Apologies in advance for what may be perceived as a rant. I have a very low tolerance for clickbait-y BS like this as it pertains to my own passions as a lifelong chess devotee and former professional player.
First, the author of the article has no professional credibility in either chess or machine learning. He's a professor of math and a writer. No disrespect to either math or writing, I love and value both very highly, but they have very little to do with chess and machine learning per se.
The problems is he tries to present AlphaZero as "humankind’s first glimpse of an awesome new kind of intelligence," which is really a bit of a stretch unless you add the disclaimer that technically all AlphaZero does is play 3 types of perfect-information games quite well. This is undoubtedly a great accomplishment, particularly in the field of Go which many domain experts felt intuitively would not crack to our AI overlords before another 5-10 years of computing power/hardware advances at least.
(As someone who had the unfortunate label of "prodigy" applied in my youth due to earning the title of chess master at age 10, I consider myself somewhat of a domain expert in chess, and I was one of those people who got it wrong. I barely know the rules of Go, but intuitively I could comprehend that it was several orders of magnitude more complex than chess, and I was really hoping that the Go gurus would fend off the machines for longer. They didn’t. Hats off to DeepMind.)
But. With all due respect to DeepMind engineers for an impressive result in chess and go, it's a bit too early to start thinking of AlphaXXX as an "oracle" where all we can do is "sit at its feet and listen intently" while we would "not understand why the oracle was always right" and eventually be left "gaping in wonder and confusion."
(As an aside, the amount of pseudo-religious worship language in the piece is truly off the charts. I realize it stokes the passions, but it would be great if we could talk about AI’s true strengths and limitations without resorting to such histrionics. But I digress.)
Why is it too early to start bowing down to a new god? Well, for starters, they basically just brute forced the game of Go a bunch of years earlier than predicted, but this wasn't just a pure software win, this was also heavily connected to massive increases in computing power aka GPUs and ginormous cloud-based render farms.
Secondly, the author tries to make the leap from AlphaZero [good at 3 perfect-information games: chess, go and shogi] to what he calls "a more general problem-solving algorithm; call it AlphaInfinity". Note how he invokes the holy grail of AGI (Artificial General Intelligence) without actually using this term, which would set off alarm bells in, well, anyone who knew anything about AI who wasn't employed by DeepMind/Google.
Notice further how this massive leap from "machine that can play 3 games well" to "machine that can, you know, actually think about stuff like a human can, including these pesky 'edge cases' and un-trained-for scenarios that always confuse our algorithms despite their otherwise inhuman level of perfection".
One great example of such a case that may cause one to question these glorious predictions is a research paper titled “Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects” which shows how ML models consistently mistake a school-bus for a snowplow under the right (snowy) conditions (2). Far be it from me to dare bursting the bubble/reality distortion field of certain ML leaders and visionaries, but c’mon - a human child, once they truly learned how to recognize a schoolbus, would never mistake it for a snowplow, even if it was upside down.
This flaw doesn’t mean that we can’t update training data to handle these types of rotations, but it does mean that we have a lot of work to do before we can say that these ML models have in some way grasped the “essence” of “school-bus” or [insert-other-object] here in a deep symbolic way, and by "deep symbolic way" I mean "any way that a human child learns how to do reasonably quickly before moving on to other, exponentially harder tasks".
I could go on, but I won’t. Just in case my overall point isn’t clear:
1. AlphaZero is an unbelievably impressive accomplishment within the limited subset of life that is [chess, go, shogi]
2. ML approaches, even in computer vision, have a long way to go before anything remotely resembling child-level human intelligence
3. Therefore, can we please please stop the marketing masquerading as news articles about DeepMind’s latest result. And if anyone at DeepMind is listening: your product is pretty sweet! It would be better strategically to simply let it speak for itself, without trying to frame it as AGI.
You might be right about it being a submarine article but this seems to be underselling it. AlphaZero is (as I understand it) undisputed world champion by an indeterminate margin on the two most popular games, and has become so after only being given the basic game rules. If you told someone from 2015 that this would happen in the next decade, they'd laugh at you.
https://www.chess.com/news/view/updated-alphazero-crushes-st....
AlphaZero won 155 games, lost 6, and drew 839 games against Stockfish. Granted, this was against Stockfish 9.
This implies that AlphaZero was roughly +52 Elo rating against Stockfish 9 (https://www.3dkingdoms.com/chess/elo.htm).
Stockfish 10 is currently rated ~32 points higher than Stockfish 9 (http://computerchess.org.uk/ccrl/4040/rating_list_all.html). If we were to do very crude transitive reasoning, you'd expect AlphaZero to still beat Stockfish 10.
EDIT:
So apparently the +155, -6 score was against Stockfish 8. Stockfish 8 is rated by the CCRL list at 3379, with Stockfish 10 rated 85 points stronger than 8.
Worth noting that AlphaZero was only given 4 out of 9 hours of total training time when playing against Stockfish 8 (https://chess24.com/en/read/news/alphazero-really-is-that-go...), but I guess we can't make any real conclusions about AlphaZero vs Stockfish 10.
EDIT 2:
So apparently AlphaZero also "defeated" Stockfish 9, but the preprint of the upcoming paper in Science doesn't seem to provide a crosstable.
It seems that Stockfish 8 was given a 44-core machine to play on, and was not constrained in terms of time spent per move etc.
What will be interesting is to see the effect on Leela (https://github.com/LeelaChessZero/lczero), which is public and open-source, takes part in public tournaments, and is built -- as much as possible, based on what's been disclosed in the papers -- to use the same approach as AlphaZero.
