
AlphaGo – The Movie [video] - wallflower
https://youtube.com/watch?v=WXuK6gekU1Y
======
brilee
Some updates on Computer/Human interaction from a Go AI author. Opinions are
my own.

In the Go world, the relationship between computer and human has turned into
one of analysis. Every pro in Asia has a powerful GPU-equipped system with
LeelaZero or KataGo or Minigo installed, and they use it to study openings and
analyze their matches. LZ/MG are similar in that they're from the AlphaGoZero
generation of bots, and thus are hardcoded to 7.5 komi and don't play well
with handicap stones. KataGo is the open-source project with the newest ideas,
using (among other things) territory ownership prediction, allowing it to
handle arbitrary komi and handicap. Many pros are switching to it for this
reason. All TV broadcast channels use AI for both winrate prediction and
variation analysis. They'll report the predicted winrates by all major bots.

There are definitely AI-inspired opening sequences, but I would say that by
and large, pro play has stabilized to just be better. A few years ago, pros
would just mimic AlphaGo just to see what would happen, but there's becoming a
better appreciation for the many different opening styles that are becoming
possible, now that we have AI to analyze them. The strongest pros are the ones
who spend the most time on their computers.

Despite a few extra years of research, all available AI systems still fail at
ladders regularly. Some may have had ladder routines hardcoded in, but these
routines still fail at loose ladders and the many wonderful ladder variants
out there.

In the online Go world, cheating has become rampant. Top echelon of
games/accounts are essentially just people competing on how beefy their GPUs
are, and human professionals complain that they can't actually get a game
against another person nowadays.

Professional tournaments / pro qualifying exams have had isolated incidents of
cheating which are usually fairly obvious, as human playstyle is still
recognizably different from computer playstyle.

~~~
shmageggy
This basically describes modern chess. Top pros use ultra-deep computers to
find opening novelties. Some isolated incidents of cheating in real
tournaments. There is some cheating online still, but the novelty wore off
long ago so it's not endemic, plus most only play fast time controls online
because it's much harder to cheat.

------
joeblau
[SPOILERS] To me there are five super interesting points in this video.

1\. The arrogance/confidence that people had towards AlphaGo assuming that it
was not going to be competitive.

2\. The lack of understanding tt even the programmers had for the game. At one
point the devs were saying "I don't even know if that's a good move or not",
yet their program is playing at superhuman play.

3\. The amazement that commentators and other players experienced when AlphaGo
performed move 37 in game 2.

4\. The disappointment experienced by everyone when they came to the
realization after game 3 the computer won.

5\. The confusion when the computers neural network fails in game 4 and
everyone (except the engineering team who sees the probabilities) is confused
about whether the machines play is brilliant or terrible.

~~~
esjeon
Just random thoughts:

> 1\. The arrogance/confidence that people had towards AlphaGo assuming that
> it was not going to be competitive.

This is pure stupidity. Those people didn't realize the nature of the game. Go
is a (large) confined space, which, by nature, can be brute-forced for the
best answer. ML was needed simply to trim and optimize search tree, to find
the best _local maxima_ from a broader range (than other AIs).

> 2\. The lack of understanding tt even the programmers had for the game. At
> one point the devs were saying "I don't even know if that's a good move or
> not", yet their program is playing at superhuman play.

You can't even predict solutions from a simple DFS. Right things are not
always obvious nor intuitive.

> 5\. The confusion when the computers neural network fails in game 4 and
> everyone (except the engineering team who sees the probabilities) is
> confused about whether the machines play is brilliant or terrible.

It's likely that AlphaGo didn't look beyond "horizon"[1], and only realized
it's losing after a dozen moves. In this sense, move 78 in game 4 itself is
more amazing than move 37 in game 2. The only reason why the latter is
uncommon is it's _believed_ that it's difficult to build house in the
sky(middle), thus one should keep stones close to ground(border), but that
naturally depends on the situation.

[1]:
[https://en.wikipedia.org/wiki/Horizon_effect](https://en.wikipedia.org/wiki/Horizon_effect)

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tobr
I watched the entire thing, but I know close to nothing about Go. I found it
frustrating that they didn’t attempt to explain what was so unexpected about
some of the specific moves, but just focused on everyone’s reactions to them.

Is there somewhere I could read a beginner-friendly explanation of what was so
significant about the moves, and how and why they changed the games?

