
AlphaGo Beats Lee Sedol in Final Game - doppp
https://gogameguru.com/alphago-5/
======
johnloeber
This was probably the closest game in the series. Livestream:
[https://www.youtube.com/watch?v=mzpW10DPHeQ](https://www.youtube.com/watch?v=mzpW10DPHeQ)

A few months back, the expert consensus was that we were many years away from
an AI playing Go at the 9-dan level. Now it seems that we've already surpassed
that point. What this underscores, if anything, is the accelerating pace of
technological growth, for better or for worse.

In game four, we saw Lee Sedol make a brilliant play, and AlphaGo make a
critical mistake (typical of monte carlo-trained algorithms) following it.
There's no doubt that with further refinement, we'll soon see AI play Go at a
level well beyond human: games one through three already featured
extraordinarily strong (and innovative) play on part of AlphaGo.

Previous Discussions:

Game 4:
[https://news.ycombinator.com/item?id=11276798](https://news.ycombinator.com/item?id=11276798)

Game 3:
[https://news.ycombinator.com/item?id=11271816](https://news.ycombinator.com/item?id=11271816)

Game 2:
[https://news.ycombinator.com/item?id=11257928](https://news.ycombinator.com/item?id=11257928)

Game 1:
[https://news.ycombinator.com/item?id=11250871](https://news.ycombinator.com/item?id=11250871)

~~~
cgearhart
>A few months back, the expert consensus was that we were many years away from
an AI playing Go at the 9-dan level.

These kinds of predictions are almost always useless. You can always find
people who say it'll take _n_ years before _x_ happens, but no one can predict
which approaches will work, and how much improvement they'll confer.

> What this underscores, if anything, is the accelerating pace of
> technological growth, for better or for worse.

What? This is a non-sequitur. Continued advancement doesn't mean that it is
_accelerating_ , and even if this does represent an unexpected achievement
that doesn't mean that future development will maintain that pace.

Appreciate it for what it is - an historic achievement for AI & ML - and stop
trying to attach broader significance to it.

~~~
johnloeber
> These kinds of predictions are almost always useless.

Let's rephrase. For a long time, the expert consensus regarding Go was that it
was extremely difficult to write strongly-performing AI for. From the AlphaGo
Paper: Go presents "difficult decision-making tasks; an intractable search
space; and an optimal solution so complex it appears infeasible to directly
approximate using a policy or value function."

For many years, the state-of-the-art Go AI stagnated or grew very slowly,
reaching at most the amateur dan level. AlphaGo presents a huge and surprising
leap.

> Continued advancement doesn't mean that it is accelerating

Over constant time increases, AI is tackling problems that appear
exponentially more difficult. In particular, see Checkers (early '90s) vs
Chess ('97) vs Go ('16). The human advantage has generally been understood to
be the breadth of the game tree, nearly equivalent to the complexity of the
game.

If we let _x_ be the maximum complexity of a task at which AI performs as well
as a human, then I would argue that _x_ has been growing at an accelerating
pace over the past few decades.

~~~
xigency
> AI is tackling problems that appear exponentially more difficult.

The hard _est_ AI problems are the ones that involve multiple disciplines in
deep ways. Here's a top tier artificial intelligence problem: given a plain
English description of a computer program, implement it in source code.

There might be some cases where this is possible, and some cases are bound to
fail.

Those are the kind of difficult problems in AI, which combine knowledge,
understanding, thought, intuition, inspiration, and perspiration - or demand
invention. We would be lucky to make linear progress in this area let alone
exponential growth.

I think there's certainly an impression of exponential progress in AI in
popular culture, but the search space is greater than factorial in size, and I
think hackers should know that.

~~~
the_af
> _To be fair, in terms of the complexity of rules, checkers is easier to
> understand than go which is easier to understand than chess. And honestly,
> go seems like the kind of brute-force simple, parallel problem that we can
> solve now without too much programming effort_

Your intuition is mistaken. Go is indeed "easier to understand" than Chess in
terms of its rules, but it is arguably harder to play well and has a way
larger search space, which makes it less amenable to brute force, and this was
precisely why people thought it'd be impossible for a computer to play it
consistently at champion level.

I don't think the achievement of AlphaGo is solely due to increased processing
power, otherwise why did people think Go was such a hard problem?

~~~
nilkn
> it is arguably harder to play well and has a way larger search space, which
> makes it less amenable to brute force, and this was precisely why people
> thought it'd be impossible for a computer to play it consistently at
> champion level.

Are human champions not subject to those same difficulties of the game,
though? When you're pitting the AI against another player who's also held back
by the large branching factor of the search tree, then how relevant really is
that branching factor anyway in the grand scheme of things? A lot of people
talk about Go's search space as if human players magically aren't affected by
it too. And the goal here was merely to outplay a human, not to find the
perfect solution to the game in general.

(These are honest questions -- I am not an AI researcher of any kind.)

~~~
jdietrich
Go players rely heavily on pattern recognition and heuristics, something we
know humans to be exceptionally good at.

