[FRIAM] Google self-evolving AlphaZero artificial intelligence program mastered chess from scratch in 4 hours: Rich Murray 2017.12.10

Mon Dec 11 12:36:53 EST 2017

Not clear that AlphaZero would do well on John's SuperGame.  It won on
chess (and Go) by playing against itself in advance. If it doesn't have the
opportunity to do that it won't have that advantage. It's strategy would
have to be something like on-the-fly playing the selected game against
itself in the background at the same time as it is playing the human
opponent. The question then is how fast it can teach itself the new game.
It's strategy would have to be to slow down the game against the opponent
as much as possible to give itself time to learn the new game.  So it
becomes a matter of computer speed (for learning the new game) and the
extent to which the real game can be delayed as it is in progress.

On Mon, Dec 11, 2017 at 8:58 AM Steven A Smith <sasmyth at swcp.com> wrote:

> Marcus wrote:
>
> Is a strategy anything more than a coarse-grained tactic?   And is
> intuition anything more than an associative memory that connects coarse-
> and fine- grained information?
>
> Is it any more?  Or any less?
>
> Learning is an iterated game that operates at many scales and on many
> dimensions...
>
> <TL;Don'tRead>
>
> The Biological Evolution record shows myriad explosions in quantity and
> diversity  that resulted from a (small? but) significant innovation (e.g.
> multicellular organisms, Eukaryotes, photosynthesis, oxygen metabolisms,
> vertebrae,  warm blooded metabolism, live birth, etc).   Punctuated
> equilibrium?
>
> There seem to be similar inflection points in the "learning" implied in
> human social/technological/economic evolution and we may be in on the
> shoulder of "yet another" which gestures in the direction of the von
> Nuemann/Vinge/Kurzweillian "technological singularity".
>
> I'm not much of a chess expert, myself, playing only *barely*
> competitively in my late teens (as Spassky and Fischer were dukingit out),
> and revisiting it in the pre-ALife era of "evolution, games, and learning"
> in the late 80s, along with GO.  Chess itself, as a "playing field" for
> learning strategy is a microcosm to observe the general idea of
> "learning".   The history of chess is fascinating.  In the current context,
> it is fascinating that out of about 1500 years of existence (in
> proto-forms), for a little over 500 of it, the rules have settled on what
> we use today, but the tactics and strategies developed *on top* of those
> has continued to  both *evolve* and *reflect* society at large.  Most
> notably, perhaps, the "Romantic Period" where one of the dominant ideas was
> that personal genius *and* style mattered more than theory or logic or even
> board positions.   This somewhat reflected the military and political style
> of that period.  During the "age of Enlightenment" it also had a moral
> embedding...  The "modern" era emerged with the industrial revolution and
> more importantly perhaps, the mechanization of war where chess strategy,
> now somewhat more "scientific" began to eventually give rise to
> "hypermodernism" which focus more on controlling the center of the board
> from afar (a parallel to mechanized warfare where power could be projected
> over a great distance in a short amount of time).   Algorithmic play and
> mathematical analysis has been considered since the late Romanitc period
> but didn't come into it's own  until the modern digital computer, with
> Claude Shannon taking an early swipe at the problem as early as 1950!   The
> fact that it took more than 50 years to get to Deep Blue's thin victory
> over Kasparov is more a testimony to how subtle and hard Chess is than how
> intelligent humans are, etc.
>
> "Deep Learning" itself seems like nothing more (and nothing less) than the
> latest innovation in machine learning (game theory, neural nets, cellular
> automata, genetic algorithms, learning classifiers, etc.) which *could*
> very well portend the breakaway point of the AI-driven technological
> singularity.  I'm not THAT up on "Deep Learning" but things like Generative
> Antagonistic Networks (and other unsupervised machine learning) seem to
> have the key quality of not needing supervision by humans to learn...
> there may be one more level of indirection to be had before things go
> ape-shit (exponentially speaking)...
>
>  I personally don't imagine that a *single* AI will be the source of this,
> but rather a Cambrian-explosion-like plethora of AI's, though they may be
> so pervasive and promiscuous as to cross-fertilize so thoroughly that they
> will be a single "organism" for all practical purposes.
>
> </TL;DR>
>
> - Steve
>
>
>
> *From:* Friam [mailto:friam-bounces at redfish.com
> <friam-bounces at redfish.com>] *On Behalf Of *John Kennison
> *Sent:* Monday, December 11, 2017 7:17 AM
> *To:* The Friday Morning Applied Complexity Coffee Group
> <friam at redfish.com> <friam at redfish.com>
> *Subject:* Re: [FRIAM] Google self-evolving AlphaZero artificial
> intelligence program mastered chess from scratch in 4 hours: Rich Murray
> 2017.12.10
>
>
>
> I once thought I had a sure-fire way to make games between humans and
> computers fairer. Start with a large set of chess-like games that use
> different boards, different pieces, different rules. Enumerate the games so
> that each one corresponds to a n-digit binary numeral (for large n). Then
> make a "super game" in which the players start by creating a n digit binary
> numeral by taking turns in which they can specify one of the n binary
> digits. The super game would continue by playing the chess-like game that
> corresponds to the created number.
>
>
>
> In a super game between a human and a computer, the computer would not
> have access to all the insights into the nature of chess that humans have
> established over hundreds of years of playing chess and which chess playing
> computers use to defeat humans.  Of course, the human player would also be
> deprived of all the years of research into chess, but humans can use their
> marvelous intuition to figure out a reasonable set of strategies even for a
> game they haven't studied before. The computer, without a reasonable set of
> strategies, would (I assumed) find little benefit from  its massive
> computing power.
>
>
>
> The new AlphaZero game playing computer refutes my idea.
>
>
>
> ------------------------------
>
> *From:* Friam <friam-bounces at redfish.com> on behalf of Rich Murray <
> rmforall at gmail.com>
> *Sent:* Monday, December 11, 2017 12:16:26 AM
> *To:* Rich Murray
> *Subject:* [FRIAM] Google self-evolving AlphaZero artificial intelligence
> program mastered chess from scratch in 4 hours: Rich Murray 2017.12.10
>
>
>
>
>
>
>
> https://futurism.com/4-hours-googles-ai-mastered-chess-knowledge-history/
>
>
>
> Chess isn’t an easy game, by human standards. But for an artificial
> intelligence powered by a formidable, almost alien mindset, the trivial
> diversion can be mastered in a few spare hours.
>
>
>
> In a new paper, Google researchers detail how their latest AI evolution,
> AlphaZero, developed “superhuman performance” in chess, taking just four
> hours to learn the rules before obliterating the world champion chess
> program, Stockfish.
>
>
>
> In other words, all of humanity’s chess knowledge – and beyond – was
> absorbed and surpassed by an AI in about as long as it takes to drive from
> New York City to Washington, DC.
>
>
>
> After being programmed with only the rules of chess (no strategies), in
> just four hours AlphaZero had mastered the game to the extent it was able
> to best the highest-rated chess-playing program Stockfish.
>
>
>
> In a series of 100 games against Stockfish, AlphaZero won 25 games while
> playing as white (with first mover advantage), and picked up three games
> playing as black.
>
> The rest of the contests were draws, with Stockfish recording no wins and
> AlphaZero no losses.
>
>
>
>
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-- 
Russ Abbott
Professor, Computer Science
California State University, Los Angeles
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