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Comment by Ensorceled | Hacker News Reader

> AlphaGo plays some unusual moves that go clearly against any classically trained Go players. Moves that simply don't quite fit into the current theories of Go playing, and the world's top players are struggling to explain what's the purpose/strategy behind them.

Could AlphaGO be winning in a way similar to left handed fencers having an advantage over right handers by wrong footing them rather than simply being better? Would giving Lee more chance to see this style give him a chance to catch up?

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I'm not a Go player but play other competitive sports. Humans have a herd mentality...as Op mentioned there's certain styles of playing...which has their own strengths and weaknesses. Sometimes people will not examine other styles that may have better strengths and just focus on the exist one. Then comes along someone who 'thinks outside the box' with a new style and revolutionize the playing field.

Think Bruce Lee and the creation of Jeet Kune Do. Before him everyone concentrated on improving one style by following it classically, rather than just thinking of 'how do I defeat someone'.

IMHO Lee is the best at the current style of Go. AlphaGO is the best at playing Go. Maybe humans can devise a better style and defeat AlphaGo, but I'm sure AlphaGo can adapt easily if another style exists.

Lee isn't even the best human player at the moment, he has a 2-8 loss record against Ke Jie, who's actually ranked number 1 at the moment.

Ke Jie is an arrogant 18 year old and he's been saying on social network in the past couple days how he will defeat AlphaGo.

He seems to have backed off that claim after the second game.

Exponential progress is going to bear down on Ke Jie like a ton of bricks soon.

I've seen this happen with "modern tennis" versus how I was taught to play.

This is interesting. Could you (or someone else whose had this experience) elaborate?

Here are three examples for you.

Swimming. It used to be that swimmers were supposed to be streamlined and avoid bulky muscles. Then a weightlifter decided he wanted to swim. Swimmers today all lift weights.

Programming. It used to be that people built programs in a very top down, heavily planned way. Think waterfall. We now understand that a highly iterative process is more appropriate in most areas of programming.

Expert systems. It used to be that we would develop expert systems (machine translation, competitive games, etc) through building large sets of explicit rules based on what human experts thought would work. Today we start with simple systems, large data sets, and use a variety of machine learning algorithms to let the program figure out its own rules. (One of the giant turning points there was when Google Translate completely demolished all existing translation software.)

Serve-and-volley is pretty much non-existent in modern professional singles tennis. We were always taught to attack the net, and every action was basically laying the groundwork to move forwards and attack.

Nowadays, top players slug it out baseline-to-baseline.

In terms of stance, we were taught to hit from a rotated position where your shoulder faces the net, and a normal vector from your chest points to either the left or right side of the court.

Nowadays, it's much more common to hit from an "open" position, where your body is facing the net, not turned. This would have been considered "unprepared" or poor footwork in my day, but it actually allows for greater reach. It does make it more difficult to hit a hard shot, but that's made up for by racquet technology and generally stronger players.

If you're in the mood for some long form literary tennis journalism about this subject, check out David Foster Wallace's Federer as Religious Experience from 2006.

http://www.nytimes.com/2006/08/20/sports/playmagazine/20fede...

Although it takes a few paragraphs until it gets into the details of "today's power-baseline game."

> AlphaGO is the best at playing Go. Maybe humans can devise a better style and defeat AlphaGo, but I'm sure AlphaGo can adapt easily if another style exists.

Which is a curious point. The gripes about early brute force search algorithms (e.g. Deep Blue?) were that they felt unnature.

However, as the searches get more nuanced and finely grained, is there a point at which a fast machine begins doing fast stupid machine things quickly enough to feel smart?

Are there any chess / Go analogs of the Turing test? Or is a computer players always still recognizable at a high level?

It has been said that a game of Go is a conversation with moves in different areas showing disagreement. The game is also known as 'shou tan' (hand talk). From the commentary, AlphaGO is currently passing the Go Turing Test in almost all cases. There are some moves which some say are uncharacteristic, then later play out well. Or so called mistakes not affecting the outcome of the match. One explanation given was that AlphaGo optimizes for a win, not win by greatest margin, which is a/most valid for human or machine.

Computer players will be recognizable as long as they are designed to win, and not to play the way a human plays.

A Turing test for game players is an interesting idea, it would be useful for designing game players that are good sparring partners rather than brutes that can whipe the floor with you.

Bruce Lee played it very smart and attained a guru status in the West, but there's no evidence he was a world-class fighter, only unsubstantiated claims by his entourage.