For example, go players habitually think in terms of "shape"[1]. Good shape is
neither too dense (inefficiently surrounding territory) or too loose (making
the stones vulnerable to capture). Strong players intuitively see good shape
without conscious effort.

Go players will often talk about "counting" a position[2] - consciously
counting stones and spaces to estimate the score or the general strength of a
position. This is in contrast to their usual mode of thinking, which is much
less quantitative.

Go is often taught using proverbs[3], which are essentially heuristics.
Phrases like "An eye of six points in a rectangle is alive" or "On the second
line eight stones live but six stones die" are commonplace. They are very
useful in developing the intuition of a player.

As I understand it, the search space is largely irrelevant to human players
because they rarely perform anything that approximates a tree search. Playing
out imaginary moves ("reading", in the go vernacular) is generally used
sparingly in difficult positions or to confirm a decision arrived at by
intuition.

Go is the board game that most closely maps to the human side of Moravec's
paradox[4], because calculation has such low value. AlphaGo uses some very
clever algorithms to minimise the search space, but it also relies on 4-5
orders of magnitude more computer power than Deep Blue.

    
    
      [1] https://en.wikipedia.org/wiki/Shape_(Go)
      [2] http://senseis.xmp.net/?Counting
      [3] https://en.wikipedia.org/wiki/Go_proverb
      [4] https://en.wikipedia.org/wiki/Moravec%27s_paradox

------
tunesmith
By the way, for those who want to learn by themselves, there are a lot of ways
to play Go against a computer in a way that is friendly for beginners.

My rough journey so far - on a Mac, but much of this can be done on Linux - I
started out playing 9x9 games against Gnugo, giving myself as much handicap as
possible (without it resigning immediately), and then removing stones as I
improve. I got to the point where I could sometimes beat 9x9 when I started
with two extra stones, and then I started with 19x19.

Took me a while to win 19x19 with 9 stones, but then I won by learning a bit
more about extending on hane. Then you can improve from there.

After that point, you can also switch to fuego or pachi, which are stronger by
default. The end result is it really is easy and possible to learn a ton just
by playing against software, tracking your ability throughout, just by picking
programs with different strength and handicap levels.

I've also enjoyed using GoGui to pit two computer programs against each other
and watch how they play with various handicaps.

Then there's all the puzzles - goproblems.com, smartgo, etc. Finally, there
are plenty of ebooks you can buy through smartgo books.

This doesn't get into playing against humans on the various servers, but
there's plenty of information about that online.

~~~
i_don_t_know
Which program do you use on the Mac? A long time ago I've used Goban[1] and I
enjoyed it very much, but it's not available here in the App Store and
apparently it doesn't fully work yet on El Capitan. (I don't know if it's not
available right now because of the El Capitan problems or for some other
reason.)

What are some good go programs for the iPhone, both for playing and for
learning/improving?

[1] [http://www.sente.ch/?p=1206&lang=en](http://www.sente.ch/?p=1206&lang=en)

~~~
tunesmith
Goban is working for me on El Capitan, but I installed it before upgrading.
There's also the older free version which might still be up on a webpage
somewhere.

But the better option is that I was able to get GoGui working - I did have to
manually build/compile it myself but there is a way to build it so that it
creates a real OS X Application. It's quite good, you can set any board
position and then tell a computer program to respond from that point.

[http://gogui.sourceforge.net](http://gogui.sourceforge.net)

EDIT: For the iPhone I like SmartGo Kifu for playing games. 'Tsumego Pro' and
'GoProblems' for puzzles (they're adaptive) and 'Go Books' by smartgo for
ebooks.

~~~
rimantas
I second the SmarGo Kifu

------
skarist
Great game and amazing series/match. This last one was absolutely nail biting.
My hat off to the AlphaGo team and to Mr. Lee Sedol. Sedol showed incredible
fighting spirit and stamina. Just imagine sitting through a 5 hour game like
that last one, with full concentration all the time. And seeing the expression
of exhaustion and disappointment on Sedol's face after last moves and his
resignation. Phew... I bet that he came in rather confident into this last
game, after beating AlphaGo in the fourth, figuring he had found a weakness.
And he seemed to have a rather good start, securing a decent territory in the
lower right corner. We can all marvel at the machine/software the DeepMind
team has built, but still I feel that the real marvel is the human brain. Will
we learn anything from this series, about how it functions and evaluates game
positions in a stratetgic games? The classic problem/mystery is how extremely
good the human brain is at pruning game-trees. Whole branches are thrown out
in split seconds and probably never explored. Currently taking a watt-for-watt
comparison there is no question about whose "hardware" is superior -> Lee
Sedol's brain. But I guess the DeepMind team and the community will take
plenty of lessons from this and in a few years span, Lee Sedol's phone will
beat him 100% of the time. At least I wouldn't be willing to bet against it,
even though we are hitting the roof in Moore's law.

