[mgoetze]

awm, on 2016-March-15, 16:29, said:

Further, part of what's exciting from an AI standpoint is that AlphaGo is built on a very general machine learning framework. There isn't really human "expert knowledge" being hard coded in the way most chess programs have.

I somewhat disagree. I realize this wasn't the point you were making, but assuming that this approach is generalizable to all sorts of problems (Mr. Hassabis' favourite example being "healthcare") seems dangerous to me.

1. The way the two neural networks are connected is already an encoding of expert knowledge, as this is exactly what an expert Go player does: read some key variations and evaluate the board at the leaf nodes. While Google claims that even the Policy Network alone can be moderately successful, it would obviously not be impressive enough to create all this hype.

2. Google found that a mixed evaluation between the Value Network and Monte Carlo rollouts was more successful than the Value Network alone. Monte Carlo rollouts, at a minimum, actually encode human expert knowledge about "eyeshape" to make sure that the game comes to a reasonable end rather than the players suiciding all their stones. The previous state of the art programs included even more hand-crafted patterns in their Monte Carlo playouts, and AlphaGo might do this also.

3. The training data builds on 1000+ years of accumulated human knowledge. Consider that, for the very first move of the game, there are 55 possibilities after accounting for symmetry. Only 2 of those are commonly played in professional games, with a further 2-4 considered potentially viable. AlphaGo so far has not deviated from those top 2 moves. While it is very exciting to Go players to see what AlphaGo would come up with if trained only on self-play, there is no guarantee that it would lead to a strong program in a reasonable timeframe.

Most importantly, it seems to me that the parametrization of the neural network is very significant. Note that the inputs to AlphaGo's networks include whether a stone/group is capturable in a ladder. This is pretty Go-specific obviously! And Go is a very well bounded problem - finding the correct parametrization of a neural network for a more fuzzy problem is going to be quite nontrivial.

[barmar]

One of the ways you can tell when a story has hit the mainstream is when it gets spoofed on a late night comedy show. Last night AlphaGo hit this, as The Daily Show did a bit about AI that was mostly about this.

I'm still hoping for SNL to spoof the bridge cheating scandal (I vaguely remember a few jokes about Disa's "doping" violation a few years ago).

[barmar]

Today's SMBC is apropos

http://www.smbc-comi...dex.php?id=4075

(I'm just linking instead of embedding because there's some NSFW content.)

[WesleyC]

My strong opinion on the topic of computers in bridge is that if *serious* effort was put into building a world-class bridge program, and it was allowed to play unrestricted by ACBL/WBF system constraints (with full disclosure, obviously) then it would easily win the BB.

The only reason it hasn't happened yet is because

a) The issues regarding disclosure (mostly towards the computer) represent a huge grey area.
b) There is no demand for such a computer.

Now all we need is for someone to offer a 100 million dollar bet, and i'll be able to prove myself correct!

Any takers?

[Mbodell]

barmar, on 2016-April-09, 10:47, said:

Today's SMBC is apropos

http://www.smbc-comi...dex.php?id=4075

(I'm just linking instead of embedding because there's some NSFW content.)

There was a game designed to make it hard for computers to win, Arimaa (https://en.wikipedia.org/wiki/Arimaa), but computers defeated humans at that in 2015 (there was a 10K challenge each year since 2004).

[1eyedjack]

Mbodell, on 2016-April-09, 17:08, said:

There was a game designed to make it hard for computers to win, Arimaa (https://en.wikipedia.org/wiki/Arimaa), but computers defeated humans at that in 2015 (there was a 10K challenge each year since 2004).

I have never heard of arimaa before this thread, but having read the wiki article I cannot understand why the designers thought that this would be particularly hard(er) for a computer to learn to play well than, say, chess, go, Othello, etc

[lycier]

Last night China CCTV1 program : Start To Lecture
Lecturer : Nie Wei-Ping ( China Go sage )
Summary :
- I should call AlphaGo as Teacher Alpha, it is far more stronger than human.
- AlphaGo gives us a shocking education.