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viig99
Lee sedol is a pretty cool guy and a normal human being, albeit the crazy
ability to remember games likes maps, do watch the latest episode of 'master
in the house' a korean variety show which featured lee sedol as the guest.

~~~
ipnon
I'm amazed that any 1 person can even compete with a team of dozens of Ph.Ds
and teraflops upon teraflops of processing at all.

------
londons_explore
Can anyone who has watched the whole thing give a summary for those of us not
sure if we should dedicate a full 90 minutes to this?

~~~
jasode
It's a "human interest" type of documentary which means it's deliberately
structured as an "emotional narrative".

I think it's fair to say that a lot of "no-nonsense-just-give-me-the-facts"
viewers in the HN demographic would find this type of film very unsatisfying.
I.e. lots of interviews with virtually no technical explanations.

I quickly scrubbed the film and it looks like it's the same footage from the
AlphaGo documentary I saw ~2 years ago:

[https://www.imdb.com/title/tt6700846/](https://www.imdb.com/title/tt6700846/)

I don't know if this new video url dated "2020" is a different film or if they
simply enhanced the 2017 documentary with new footage.

~~~
okareaman
> a lot of "no-nonsense-just-give-me-the-facts" viewers in the HN demographic
> would find this type of film very unsatisfying

I disagree with this assertion. The film explores the idea that AlphaGo, like
the game of Go itself, raises a lot of questions about what it means to be
human. The relationship between AI and humans is developing and a topic of
intense interest, especially among the HN crowd. I think what you might be
saying is people with a high IQ but low EQ might not relate to this film. I'm
sitting here wondering if people like that don't find human/AI intersection
interesting because they are already mostly like a computer program themselves
so it's not a big deal.

~~~
jasode
_> , raises a lot of questions about what it means to be human. The
relationship between AI and humans is developing and a topic of intense
interest, especially among the HN crowd._

We're talking about 2 different things: the _importance of the topic_ vs the
the _method of exposition_.

You're emphasizing the topic's importance. I'm not disagreeing with that.
Instead, I'm explaining that the documentary's _style of exposition_ would be
a turnoff to a subset of the HN demographic.

As example showing differences in how Battle of Midway can be explained...

Here's the "personality-driven" type of history documentary. Scrub the video
and you'll see there's a lot of video showing _the narrators_ walking around
the beach and flying in helicopters:

[https://www.youtube.com/watch?v=wo9wrUcb8jI](https://www.youtube.com/watch?v=wo9wrUcb8jI)

To be fair, some viewers need attractive people talking to the camera to make
history "interesting". What does an attractive man walking along the beach
have to do with explaining strategy and tactics at Midway?!? Nothing. Maybe
some viewers need "eye candy" to go along with their medicine of history.
Basically, all of The History Channel is structured as personality-driven type
of documentaries with a human-interest angle.

Compare that to another style of delivery with no video of talking heads but
has a voice-over with maps and higher information content:

[https://www.youtube.com/watch?v=Bd8_vO5zrjo](https://www.youtube.com/watch?v=Bd8_vO5zrjo)

If HN viewers want the 2nd style of documentary for AlphaGo, this thread's url
is not it. The importance of "what it means to be human" is orthogonal to the
method of explanation.

~~~
okareaman
Thanks. I understand now.

------
akvadrako
Does anyone know how the go world has been evolving since AlphaGo?

I wonder if people are looking for weaknesses or perhaps just ways to
contribute, so that a human + AI could beat an AI playing alone.

~~~
roenxi
Openings have standardised around the patterns that the AI systems have
converged on, and people are practicing against neural networks/using neural
networks to review their games. That is probably increasing the standard of
play but it is difficult to measure.

~~~
arvinsim
Even though I am not a Go player, it is concerning. This because now money
comes into play rather than just raw skill.

Picture a brilliant but poor player. How would that person fare against
someone who has a good AI to practice against?

~~~
ALittleLight
Picture a brilliant but poor person who doesn't have the time to dedicate to
the game because they must deal with the constraints of being poor.

Buying high end computer hardware is very cheap relative to the time required
to become very good at go. Conversely, having a reasonably strong go program
available on your mobile will mean there's more access to people to play and
get analysis.

I would expect that AI makes it better for poor people. Previously, a non-poor
person might take classes or have a tutor. Now, a poor person can have an AI
analysis of each of their games if they want.

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Rerarom
Saw it on netflix last year. Good that it's public now

------
29athrowaway
[2017]