~~~
eggie
I would love to compare the energy requirements of the AlphaGo and Mr. Sedol.
I imagine there are many orders of magnitude in difference between them.
Perhaps the most fair comparison would be between a computer that uses no more
energy than a human does. Or, to let the human work with a computer provided
they do not use more total energy to play the game.

~~~
frabcus
Nice question!

To make it fair, do you include the energy used to train it? From scratch, or
from the amateur human game data?

Likewise, do you include the energy used to evolve the human brain?

~~~
eggie
> Likewise, do you include the energy used to evolve the human brain?

I was thinking of this in a limited, human-promoting sense. We shouldn't lose
sight of our own special powers just because a computer the size of a house
can outsmart us in a specialized domain :)

~~~
PeCaN
...Especially when, you know, we _made_ that computer....

That's the really impressive part IMO. AlphaGo is an incredibly cool creation.
Hats off to the DeepMind team.

------
awwducks
My rough summary of the match, informed by the various commentators and random
news stories.

Game 1: Lee Sedol does not know what to expect. He plays testing moves early
and gets punished, losing the game decisively.

Game 2: Lee Sedol calms down and plays as if he is playing a strong opponent.
He plays strong moves waiting for AlphaGo to make a mistake. AlphaGo responds
calmly keeping a lead throughout the game.

Game 3: Lee Sedol plans a strategy to attack white from the start, but fails.
He valiantly plays to the end, creating an interesting position after the game
was decided deep in AlphaGo's territory.

Game 4: Lee Sedol focuses on territory early on, deciding to replicate his
late game invasion from the previous game, but on a larger scale earlier in
the game. He wins this game with a brilliant play at move 78.

Game 5: The prevailing opinion ahead of the game was that AlphaGo was weak at
attacking groups. Lee Sedol crafted an excellent early game to try to exploit
that weakness.

Tweet from Hassabis midgame [0]:

    
    
        #AlphaGo made a bad mistake early in the game (it didnt know a known tesuji) but now it is trying hard to claw it back... nail-biting.
    

After a back and forth late middlegame, Myungwan Kim 9p felt there were many
missed chances that caused Lee Sedol to ultimately lose the game by
resignation in the late endgame behind a few points.

Ultimately, this match was a momentous occasion for both the AI and the go
community. My big curiosity is how much more AlphaGo can improve. Did Lee
Sedol find fundamental weaknesses that will continue to crop up regardless of
how many CPUs you throw at it? How would AlphaGo fare against opponents with
different styles? Perhaps Park Jungwhan, a player with a stronger opening
game. Or perhaps Ke Jie, the top ranked player in the world [1], given that
they'd have access to the game records of Lee Sedol?

I also wonder if the quick succession of these games on an almost back-to-back
game schedule played a role in Lee Sedol's loss.

Myungwan Kim felt that if Lee Sedol were to play AlphaGo once more, the game
would be a coinflip since AlphaGo is likely stronger, but would never fix its
weakness between games.

[0]:
[https://twitter.com/demishassabis/status/709635140020871168](https://twitter.com/demishassabis/status/709635140020871168)

[1]: [http://www.goratings.org/](http://www.goratings.org/)

~~~
Razengan
> Did Lee Sedol find fundamental weaknesses that will continue to crop up
> regardless of how many CPUs you throw at it?

Unrelated to Go and this article, but I wonder if I'm the only one for whom
such commentary evokes an image of future warfare between AI and humans;
ruthlessly efficient machines against which many people give their lives, to
find a weakness that can be exploited by future generations. :)

~~~
danmaz74
Why would an AI _want_ to make war with humans, in the first place?

~~~
astrofinch
Computers do what you say, not what you mean. If I write a function and name
it quickSort, that's no guarantee that the function is a correctly implemented
sorting algorithm. If I write a function called beNiceToHumans, that's no
guarantee that the function is a correct implementation of being nice to
humans.

It's relatively easy to formally describe what it means for a list to be
sorted, and prove that a particular algorithm always sorts a list correctly.
But it's next to impossible to formally describe what it means to be nice to
humans, and proving the correctness of an algorithm that did this is also
extremely difficult.

These considerations start to look really important if we're talking about an
AI that's (a) significantly smarter than humans and (b) has some degree of
autonomy (can creatively work to achieve goals, can modify its own code, has
access to the Internet). And as soon as the knowledge of how to achieve (a) is
widely available, some idiot will inevitably try adding (b).

Note: Elon Musk and Sam Altman apparently think spreading (a) to everyone is a
good way to mitigate the problem I describe. This doesn't make sense to me.
You can read my objections in detail here:
[https://news.ycombinator.com/item?id=10721621](https://news.ycombinator.com/item?id=10721621)
There's another critique of their approach here:
[http://slatestarcodex.com/2015/12/17/should-ai-be-
open/](http://slatestarcodex.com/2015/12/17/should-ai-be-open/)

If you're interested to learn more, here's a good essay series on the topic of
AI: [http://waitbutwhy.com/2015/01/artificial-intelligence-
revolu...](http://waitbutwhy.com/2015/01/artificial-intelligence-
revolution-1.html)

~~~
Udik
The funny thing is that this "computers do what you say, not what you mean"
comes directly from their _lack_ of intelligence. So it's kind of strange that
we talk about the threats of _superintelligence_ brought along by the fact
that, fundamentally, a machine is _stupid_. Am I the only one to see a slight
contradiction there?