As for JKD, people are drawn in by its oriental esotericism, but there's no evidence it is an especially effective fighting style, or that it has something that (kick)boxing does not.

Absolutely! And it doesn't matter in the end...

Remember that AlphaGo has spent months developing its own style and theory of the game in a way that no human has ever seen. Its style is sure to have weaknesses, but humans will have a hard time figuring them out on first sight.

Similarly chess computers do better in some positions than others (they love open tactics!) and one of the games that Kasparov won against Deep Blue he won by playing an extreme anti-silicon style that took advantage of computer weaknesses. However Kasparov didn't have to figure out what that style was because there was a lot of knowledge floating around about how to do that.

Therefore I'd expect that Lee Sedol from a year from now could beat AlphaGo from today. And human Go will improve in general from trying to figure out what AlphaGo has discovered.

However that won't help humans going forward. AlphaGo is not done figuring out the game. At its current rate of improvement, AlphaGo a year from now, running on a single PC, should be able to beat the full distributed version of AlphaGo that is playing today. Now the march of progress is not whether computers can beat professionals. It is going to be how small a computing device can be and still beat the best player in the world.

But when the weaknesses it has require looking 20 ply into the game, can anyone exploit those weaknesses? And furthermore, if the computer itself is able to see 20 ply into the game, then it can spot its own weaknesses and you need to look even further, making the question of whether it's really a weakness.

Weaknesses are only relative to capabilities of the opponent to exploit them. If a tank has a weak spot that rockets can hit, but it's being opposed by humans on horseback, is it really a weakness in that context?

The weaknesses that it has will be of the form that it has wrong opinions about certain kinds of positions. In the case of chess, those weaknesses showed up in closed positions where the program owned the center and large amounts of space. In the case of AlphaGo, the weaknesses will be much more subtle, but will be discoverable and exploitable in time.

Additionally AlphaGo has the advantage that it started with a database of human play, so it has some ideas what kinds of positions humans miscalculate.

As for your tank vs horseback analogy, that's flawed at the moment. AlphaGo is probably reasonably close in strength to the human facing him. Improved human knowledge could tip the balance.

However in the future it will become an apt analogy. Computers are going to become so good that knowing the relative weaknesses in their style of play may reduce the handicap you need against them, but won't give you a chance of becoming even with them. That happened close to 20 years ago in chess, and is now only a question of time in Go.

I wonder if AlphaGo has some specialized knowledge to handle ladders, where stones can have an effect at a distance that might only come into play after 20 moves.

>I wonder if AlphaGo has some specialized knowledge to handle ladders

Yes. A representation of ladders is among the input features of its neural networks.

https://gogameguru.com/i/2016/03/deepmind-mastering-go.pdf

Stone colour 3 Player stone / opponent stone / empty

Ones 1 A constant plane filled with 1

Turns since 8 How many turns since a move was played

Liberties 8 Number of liberties (empty adjacent points)

Capture size 8 How many opponent stones would be captured

Self-atari size 8 How many of own stones would be captured

Liberties after move 8 Number of liberties after this move is played

Ladder capture 1 Whether a move at this point is a successful ladder capture

Ladder escape 1 Whether a move at this point is a successful ladder escape

Sensibleness 1 Whether a move is legal and does not fill its own eyes

Zeros 1 A constant plane filled with 0

Player color 1 Whether current player is black

(The number is how many 19x19 planes the feature consists of.)

20 moves may sound like a huge number of variations but when you prune things early, it can be quite manageable. The alphabeta algorithm in chess does pruning quite a lot.

I would also posit that lefties' advantage basically disappears once you get to a certain level in fencing. Past some point, it's basically all just footwork anyways, and your orientation doesn't change the distance of your target (foil and saber at least, can't comment on epee as they seem to just kinda bounce in place a lot even at Olympic level).

Can confirm. My brother fenced at a club that had a lot of lefties. All the righties got used to it quickly, and had no real disadvantage when playing against lefties.

I could easily see the difference in tournaments with other clubs that were not used to left handed players.

Seems unlikely. Training was partly from human games, and partly from self play; if there's some new, off book heuristics at play, there's no way to know that humans would respond poorly to them. Though I suppose it's possible it would notice that humans do poorly on simply off book moves generally.

Why does this seem unlikely? Humans do poorly with "off book" moves in general in sports and other games; it's why new styles of play or management work really well until others get used to them. Why would it be unlikely in Go?

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