If you are chinese, you can watch it. Its link : http://tv.cctv.com/2...60o160410.shtml

[Mbodell]

1eyedjack, on 2016-April-10, 01:04, said:

I have never heard of arimaa before this thread, but having read the wiki article I cannot understand why the designers thought that this would be particularly hard(er) for a computer to learn to play well than, say, chess, go, Othello, etc

Because each side takes 4 moves at a time in a single turn, that raises the branching factor by an exponent of 4, more or less. There are at most 361 moves per turn in a 19 x 19 go board (and decreasing over time, plus some symmetry early) - all over about 250 on average for the branching factor of go. In a typical chess position there are about 35 moves per turn for chess. For Arimaa you'd expect that could mean about 35^4 = 1,500,625. In actuality, because the pieces in Arimaa don't move quite as freely as the pieces in chess it is quite a bit less than that, and the real measured average branching factor for Arimaa is 17,281. But still, it was largely the branching factor for go with 250 >> 35 that made go considered much harder than chess. And 17,281 >> 250 which makes Arimaa much harder as a game.

Since a lot of computer uses some form of minimax processing considering what is my best choice move assuming you make your best move assuming I make my best move, etc. it means that you are doing a search down these branching factors. So if you are looking 4 ply (2 turns for each of us) down the tree than in chess you need to worry about 1,500,625 possible sequences of moves. For go it would be about 250^4 = 3,906,250,000 possible sequences. In Arimaa it would be about 17281^4 = 89,181,645,395,627,521 possible sequences. Now all of these algorithms would have optimizations with pruning and symmetry and memoization of same position from different paths and other optimizations. And it is possible static evaluation functions for the various games are different degrees of difficulty, but still the branching factor is an obvious reason why the game would be hard for traditional computer AIs.

[billw55]

WesleyC, on 2016-April-09, 11:14, said:

My strong opinion on the topic of computers in bridge is that if *serious* effort was put into building a world-class bridge program, and it was allowed to play unrestricted by ACBL/WBF system constraints (with full disclosure, obviously) then it would easily win the BB.

"Easily" is an overbid. Bridge results are limited to some extent by luck.

Also, what difference would it make to remove system restrictions? Do you imagine bots that will create their own bidding systems?

lycier, on 2016-April-10, 07:01, said:

Last night China CCTV1 program : Start To Lecture
Lecturer : Nie Wei-Ping ( China Go sage )
Summary :
- I should call AlphaGo as Teacher Alpha, it is far more stronger than human.
- AlphaGo gives us a shocking education.

If you are chinese, you can watch it. Its link : http://tv.cctv.com/2...60o160410.shtml

She does not think Ke Jie can beat alphago?

[1eyedjack]

Thanks to Mbodell for that insight. I would have thought that the same factors that make it hard for a computer would also make it hard for a human (to do well). But I have not given it a lot of thought.

[lycier]

billw55, on 2016-April-11, 06:06, said:

She does not think Ke Jie can beat alphago?

It should be he.
Nie Wei-Ping think apparently AlphaGo skill is far more strong than the highest skill of all the human Go professional players. AlphaGo makes human feel fear!
Of course, Ke Jie probably can't beat AlphaGo.
P.S.
Google CEO came to Beijing several days ago,dicussed challenge match.

[barmar]

billw55, on 2016-April-11, 06:06, said:

"Easily" is an overbid. Bridge results are limited to some extent by luck.

That's why you play long matches, to reduce the luck factor.

Notice that we typically see most of the same faces in the late rounds of major bridge events like Spingold and Bermuda Bowl -- how often is there a real surprise win? That implies that ability is the major factor in who wins, not luck. Therefore, if we could make a bridge program that's better than all the human bridge champions, it should be able to win. And if it's much better, it should win consistently.

[WesleyC]

billw55, on 2016-April-11, 06:06, said:

"Easily" is an overbid. Bridge results are limited to some extent by luck.

Also, what difference would it make to remove system restrictions? Do you imagine bots that will create their own bidding systems?

Fair call - "go in as short priced favourites" is probably more accurate.

Forcing computer bridge engines to play a standard method is the equivalent of forcing a chess engine not to deviate from standard opening lines.

Given computers have no limitations on memory or complexity, they would gain a huge advantage from being allowed to devise a complex and completely optimal, (and artificial) relay bidding system that attributed a precise meaning to every possible auction and continuation.