~~~
Strilanc
Goals are orthogonal to intelligence. The fact that the AI understands what
you want won't motivate it to change what it's optimizing. It's not being
dumb, it's being _literal_.

You asked it to make lots of paperclips, tossing you into an incinerator as
fuel slightly increases the expected number of paper clips in the universe, so
into the incinerator you go. Your complaints that you didn't mean _that many_
paperclips are too little, too late. It's a paperclip-maximizer, not a
complaint-minimizer.

Choosing the goal for a superintelligent AI a goal is like choosing your wish
for a monkey's paw[1][2]. You come up with some clever idea, like "make me
happy" or "find out what makes me happy, then do that", but the process of
mechanizing that goal introduces some weird corner case strategy that
horrifies you while doing really well on the stated objective (e.g. wire-
heading you, or disassembling you to do a really thorough analysis before
moving on to step 2).

1:
[https://en.wikipedia.org/wiki/The_Monkey's_Paw](https://en.wikipedia.org/wiki/The_Monkey's_Paw)
2:
[http://lesswrong.com/lw/ld/the_hidden_complexity_of_wishes/](http://lesswrong.com/lw/ld/the_hidden_complexity_of_wishes/)

~~~
Retric
I would suggest that a computer is not 'super intelligent' until it can modify
it's goals.

Further, maximizing paperclips in the long term may not involve building any
paperclips for a very long time. [https://what-if.xkcd.com/4/](https://what-
if.xkcd.com/4/)

~~~
astrofinch
>I would suggest that a computer is not 'super intelligent' until it can
modify it's goals.

This is a purely semantic distinction. Thought experiment: Let's say I modify
your brain the minimum amount necessary to make it so you are incapable of
modifying your goals. (Given the existence of extremely stubborn people, this
is not much of a stretch.) Then I upload your brain in to computer, give you a
high speed internet connection, and speed up your brain so you do a year of
subjective thinking over the course of every minute. At this point you are
going to be able to quit a lot of intelligent-seeming work towards achieving
whatever your goals are, despite the fact that you're incapable of modifying
them.

~~~
Retric
Your assuming you can do work without modifying goals. I have preferences, but
my goals change based on new information. Suppose bob won the lottery and
ignored that to work 80 hours a week to get a promotion to shift manager at
work untill the prize expired. Is that intelegent behavior?

~~~
Strilanc
You're confusing instrumental goals with terminal goals.

~~~
Retric
Try and name some of your terminal goals. Continuing to live seems like a
great one, except there are many situations where people will chose to die and
you can't list them all ahead of time.

At best you end up with something like maximizing your personal utility
function. But, defacto your utility function changes over time, so it's at
best a goal in name only. Which means it's not actually a fixed goal.

Edit: from the page _It is not known whether humans have terminal values that
are clearly distinct from another set of instrumental values._

~~~
Strilanc
That's true. Many behaviors (including human behaviors) are better understood
outside of the context of goals [1].

But I don't think that affects whether it makes sense to modify your terminal
goals (to the extent that you have them). It affects whether or not it makes
sense to describe us in terms of terminal goals. With an AI we can get a much
better approximation of terminal goals, and I'd be really surprised if we
wanted it to toy around with those.

1:
[http://lesswrong.com/lw/6ha/the_blueminimizing_robot/](http://lesswrong.com/lw/6ha/the_blueminimizing_robot/)

------
vermontdevil
Ken Jennings just welcomed Lee Sedol to the "Human Loser Club"

[http://www.slate.com/articles/technology/technology/2016/03/...](http://www.slate.com/articles/technology/technology/2016/03/google_s_alphago_defeated_go_champion_lee_sedol_ken_jennings_explains_what.html)

Pretty good article here.

~~~
scott_s
Jennings is a surprisingly good and humorous writer. (I say "surprising"
because there is no reason to expect that someone so good at Jeopardy would
also be so good at expressing himself with such charming self-deprecation.)

------
malanj
After the first 3 games I thought that AlphaGo was far beyond human level, but
it's a harder call to make now. It seems very unlikely that an AI would be
very close to exactly matching a human, one would expect it to be much
stronger or much weaker.

Perhaps humans are closer to the "Perfect Game" than we think?
[http://hikago.wikia.com/wiki/Hand_of_God](http://hikago.wikia.com/wiki/Hand_of_God)
The top players estimate they would need a 4 stone advantage to win a perfect
player.

~~~
ggreer
I think AlphaGo is best described as, "Superhuman, but with bugs."[1] The
software is very young. I bet these glitches will become ever rarer over time.

> The top players estimate they would need a 4 stone advantage to win a
> perfect player.

