On Thu, Oct 24, 2013 at 11:29 PM, Telmo Menezes <te...@telmomenezes.com>wrote:
> On Thu, Oct 24, 2013 at 7:02 PM, Craig Weinberg <whatsons...@gmail.com>
> > On Thursday, October 24, 2013 12:43:49 PM UTC-4, telmo_menezes wrote:
> >> On Thu, Oct 24, 2013 at 6:39 PM, Craig Weinberg <whats...@gmail.com>
> >> wrote:
> >> >
> >> >
> >> >
> >> > The Man Who Would Teach Machines to Think
> >> >
> >> > "...Take Deep Blue, the IBM supercomputer that bested the chess
> >> > grandmaster
> >> > Garry Kasparov. Deep Blue won by brute force. For each legal move it
> >> > could
> >> > make at a given point in the game, it would consider its opponent’s
> >> > responses, its own responses to those responses, and so on for six or
> >> > more
> >> > steps down the line. With a fast evaluation function, it would
> >> > a
> >> > score for each possible position, and then make the move that led to
> >> > best score. What allowed Deep Blue to beat the world’s best humans was
> >> > raw
> >> > computational power. It could evaluate up to 330 million positions a
> >> > second,
> >> > while Kasparov could evaluate only a few dozen before having to make a
> >> > decision.
> >> >
> >> > Hofstadter wanted to ask: Why conquer a task if there’s no insight to
> >> > had
> >> > from the victory? “Okay,” he says, “Deep Blue plays very good chess—so
> >> > what?
> >> > Does that tell you something about how we play chess? No. Does it tell
> >> > you
> >> > about how Kasparov envisions, understands a chessboard?” A brand of AI
> >> > that
> >> > didn’t try to answer such questions—however impressive it might have
> >> > been—was, in Hofstadter’s mind, a diversion. He distanced himself from
> >> > the
> >> > field almost as soon as he became a part of it. “To me, as a fledgling
> >> > AI
> >> > person,” he says, “it was self-evident that I did not want to get
> >> > involved
> >> > in that trickery. It was obvious: I don’t want to be involved in
> >> > off
> >> > some fancy program’s behavior for intelligence when I know that it has
> >> > nothing to do with intelligence. And I don’t know why more people
> >> > that way...”
> >> I was just reading this too. I agree.
> >> > This is precisely my argument against John Clark's position.
> >> >
> >> > Another quote I will be stealing:
> >> >
> >> > "Airplanes don’t flap their wings; why should computers think?"
> >> I think the intended meaning is closer to: "airplanes don't fly by
> >> flapping their wings, why should computers be intelligent by
> >> thinking?".
> > It depends whether you want 'thinking' to imply awareness or not.
> Ok. I don't think we can know that in any case.
> > I think
> > the point is that we should not assume that computation is in any way
> > 'thinking' (or intelligence for that matter). I think that 'thinking' is
> > passive enough to describe computation. It is to say that a net is
> > 'fishing'. Computation is many nets within nets, devoid of intention or
> > perspective. It does the opposite of thinking, it is a method for
> > the measurable residue or reflection of thought.
> Ok but let's take a human grand master playing chess. You don't think
> a computer can play like him?
This relates to what you said earlier which I agree with:
*They are more intelligent in
the sense that they can play competitively while only considering a
small fraction of the scenarios. How do we do this? There is almost no
real AI research nowadays because people gave up on answering this
The answer lies somewhere in building branch histories and databases that
are for now only partial. The computer cannot beat humans without databases
for openings, middle, and endgame. I believe this is what freaked out
Kasparov in the questionable game and what gives his suspicion of human
intervention in the code, which IBM never ruled out or proved negatively
between games, some substance. Kasparov lost because IBM eventually accrued
enough understanding of Kasparov's database (dozens of years of notes and
logs that make up his holy grail secret) to not let it fall for Kasparov's
Kasparov's and any GM's algorithm for beating chess engines often runs
along the lines of:
Keep position closed via Botvinnik type openings and middlegame so the
computer will have to contend with billions of possible move continuations
instead of a few dozen million. Then implement precise, but highly complex,
long term strategy that offers both positional and material gambit for
twenty or so moves which is designed to flip at exactly the point of the
computer's computational horizon, and the computer loses.
This doesn't work today, because human GMs have fed the databases with
every line/variation up their sleeves (from hundreds of years of recorded
games) and consequently we feed the software with every refutation. Once a
refutation is implemented, it's our loss in terms of raw summing value,
because we are forced into unexplored territory and can't compute those
positions optimally anymore (no more strategy, just local computational
tactics; computer wins).
This is vague, so I'll give concrete example: Marshall Attack for black
against white Ruy Lopez. This one is odd and even powerful chess engines
that *don't include this in their databases*, that can brute force billions
of position assessment sums (more than deep blue could on a desktop of
today), will loose even against yours truly! :-)
Why? Because black gives up central control, sacrifices a central pawn and
can get away with savage sacrifices of minor and major pieces and loss of
positional strength; all things the computer is summing highly peering and
brute forcing 10 or 20 moves in advance to calculate an undecidable flurry
of too many best possible moves, in function of finding itself in an
incredible advantage. It doesn't see black's long term but explosive
minority attack king side unless it has a database to draw from at a much
earlier stage in the game, innocently conspiring against pieces that are
irrelevant to the plan.
At the precise point my engine sees that branch in which black mates white
suddenly, it's already too late. The board is already set and the
computer's positional and material advances are irrelevant. I let my engine
evaluate positions without database, and it sees black losing clearly a
couple of points (half a point can tip a game in GM level chess) until on
some billionth branch it discovers black's long term intent, at which
suddenly, in computing on one move, black gets a ten point lead despite
material and positional sacrifice because the computer sees suddenly that
unavoidably white is mated.
Of course, with database all the computer has to do is refutation move like
pawn a4 early enough, which prevents the mess above at its root in the
game. But we don't have algorithms to find these really vague (in terms of
position and material) but precise variations (in terms of fuzzy felicity
conditions for long term strategy; which pieces of black become crucial is
open depending on particular game, save the queen, so you can't define "if
black sacrifices piece x this indicates gambit" independent of position for
example), even though now computers are helping GMs find these types of
Therefore, the whole pitting "human vs. computer" makes little sense to me
in chess until we have computing power to solve chess completely or prove
it undecidable. Human and computer are playing the same game, and without
the right memory and branch, both are lost. I guess a GM panel with
complete access to code, databases, and hardware specs could, given
sufficient time that nobody would finance, find lines long and complex
enough to beat a computer, and a GM panel without the code and in an
unfamiliar branch doesn't have a chance against an entertainment engine
they sell on itunes!
The question regarding superiority should be: what are we even proving
here, given we write the code and we designed the game? Right, the
distinction between machine and human becomes blurry. Every discovery of
line enriches the game up to this point. Even when or if a super computer
solves chess, it will not have done so without the notes of monks' games in
the 15th century. PGC
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