[barmar]

WesleyC, on 2016-April-11, 09:04, said:

Given computers have no limitations on memory or complexity, they would gain a huge advantage by being allowed to devise an entirely artificial relay bidding system that incorporated all kinds of revolutionary ideas.

Why shouldn't computers be subject to the same system restrictions as humans? They should play the same game as we do. We have to figure out how to find the best game within these limits, so should they.

Sure, one of the reasons human bidding systems have only limited artificiality is because most of us wouldn't be able to remember dozens of artificial bids, so the restrictions are to some extent our own making.

There's also an inherent limit to artificiality: someone needs to bid naturally before you get past the safe level. So even though there are 34 legal responses to a 1♣ opening, potentially allowing you a huge number of messages you can send, most of them will get you past a contract you can make.

[billw55]

lycier, on 2016-April-11, 06:42, said:

It should be he.

Oops

lycier, on 2016-April-11, 06:42, said:

Google CEO came to Beijing several days ago,dicussed challenge match.

That could be interesting, even if I don't expect a different result.

[WesleyC]

barmar, on 2016-April-11, 09:13, said:

Why shouldn't computers be subject to the same system restrictions as humans? They should play the same game as we do. We have to figure out how to find the best game within these limits, so should they.

Sure, one of the reasons human bidding systems have only limited artificiality is because most of us wouldn't be able to remember dozens of artificial bids, so the restrictions are to some extent our own making.

There's also an inherent limit to artificiality: someone needs to bid naturally before you get past the safe level. So even though there are 34 legal responses to a 1♣ opening, potentially allowing you a huge number of messages you can send, most of them will get you past a contract you can make.

Why are system restrictions imposed at all?

In a practical (read: human vs human) situation, there are many good reasons to impose some system restrictions. They make disclosure easy, create a level playing field and generally facilitate an enjoyable game.

But if you consider bridge in it's purest form, the rules are elegantly simple. You must make a legal bid and that bid can mean anything as long as you fully disclose the meaning to your opponents. Computers would have absolutely no issue following the disclosure aspect - they could even deal out a huge sample of hands that fit partner's bidding pattern on request and present them to the opponents as an example.

However complex methods have enormous unrealized potential. Just one example (which is legal even with the current rules) is that it's possible to exchange a key (specifically possession of some subset of particular cards) which allows you to encrypt a message that only your partner can understand. A method that I've seen allows you to check for a major fit, without revealing to the opponents which major either player holds.

Have a look at the final page of this system card for a full explanation.

http://livebridge.ne...6791-497746.pdf

[lycier]

billw55, on 2016-April-11, 09:37, said:

That could be interesting, even if I don't expect a different result.

More details didn't be disclosed, it appears that Google doesn't arrange to hold AlphaGo Vs human event recently.
here I post three pictures.



Kejie and Sundar Pichai

[lycier]

In 2015, Kejie, was only 18 years old, he won the prize of 7.65 million Yuan RMB, net income was more than 4.3 million Yuan RMB.

[awm]

System restrictions rarely impose any limits beyond openings and initial overcalls. Even the ACBL mid-chart has few restrictions beyond this; WBF has virtually none.

An advantage computers have is ability to memorize many long sequences without error. This will matter more in long and rare auctions, not the openings. So I don't see much difference made by system restrictions here.

There are issues with communicating meanings of bids between computers and humans, especially in the area of when/how an agreement may be violated.

In any case, watching late rounds of top level matches there are enough clear errors (fatigue probably a factor) that it's easy to believe a serious effort at computer players could win consistently.

[barmar]

WesleyC, on 2016-April-11, 11:48, said:

But if you consider bridge in it's purest form, the rules are elegantly simple. You must make a legal bid and that bid can mean anything as long as you fully disclose the meaning to your opponents. Computers would have absolutely no issue following the disclosure aspect - they could even deal out a huge sample of hands that fit partner's bidding pattern on request and present them to the opponents as an example.

That's a horrible method of disclosure. Seeing a huge sample of hands doesn't tell you which features of those hands are relevant to the situation at hand. It would be like the encyclopedia entry for "dog" just having pictures of dozens of dogs, without any words explaining how they differ from wolves.