The branching factor for Go is so huge that I doubt anyone or anything comes
close to optimal play. I heavily discount the opinions of most Go players on
this topic, as they've been right about very little lately. Before AlphaGo
existed, many of them thought it would be decades before a Go AI beat the best
humans. Before this tournament, the vast majority of them predicted that Lee
Sedol would trounce AlphaGo. And during the live commentary, I saw multiple 9
dan pros estimate that AlphaGo was behind, then gradually realize that it was
winning. That's exactly what happens when you encounter a much more formidable
player.

1\. Coined by Eliezer Yudkowsky:
[https://www.facebook.com/yudkowsky/posts/10154024894449228](https://www.facebook.com/yudkowsky/posts/10154024894449228)

~~~
anentropic
"Superhuman, but with bugs." ...this is the future that is coming :)

~~~
hughperkins
This :-)

------
krig
Really interesting and close match, it was great listening to the expert
player analyse the game and having the final score be uncertain until very
late in the game.

I found the discussion around weaknesses in the Monte Carlo tree search
algorithm interesting. It sounds like the opinion from the expert is that
there are some inherent weaknesses in how MCTS tends to play moves against
theoretical moves from the opponent that don't make sense; ie. that AlphaGo
sees a potential win that would only happen if the human player made very bad
moves. It's fascinating that the seeming weakness in AlphaGo would come from
the algorithmic part of the AI and not the neural net. Could it be that as the
neural net becomes stronger and stronger at the game, eventually the
algorithmic part of it would become less useful to it? If that's the case, it
really feels like this could be the path to truly general AI.

~~~
GolDDranks
I think the "weakness" isn't that much of a weakness in the sense, that it's
still playing optimally given it's search space – but it doesn't know how to
overplay to make a comeback. (Overplay is a non-optimal play that is intended
to be confusing for the opponent. AlphaGo doesn't regard it's opponent in any
way, or assess what might be confusing for him, it just plays moves that it
thinks are optimal.)

A (min-max, alpha-beta-pruning) tree search is the optimal way to determine
your best move if you can afford to search the situation space globally.
However, as that's clearly impossible, there's clever ways to reduce the
search space. Random pruning, as a "normal" monte carlo search would do, or
semi-random pruning with a neural network estimating the situations, like
AlphaGo does.

However, as the search space is now non-global, it might exclude the optimal
solution. And thus, the min-max-assumption doesn't hold: your opponent might
come up with moves that you didn't think of, screwing your calculations off.

If your opponent is a god ( = can afford global search space), or at least has
a search space that is a strict superset of yours, it's "game over, man".

But: if your opponent isn't a god, it's likely that his search space is NOT
the same as yours. And you can exploit the fact. If you have any idea what
kind of search space your opponent has, you can come up with moves, that he
couldn't have included in his tree search, and bet on that his/her "hidden"
moves aren't better than yours.

Currently AlphaGo doesn't do that. It behaves like it'd be playing against...
well, against another AlphaGo.

~~~
taneq
> If your opponent [...] has a search space that is a strict superset of
> yours, it's "game over, man".

Not necessarily. I think that's what we saw in game 4; that despite AlphaGo
having a general advantage in terms of search space, it's still possible for
the weaker of two strong-but-imperfect players to 'get lucky' and play a move
that the stronger player didn't anticipate or account for.

~~~
Shaanie
If he didn't anticipate or account for that move, that means his search space
wasn't a strict superset. Unless I'm missing something, you're essentially
repeating what OP said after his "But: if your opponent isn't a god, it's
likely that his search space is NOT the same as yours.".

------
wowzer
While what the AlphaGo team has accomplished is nothing short of amazing, I'm
not sure if everyone's thinking about this in the right context. While playing
there's a "super computer" behind the scenes with these specs 1,920 CPUs and
280 GPUs [0]. Then consider all the machines used to train this neural net.
I'd say Sedol's brain is pretty freaking powerful. Also, with that much
computing power I would expect AlphaGo to win with the right team and the
right approach to solving the problem. It would be very interesting to change
the rules and limit the processing power of the computer playing a human.

[0]
[https://en.wikipedia.org/wiki/AlphaGo](https://en.wikipedia.org/wiki/AlphaGo)

~~~
chillacy
You can make AlphaGo stronger by adding machines, you can't make Sedol
stronger by gluing more gray matter in his brain (yet I guess). So yea,
efficiency is nice but so is scalability, and that's why this is exciting.

Also in a few years AlphaGo could be running on your cellphone. The chess AI
Stockfish runs on an iphone today, and cellphones from what I could find
online, uses less power than the brain (brain is roughly 20W, iphone has 1.4Wh
battery which even if stockfish drains in 1 hour, is still 1W of power
consumption)

Give it a few years and we'll all be saying "of course computers can play Go,
but at least they can't <Insert task humans are still good at>"

------
Wildgoose
Four components:

Learning (viewing millions of professional game moves).