FYI, Leela Chess Zero is the open-source project inspired by AlphaZero, and they recently did a match against SF 10, details here: https://lichess.org/blog/XA7juREAAC4AxZsR/deathmatch-leela-v...
http://news.cornell.edu/stories/2006/05/checkmate-professor-...
I've been pretty impressed with him as a sort of cross-disciplinary math wizard ever since I used his Nonlinear Dynamic and Chaos textbook in a class. His research is pretty wide ranging. He may be wrong about the future, but I'd be shocked if it was because of a lack of technical understanding of the algorithms.
Not that I doubt his cross-disciplinary math skills or whatnot. But let's not pretend that he's anything resembling a serious chess player.
But even I know enough, from watching videos on YouTube that analyze some of the AlphaZero vs. Stockfish games, to appreciate that AlphaZero was playing in a style I haven't seen before. What's the threshold for someone serious enough to write a chess article?
I mean, I guess playing off of the whole "AI apocalypse" and the image of our computer overlords makes a quite evocative point. To be fair, this isn't just a problem with AlphaZero — similar problems in science journalism, especially in the field of medicine (i.e. Immortality is just a few decades away! Stem cells cure cancer!) seem to show up whenever a new or interesting result happens.
Well, do you accept Garry Kasparov qualified to comment on this?
"I admit that I was pleased to see that AlphaZero had a dynamic, open style like my own."
"Alpha-Zero is surpassing us in a profound and useful way, a model that may be duplicated on any other task or field where virtual knowledge can be generated."
If AlphaZero is brute force than any use of a non-exhaustive planning mechanism (pruned MCTS in this case) is brute force which is honestly ridiculous. Search and planning have a long history in both computer science _and_ neuropsychology because that is what we call the methods that are more efficient than brute force at the expense of some accuracy.
There are some problems with the article but it isn't that AlphaZero is just some overhyped brute force algorithm.
On a related note, AlphaZero used quite a lot of processing power - even with the optimizations, if it had to run on human hardware it would be pretty worthless.
It ran on a single machine with four TPUs. In a few years with a few more optimizations, I can imagine an equal strength implementation on a handheld chess computer.
If you're talking about training hardware, the correct comparison is against the processing time used by all serious human chess players through history because an apples to apples comparison would have both training from scratch (just the rules).
>AlphaZero gives every appearance of having discovered some important principles about chess, but it can’t share that understanding with us.
No, it doesn't have any new principles, it just has a very good system of weights.
Imagine a perfect computer that plays the game by mapping out every single move. AlphaZero is a series of optimizations that allow for an efficient but lossy simulation of such a computer. Most of the research in this area is about making an oracle more practical/more accurate estimations of the theoretically correct response. I think it's fair to call it brute force with optimizations.
The difference between AlphaZero and StockFish is that StockFish only does the latter (compute weights for features given to it by others), while AlphaZero also does the former (distill features from game state).
As is evolution.
https://www.scientificamerican.com/article/time-to-fold-huma...
The development of "GTO" (game theory optimal) play in Texas Hold 'Em is certainly a first step in the direction of computers playing poker. However, there's still quite a long way to go.
Poker Snowie, one of the cutting edge "GTO" programs, is based off of NNs (https://www.pokersnowie.com/about/technology-training.html). At the same time, there are some glaring weaknesses in the software, namely that it can only offer suggestions at specific pot size bets (0.25, 0.5, 1, 2). The authors themselves concede some other weaknesses (https://www.pokersnowie.com/about/weaknesses.html).
Worth mentioning that Amaya, the owner of PokerStars, has posted job openings for "AI researchers" (http://www.starsgroup.com/careers/job/Poker-AI-Research-Engi... & https://www.pokernews.com/news/2017/10/pokerstars-to-hire-ar...). Some people think that the position may be to help PokerStars detect/combat bot use, but others think that there may be a (arguably bleak) future where players have the option to compete against Amaya-created bots online.
In January 2017 Libratus beat a team of four top-10 heads- up no-limit specialist professionals in a 120,000-hand Brains vs. AI challenge match over 20 days.
(PDF) https://www.cs.cmu.edu/~noamb/papers/17-IJCAI-Libratus.pdf
Seems like we are happy to give them billions of games of practice. But what happens when exercise becomes a constraint?
You've played a ton of chess. Now here's the rules to Go. Now play one game of it.
The World Champion that played in the most sacrificial/attacking style, Mikhail Tal, was famed for giving up pieces to generate attacking momentum. Contemporary analyses of his play have found that some of these sacrifices were unsound, and some were actually the "best move" in a given position.
I don't think it's feasible to expect human players to be able to calculate at the ply/depth that AlphaZero (or other chess engines) is able to. See this example from the latest World Championship (https://www.chess.com/news/view/world-chess-championship-gam...). A "forced" win in 30 moves was available on the board, but it would've required that Caruana make moves that cut against the "principles" regarding piece placement ("positioning") that are drilled into chess players.
I think a simple reality is that the search depths that AlphaZero (and to a lesser extent other chess engines) are dealing with are simply beyond human capability. A human player trying to execute the sacrifices that AlphaZero did (https://chess24.com/en/read/news/alphazero-really-is-that-go...) would be taking a stab in the dark. In most positions, they wouldn't really be able to calculate all the variations, or foresee how the endgame would play out.
> Steven Strogatz is professor of mathematics at Cornell and author of the forthcoming “Infinite Powers: How Calculus Reveals the Secrets of the Universe,” from which this essay is adapted.
