
One Giant Step for a Chess-Playing Machine - my_first_acct
https://www.nytimes.com/2018/12/26/science/chess-artificial-intelligence.html
======
fjfaase
Interesting how this is covered in the New York Times, while earlier,
AlphaZero (and its predecessor AlphaGo) showed the similar kind of insight in
playing go (a much more complicated game than chess) coming up with moves that
humans would dismiss almost immediately. Since then, go playing professionals
have started to imitate this style of playing. I guess the same will happen
with professional chess players in the coming years: there will be a less
strong focus on material and more on positioning. Because AlphaZero cares less
about pieces, but more about their position and abilities to attach and/or
defend.

~~~
cepth
IMO, at the highest levels of chess, there has always been a focus on
"positioning" (positional chess) over material. World Champions like
Capablanca, Botvinnik, Karpov, and Kramnik all play/ed in a style that was
postionally sound, and at times boa-constrictor like. If you want to be a
grandmaster today, you have to be able to understand/execute concepts like
giving up material to establish a fortress (
[https://en.wikipedia.org/wiki/Fortress_(chess)](https://en.wikipedia.org/wiki/Fortress_\(chess\))
).

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...](https://www.chess.com/news/view/world-chess-championship-
game-6-caruana-misses-nearly-impossible-win)). 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...](https://chess24.com/en/read/news/alphazero-really-is-that-good)) 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.

------
my_first_acct
About the author (from the bottom of the article):

> 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.

------
mindgam3
Is it just my sonar beeping off the charts, or does anyone else hear the
unmistakable signs of a submarine article? (1)

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.

1\.
[http://www.paulgraham.com/submarine.html](http://www.paulgraham.com/submarine.html)

2\.
[https://arxiv.org/pdf/1811.11553.pdf](https://arxiv.org/pdf/1811.11553.pdf)

~~~
taneq
> technically all AlphaZero does is play 3 types of perfect-information games
> quite well

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.

~~~
slaveofallah93
Is it the undisputed champion of chess? I thought that A0 had never played
Stockfish 10, the current best engine.

~~~
cepth
FYI earlier this month, they actually did announce the results of a 1000 game
match:

[https://www.chess.com/news/view/updated-alphazero-crushes-
st...](https://www.chess.com/news/view/updated-alphazero-crushes-stockfish-in-
new-1-000-game-match).

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](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](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...](https://chess24.com/en/read/news/alphazero-really-is-that-good)), 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.

------
Waterluvian
I know very little about AI, but something that excites me, maybe just
romantically, is the idea of an AI taking everything it learned from one game
and applying it to another.

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.

~~~
Lkjhmnbv
There's a lot of research into this. If you're curious you can Google around
for transfer learning and one shot learning.

~~~
Waterluvian
Thanks for the terms to search.

------
Animats
Has someone applied this to online poker yet? Make some real money.

~~~
Lkjhmnbv
Heads up Texas holdem is "solved".

[https://www.scientificamerican.com/article/time-to-fold-
huma...](https://www.scientificamerican.com/article/time-to-fold-humans-poker-
playing-ai-beats-pros-at-texas-hold-rsquo-em)

~~~
cepth
The vast majority of "action" available online is "6-max" (6 player) or "full-
ring" (9 player). On a PokerStars, WSOP.com, or Party Poker, you're going to
find that there are maybe 1/10th or 1/20th the number of headsup tables as
higher capacity tables.

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](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](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...](http://www.starsgroup.com/careers/job/Poker-AI-Research-
Engineer-oyBn7fwn) & [https://www.pokernews.com/news/2017/10/pokerstars-to-
hire-ar...](https://www.pokernews.com/news/2017/10/pokerstars-to-hire-
artificial-intelligence-researchers-29136.htm)). 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.

------
thomble
The prospect of curing diseases is wonderful. But for now, we're just using
these algorithms to figure out how to maximize the amount of time people spend
staring at their phones.

------
ikeboy
I don't like the framing. AlphaZero is just a very refined and efficient form
of brute force. Precomputing weights obtained by brute force doesn't make the
overall enterprise not brute force anymore.

~~~
soraki_soladead
In your comment here and below it sounds like you're saying: "if you take a
brute force algorithm and make it more efficient than brute force by not brute
forcing the solution then it's just a brute force algorithm!"

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.

~~~
ikeboy
I never suggested it's overhyped. I think the specific terms being used to
compare it to humans are inaccurate.

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.

~~~
lern_too_spel
> AlphaZero used quite a lot of processing power

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).