Experience (playing different versions of itself)

Intuition (ability to accurately estimate the value of a board)

Imagination (evaluating a series of "what if?" scenarios using Monte Carlo
Tree Search)

I think the significant thing about AlphaGo is that apart from some hand-
crafting in the Monte Carlo Tree Search routines, this is all general purpose
programming.

It may only be baby-steps, but it does feel like a genuine step towards true
(general) AI.

~~~
awwducks
> Learning (viewing millions of professional game moves)

According to the last press conference, it was apparently strong amateur games
from the internet that it used to train with. Afterwards, it just played
itself, as you mentioned.

~~~
thomasahle
Yes, that was surprising to me as well. It seems unfair to not give it access
to the thousands of years of knowledge in the go community, though even more
impressive that it still plays so well.

~~~
visarga
I think self play games would be an even better source for learning because
they are at 9p level, not amateur.

------
nichochar
Guys, this is fantastic, but lets not forget: What "shows how capable the
human brain actually is," is: 1) The human brain invented Go to begin with 2)
The long and celebrated history of Go 3) The human brain made DeepMind 4) The
human brain finds value and beauty in all of this, which no machine would

~~~
ASalazarMX
We will always win at love and free will /s

~~~
cLeEOGPw
Robots will never feel emotions, that's what separates us from animals and
robots. /s

------
typon
Does anyone else find it funny that the Game 4 in which Lee Sedol won got the
most upvotes on Hacker News? We're still firmly with team human it seems :P

~~~
madez
I think your reasoning is flawed. Imagine Lee Sedol won the first three
matches and AlphaGo the fourth. I think that news would have more upvotes than
the former three matches.

The outcome was a surprise and therefore gathered more attention.

------
tunesmith
AlphaGo was strong enough to survive a mistake, not knowing a known tesuji,
and still claw back to win by a couple of points. I wonder wonder that means
in terms of handicap, maybe it is a stone stronger than Sedol?

------
trott
I tried to learn Go a decade ago. After spending some time on it, I came to
the conclusion that it's just not an enjoyable game for me. Here's why:

As you can see in this match, games are often won and lost by just a few
points (1% of the whole territory). So, not only do you have to count
territory precisely at the end, but throughout the game, and this isn't easy
to do in your head.

Maybe if you are an autistic accountant, that's fun, but not for me. If I have
to play a strategic board game, it will be good old chess. And now that
computers are finally beating people at both, there is no longer any need to
look at Go as some kind of mythical last refuge of humanity.

~~~
lmm
> So, not only do you have to count territory precisely at the end, but
> throughout the game, and this isn't easy to do in your head.

You don't need to count - you can just play to take as many points as you can.

> Maybe if you are an autistic accountant, that's fun, but not for me. If I
> have to play a strategic board game, it will be good old chess.

I find it's the opposite. In chess you have to play with constant vigilance,
because a single blunder decides the game - even at grandmaster level,
something like 60% of games are decided by blunders. In go you can play much
more casually, you can take some risks, because a mistake costs one or two
points but it doesn't snowball much. So not every move has to be perfect; it's
much more possible to recover from mistakes.

~~~
trott
> You don't need to count - you can just play to take as many points as you
> can.

You don't have to take my word for it, since I never got past beginner level,
but I know there is a consensus among the experts on this matter:

"""

> Also, do players actively count territories of their and their opponents
> territories during the game (does this differ in a 9x9 vs 19x19 game)?

Yes, skilled players actively count territories frequently as they play. This
includes making estimates for areas that aren't completely settled yet. In a
serious game with enough time, skilled players will usually re-count the board
every dozen or so moves. This is useful because it informs you whether you
need to play risky and invade or reduce, or whether a peaceful,
straightforward development strategy is enough. This doesn't differ too much
depending on the board size, but on smaller board sizes there is a lot less to
count, obviously. :)

"""

~~~
lmm
I know from direct personal experience that you can have fun games, and even
be reasonably competitive at the university-club-level, without ever
explicitly counting points in your head.

------
zubspace
Google improved the outcome by putting in large amounts of processing power.
What happens, if humans would do the same?

Instead of just Lee Sedol, how about putting the top 10 Go players in a room
vs. AlphaGo? Would the chance to win increase?

Maybe we find out, that 3 top go players vs. AI is the optimal way and adding
more humans decreases the odds to win the match?

This would lead to following question: Why does AI improve, if we add more
processing power while adding more human brainpower decreases their overall
power?

Maybe we find out, that 3 good developers working on a project are optimal and
more decrease the chance of success?

~~~
SixSigma
With multiple humans, the co-ordination and communication would dominate.

Do you think the humans would win at Twitch Plays AlphaGo ?

~~~
crazysim
Knowing Google, it would be a good promotion to have a Youtube Plays AlphaGo.

------
goquestion
What's needed to use programs like AlphaGo to enhance human enjoyment of Go
(and other games like chess where i have more experience)? I'm more interested
in this than in the "man vs. machine" narrative.

Ideally we could take AlphaGo and produce an algo that can smoothly vary its
playing proficiency as a human opponent increases in skill. The problem I've
seen in chess computers is that setting them to "amateur" results in 3-4
grandmaster-perfect moves followed by a colossal blunder to enable the human
opponent to catch up.

Ideally you could use a computer opponent as an always-available, continuously
adapting challenger to train hard against all the time.

