Slashdot Mirror
Alpha Go Takes the Match, 3-0 (i-programmer.info)
mikejuk writes: Google's AlphaGo has won the Deep Mind Challenge, by winning the third match in a row of five against the 18-time world champion Lee Se-dol. AlphaGo is now the number three Go player in the world and this is an event that will be remembered for a long time. Most AI experts thought that it would take decades to achieve but now we know that we have been on the right track since the 1980s or earlier. AlphaGo makes use of nothing dramatically new — it learned to play Go using a deep neural network and reinforcement learning, both developments on classical AI techniques. We know now that we don't need any big new breakthroughs to get to true AI. The results of the final two games are going to be interesting but as far as AI is concerned the match really is all over.
Every time a computer beats a human at a "smart" game, we hear the same thing. And every time, when all is said and done, all we have is a program that can play a game well (and maybe a really aggressive marketing campaign to sell consulting services, Watson).
Look, we barely understand what intelligence is let alone know what it means to have a computer replicate it. We can have computers perform tasks that we ascribe to smart people and call it intelligence, but that's about it right now.
And, with deep learning and neural nets, we haven't gained any real insights into intelligence. We just have a black box mathematical function that can play a game.
Although in a sense it is "nothing new" (neural networks and monte carlo statistical techniques), the combination is one of the most convincing demonstrations of short-circuiting huge branching factors to arrive at what a human player would call "intuition".
Chess has a branching factor of about 35. This is small enough that if you prune the most dismal lines, you can brute force the rest to many ply and arrive at a very good result, but this is not a well generalizable technique.
Go has a branching factor of about 250. This cannot be brute forced, even with aggressive pruning. The result of a NN evaluator function plus Monte Carlo has been astonishing: it was not predicted for computer Go to reach this strength for decades yet, but here we are.
The implications of this combination of techniques to other kinds of problems requiring "intuition" will be interesting to watch.
Is this AI software written in Rust?
I believe it is written in C++ and Lua, because that is what the authors used in previous projects. Most of the computing is done on GPUs, which is most likely done with CUDA, because that is what they used in the past, but they could use OpenCL.
They've already stated that their next goal is to do it again without the human database, but rather through iteration. And the big final advancement *this time* was made by it playing a ton of games with itself. Studying the human games was not enough to get to this level.
And humans do the same thing. They spend their lives studying the important games that came before. So the point is it did it pretty much the same way humans do. And it has already played a move that no strong human has ever played (Game 2, move 37). At first it (not surprisingly) appeared to be blunder, until its strength became clear. Humans will now learn from the computer and their level of play will rise. It happened in chess and checkers, and in a very big way in backgammon. Any strong human backgammon player today would trounce the World Campion of 20 years ago.
We know now that we don't need any big new breakthroughs to get to true AI.
Grossly exaggerated claim. The following article worth reading on this subject by no one else than two of authorities in the field, one did work on the backgammon game in the 90s and the other one on the IBM Deep Blue program that win over the world chess champion Garry Kasparov in 1997. http://www.ibm.com/blogs/think... In particular:
"However, research in such “clean” game domains didn’t really address most real-life tasks that have a “messy” nature. By “messy,” we mean that, unlike board games, it may be infeasible to write down an exact specification of what happens when actions are taken, or indeed what exactly is the objective of the task. Real-world tasks typically pose additional challenges, such as ambiguous, hidden or missing data, and “non-stationarity,” meaning that the task can change unexpectedly over time. Moreover, they generally require human-level cognitive faculties, such as fluency in natural languages, common-sense reasoning and knowledge understanding, and developing a theory of the motives and thought processes of other humans."
Achille Talon
Hop!
But, this isn't 'AI', it's just another 'expert system'.
No. Alpha-Go is pretty much the opposite of an "expert system". Expert systems encode expert human knowledge in a series of explicit rules and if-then tables. Alpha-Go is based on neural nets and self-learning. There is no list of explicit rules.