demis Hassabis, the founder and CEO of DeepMind, announced at the Neural Information Processing Systems conference (NIPS 2017) last week that DeepMind’s new AlphaZero program achieved a superhuman level of play in chess within 24 hours.The program started from random play, given no domain knowledge except the game rules, according to an arXiv paper by DeepMind researchers published Dec. 5.
“It doesn’t play like a human, and it doesn’t play like a program,” said Hassabis, an expert chess player himself. “It plays in a third, almost alien, way. It’s like chess from another dimension.”
AlphaZero’s ‘alien’ superhuman-level program masters chess in 24 hours with no domain knowledge — https://tinyurl.com/y9lcqy8q
I started programming IBM machines in the late 60s, and at the time there was talk about the possibility of a computer someday beating a human at chess. Almost no one was talking seriously about a computer learning chess on its own, and not merely learning it but mastering it. And mastering it in 24 hours. AlphaZero is mind boggling.
What will AlphaZero be doing in three years? Five? Will we be carrying AlphaZero around in our pockets? Our brains? Will some other AI be the new king of the hill? Will AlphaZero be regarded as quaintly primitive by then? Will Kurzweil’s 2029 prediction of a computer passing as human in a Turing test arrive earlier than expected?
And what will humans be like in 2029? Here’s a guy working from the other end:
Humans 2.0: meet the entrepreneur who wants to put a chip in your brain —https://tinyurl.com/gfs543chip
The article he cites begins with this:
Bryan Johnson isn’t short of ambition. The founder and CEO of neuroscience company Kernel wants “to expand the bounds of human intelligence”. He is planning to do this with neuroprosthetics; brain augmentations that can improve mental function and treat disorders. Put simply, Kernel hopes to place a chip in your brain.It isn’t clear yet exactly how this will work. There’s a lot of excited talk about the possibilities of the technology, but – publicly, at least – Kernel’s output at the moment is an idea. A big idea.
“My hope is that within 15 years we can build sufficiently powerful tools to interface with our brains,” Johnson says. “Can I increase my rate of learning, scope of imagination, and ability to love? Can I understand what it’s like to live in a 10-dimensional reality? Can we ameliorate or cure neurological disease and dysfunction?”
This is a science-fiction scenario. I suppose the best example of this theme in literature is the first Star Trek movie. The movie’s theme was based on a theme in the writings of Isaac Asimov. Asimov was a science consultant for the movie. The theme is this: a coming singularity, which will be a fusion of machines and human beings. A new form of life will emerge from this evolutionary development. This is the long sought-after leap of being which motivated alchemists five centuries ago.
The fact that the owner of a self-teaching chess computer program has described the learning process of the algorithms as being alien is indicative of the intellectual framework associated with the thesis of a coming singularity. The possibility of fusing human thought and digital algorithms that are in some way implanted in the human brain is science fiction. It is now being taken seriously by some futurologists.
Obviously, no one knows if this fusion is technologically feasible. The inherent nature of scientific innovation resists forecasts of what is or is not possible. As Arthur C. Clarke said a generation ago, whenever we hear an expert say that something is scientifically impossible, we will probably see that the scientific impossibility comes true.
THE MATHEMATICAL THEORY OF GAMES
A lot of focus is being placed on the digital nature of self-teaching algorithms. This is easily applied to games. This is where the great breakthroughs have been made over the past decade. The rate of accomplishment is now speeding up astronomically. But never forget this: chess is a matter of fixed rules. There are patterns in games of chess that are imposed by these rules. Computer programmers now find that they do not have to teach these rules to algorithms. This is the great breakthrough that has taken place over the last 18 months. The algorithm can survey a huge number of games if the games have been recorded digitally. The algorithm can deduce the rules and then implement strategies in terms of these rules. Here is how an article in Wired described the process.
At one point during his historic defeat to the software AlphaGo last year, world champion Go player Lee Sedol abruptly left the room. The bot had played a move that confounded established theories of the board game, in a moment that came to epitomize the mystery and mastery of AlphaGo.A new and much more powerful version of the program called AlphaGo Zero unveiled Wednesday is even more capable of surprises. In tests, it trounced the version that defeated Lee by 100 games to nothing, and has begun to generate its own new ideas for the more than 2,000-year-old game.
AlphaGo Zero showcases an approach to teaching machines new tricks that makes them less reliant on humans. It could also help AlphaGo’s creator, the London-based DeepMind research lab that is part of Alphabet, to pay its way. In a filing this month, DeepMind said it lost £96 million last year.
DeepMind CEO Demis Hassabis said in a press briefing Monday that the guts of AlphaGo Zero should be adaptable to scientific problems such as drug discovery, or understanding protein folding. They too involve navigating a mathematical ocean of many possible combinations of a set of basic elements.
Despite its historic win for machines last year, the original version of AlphaGo stood on the shoulders of many, uncredited, humans. The software “learned” about Go by ingesting data from 160,000 amateur games taken from an online Go community. After that initial boost, AlphaGo honed itself to be superhuman by playing millions more games against itself.
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