~~~
bnewbold
Nothing! This is already very much a "thing" in the Go community. Bots like
GNU Go, CrazyStone, and Zen are all running under multiple accounts on most of
the popular online Go servers (KGS, etc). There are enough bots of differing
age and ability that one can either hop up a chain of different bots or try to
configure a strong bot into a weak configuration. The GNU Go bot is also
downloadable free software and is frequently integrated into, eg, mobile apps
(it is, however, old and not as strong as other bots, I think around 6k
level). The game of Go also has a wonderful and essential handicap system to
allow players (human or bots) of differing abilities (within a reasonable
range; eg not possible for a novice to play an even game against Lee Sedol
even with 9 stones).

As far as I can tell the vast majority of amateur players play against bots
online and review games to improve their skills. It would be nice if it was
easier to select a bot with a given skill rating, but you can figure this out
pretty easily by playing some games or reading up on bots. Playing against a
skilled human who cares about your advancement is still the best way to
advance though, in my opinion. Getting good feedback on your mistakes and
style of play is extremely helpful.

------
conanbatt
One interesting thing happened to me: I got to the game before knowing which
side was which color or the result, and I could tell which one was the
computer, an exercise I hadn't tried with the previous games.

------
dopamean
I've gone from thinking "it will be impressive if AlphaGo wins a game in this
series" to "wow, it's pretty impressive that Sedol took a game off AlphaGo."
Craziness.

------
brador
I question how much of it's success is down to the AI/understanding the
game/outplaying the opponent vs. simple culled brute force. Especially when
they can throw Google level computing power at it and they have mentioned
using heat maps and looking at move sets.

It could be argued that it's only AI when it understand the game rules and
plays to them without iterating random choices until it finds a hit. Machine
learning would be between the two, but still not what many would consider true
AI.

~~~
gus_massa
I played chess instead of go, but I think they are similar enough ...

When you play, you consider a few possible movements, and a few possible
responses of the other player, and in each case a few possible response of
you, and ... I think amateur players like me consider only 3 or 4 levels
(unless it's some easy but interesting situation like a multiple capture
chain) but professional players consider much deeper trees. So humans also
iterate randomly, but usually we prune the tree more aggressively for the time
and memory constrains of the current implementation.

Unless you are Capablanca :). There is a famous fake quote from Capablanca
that says "I see only one move ahead, but it is always the correct one." It's
probably fake, but it's funny. More info and similar quotes:
[http://www.chesshistory.com/winter/extra/movesahead.html](http://www.chesshistory.com/winter/extra/movesahead.html)

~~~
brador
You are correct, but you are playing the game. You see and process the
possibilities based on your understanding of the chess ruleset.

With machine learning with brute force you are simply trying X possibilities
until something sticks and gives a high % of win state. That's different to
playing the game using knowledge of the ruleset, even though, most of the
time, the end result is the same.

This is what killed AI research in the 80s. That moment when everyone
collectively saw they were simply working on a more powerful culled brute
force (pruned tree as you call it) when they all thought it was true AI.

True AI is hard. The required computational resources are immense even for
something simple. Take a Bishop on a chess board. How would you tell an AI the
ruleset that the Bishop moves diagonally only? It must first understand what
it is looking at, then what diagonally means, then what diagonally means in
this particular context. All with nodes of pattern matches and an input
stream.

I feel these young guns are falling into the same trap of calling machine
learning AI without the benefit of experience an older researcher would have,
having been through this situation before.

~~~
visarga
Actually, teaching AlphaGo the rules was easy. And what you call brute force
is in fact intuition based search. It learns to guess by intuition (policy
net) what moves to try and to give up (value net) the bad ones. It's far from
brute search, and that's why AlphaGo is so much better than the other Go
software.

------
ioncube
Anybody knows the music playing in the background?

-EDIT- Thats what Shazaam was able to recognize Hit Me! - Dreamliner - [https://soundcloud.com/hit-me-music-production/dreamliner](https://soundcloud.com/hit-me-music-production/dreamliner)

~~~
zeeZ
There's also Somnium - Bright Future somewhere in there.

~~~
zeeZ
Coldplay - Adventure Of A Lifetime

Also in the mix, just came up in shuffle.

------
mikhail-g-kan
Yann Lecun commented AlphaGo regarding General AI:

[https://www.facebook.com/yann.lecun/posts/10153426023477143](https://www.facebook.com/yann.lecun/posts/10153426023477143)

in short - we are at least 1 big step before creating human-level AI

------
sebkomianos
Excuse my most probably naive approach at this but aren't such games very
unfair at this point? AlphaGo has been trained with data provided by humans
while Lee Sedol played against an AI opponent of such calibre for the first
time, no? Is it a false assumption that with the right training humans will
take the upper hand again?

------
thatsadude
The game is amazingly close though.

~~~
the_mitsuhiko
I wish they counted it. Was curious to see how that works.

~~~
awwducks
Here's a tutorial.

[http://playgo.to/iwtg/en/count.html](http://playgo.to/iwtg/en/count.html)

The whole series isn't bad if you're interested in other aspects of the game.

~~~
the_mitsuhiko
They would have counted with the Chinese method which is different.

------
yangtheman
Would it be conceivable that similar type of AI can deploy and manage unmanned
military vehicles, e.g. unmanned drones and tanks, and monitor battle progress
(assuming that the other side is managed by human)? It wouldn't necessarily be
turn-based, but constantly evaluating its moves against changing environment
outside its control and reach its objective? I think such future is
conceivable and scary at the same time.

------
tetraodonpuffer
note that form whomever is interested reddit will do an AMA with all the
various pro commentators this Saturday it seems, check r/baduk for more
information

[https://www.reddit.com/r/baduk/comments/4ai8e8/what_do_i_nee...](https://www.reddit.com/r/baduk/comments/4ai8e8/what_do_i_need_to_know_about_setting_up_the_ama_w/)

------
xefer
I'm curious, if these machines can consistently beat 9-dan players like this,
is there talk of creating a 10-dan level?

~~~
xzephyr
You can just have the ELO score, which is the basis for assigning dan levels:
[http://www.goratings.org/](http://www.goratings.org/)

------
partycoder
It was very close though. Lee Sedol resigned in the yose after noticing he was
behind by no more than a moku.

------
eatbitseveryday
I wonder what factors of game play create different advantages. For example,
if per-turn time limits are below some threshold, whether humans would be at
an advantage. It would certainly make for an interesting game.

------
haffi112
Next challenge: Can AlphaGo beat 10 or 100 humans playing together against it?

------
marcell
I've heard that in Chess, there are specific strategies that are effective
against computers, but not against humans. Is this the case, and is it
possible to do anything similar in Go?

~~~
sobellian
This was the case many years ago because of limited search depth and simple
evaluation heuristics. For example, chess engines used to be known to be
overly attached to material; piece sacrifices for long-term compensation used
to confuse them.

Those problems have since gone away. Anti-chess-computer tactics are dead. A
human would be very happy to play a modern chess engine to a draw, and winning
is not even in the discussion.

------
anocendi
When it comes to competitive gaming, Koreans _were_ Gods, and now AI has come
to top them.

Before we realize it, we will hear Google's StarCraft 2/3 bot beating the
Korean World Champion.

------
ep103
Does anyone have any suggested ways to learn the algorithmic techniques
alphago uses? I've heard monte carlo tree search, and neural nets both
mentioned

------
atrudeau
Has anyone found the 15 minute summary for match 5? They had them for the
first 4 matches. Really great stuff.

------
pervycreeper
Could anyone suggest a good introduction to the rules and basic strategies of
this game?

~~~
kqr
[https://online-go.com/learn-to-play-go](https://online-go.com/learn-to-play-
go)

May need an account, but registration takes 5 seconds (you just type in your
desired username and password). It puts you through 10 simple lessons and then
lets you play a really easy computer opponent.

~~~
pervycreeper
Thanks.

------
guilhas
Shouldn't Lee have had the opportunity to train 1 year against this AI before
the competition?

training (Human vs Human) not the same as (Human vs Algorithm) or (Algorithm
vs Algorithm)

------
nickjj
Is it just me or is it really lame that Google isn't going to pay Lee?

From the article:

> Lee was competing for a $1 million prize put up by Google, but DeepMind's
> victory means it will be donated to charity.

So Lee provides Google knowledge that only he is capable of providing due to
his extreme skill in the game and Google won't even pay him?

~~~
asherkin
He's getting $170,000, $150k for playing the 5 matches, and $20k for winning
match 4.

~~~
nickjj
Ah. I wish they included that in the article.

I'm all for open source and unleashing Skynet, but I was concerned someone as
important and skilled as Lee will be to improving AI was going to go unpaid.

------
vbezhenar
What I'm finding terrifying is that we compare best players of entire human
population to machine. Even if machine is only barely on the same level,
honestly, 99.9% of people probably wouldn't stood a chance against it, no
matter how hard they would be trying. Those professional players are best of
best.

Now compare that level of AI with average person. Go game might not be
directly applicable to our lives, but it's only a demonstration. And it's
replicable as easy as copy'n'paste, compare that to amount of time, money and
efforts required to grow and train a human.

That future where not only drivers and factory workers are replaced by robots,
but anyone who's not doing extremely intellectual work, is getting closer.
Factory robot is not that cheap, it requires manipulators, repairs. But cheap
office work does not require anything physical, it's replicable extremely fast
and gonna cost very low. It's exciting and terrifying feature. It's not going
to look well with current capitalistic economical model.

