In or around 2015, Google's AlphaGo was the original impetus that started a sort of evolutionary "Cambrian explosion" in the world of Computer Go AI. At this point it is fair to say the genie is out of the bottle and there is no going back. Go has existed in some form or another for roughly 5000 years, and it was only in the last year and a half that computers have suddenly reached a level that surpassed top pros. So while AlphaGo was the first, it will certainly not be the last. In a few years, more and more AI programs will proliferation their way to besting and beating the top pros at this ancient game. From the grand scheme of things, a few years compared to a few thousand years is but a blink of an eye. AI has won, forever, and there is no going back.

But that is not to say that in the future Human vs Computer Go games would be futile or meaningless. We must merely adapt and evolve the nature of the competition and the structure of the dynamic to make things more interesting and to put things on more common ground without giving a handicap or a surplus of moves or stones.

One way to naturally do this is to compete on energy parity. Kudos that the AG version that has won against Kie Je is reported to only use 10% of the total power requirement compared to the AG19 version of Lee Sedol games a year or so back. This is a huge step forwards but still it uses far more power than the human brain or even the entire human body. So as computers get stronger and stronger at Go, ultimately and eventually one metric to target would be the first computer AI Go program that can beat a top pro while using same equivalent to the top pro himself. This can be in the form of scaling down the AI to use less and less resources, or to artificially cap the thinking time of the AI, to give it less time and thus forcing it to use less total energy consumption, in order to match that of the human, so that at the end of the game the AI used no more total power or energy than its human counterpart. This would be an interesting goal post and qualitative challenge.

Another way to make human vs computer matches more intriguing is to give the human a limited number of redos or undos. To essentially allow the human to go back to a particular move, to play out a different branch to see if that new path or new route might entail a better end result or maybe even end up in a winning game. While this may seem like “cheating” at first glance, it is really not. Let me explain.

Computers have always been better than humans at brute force calculations. This has been decided since the days of the very first pocket calculator. Your mobile phone can calculate arithmetic operations orders of magnitude faster than all the top mathematicians in the world added together could do in terms of team mental math or group pencil and paper. But we should not confuse brute force calculations as “artificial intelligence” any more than we would say that a rudimentary autopilot program capable of holding an airplanes altitude without deviation is more “intellectual” than the human pilot. Likewise we would not attribute “artificial intelligence” to a fast calculator.

Everything from brains to computers are actually higher level emulators, at least second order or higher computers. It is the laws of physics and the individual quantum effects, the atoms and molecules and forces of the universe that combine together in such and such a way as to actually do the “calculation” and the “computer”. Everything else is a virtualization and symbolic computing layer on top of that. So be it an abacus or pocket calculator or slide ruler or human brain or silicon processor, these are all second/higher order computers within a computer, virtual emulators of processing, if you will. The reason Intel processors are faster than the abacus is because the integrated circuit is far smaller and thus more efficient than the larger macro-sized beads, but make no mistake, these are all symbolic representations of calculations, the smaller we can make these representations the more powerful of a processor we can have. But at the end of the day, the universe is the only true computer, and everything else is a higher level emulator, the only question is how efficient is the emulation and the symbolic emulation.

Humans are better than computers at some things while computers are much better than humans at a lot of other things. The question is fundamentally why?

It is akin to building a virtual CPU inside of Minecraft. It would be much more efficient not to have to have these higher order virtualizations which actually add to the symbolic unnecessities by artificially increasing computational overhead by orders of magnitudes and for no good reasons. This is why the human brain cannot compute arithmetic faster than a crude calculator. Because the brain was not hardwired to directly manipulate so closely with efficiency level the way a silicon processor can symbolically reduce numbers to more atomic bits of binary zeros and ones and thus calculate them much faster, and without error. It is not because the calculator is more advanced than the human brain, but that the human brain has to visualize and add symbolic layers to concept such as numbers (and cannot directly manipulate them on the molecular level) and thus just like in the earlier virtual CPU inside of Minecraft example, this is why humans cannot outdo computers when it comes to things like brute force arithmetic calculations.

Coming back to Go games, MCTS is akin to brute force calculation and not the sort of “intuitive artificial Intelligence” people think of when they think of AI outsmarting human intuition. Traditionally humans have had the advantage against computer Go programs of the past due to the large search tree of the Go game made it not possible to do an exhaustive brute force search. Therefore to a large degree this thing we call “human intuition” came into play, and without computers being able to emulate that intuition, there was no way computers were ever going to catch up by sheer scaling up of brute force searches. Modern AI Go programs have changed all of that because for the very time in human history, computer programs are now starting to catch up with human intuition when it comes to the game of Go. And with the advantageousness of brute force calculations that are not afforded to humans, the MCTS and other “brute force” simple algorithmic methods help carry Alpha Go and DeepZen and others the “last miles” to the “finish line”, finally boosting it above superhuman levels of overall effective play.

So the question is one of how do we highlight the true “intelligence” aspect of artificial intelligence and bring that real AI to the forefront and diminish the whole brute force aspect of modern “GO AI”, which in truth is actually a hybrid of AI and that of good old fashioned brute force calculations which humans were never good at and never had a chance or a hope against computers anyway.

A way to do that would be to give human players “redo” attempts. Since by definition Go is not a ‘brute force-able’ game anyway, (ironically, otherwise computers would have beat humans at Go back in the 1960’s without need for DCNN etc) a 25kyu player could have unlimited redo’s and could spend the rest of his life and would never win a single game against something like AlphaGo 2017. But on the other hand it is fair to say that someone like Ke Jie and Lee Sedol, given sufficient redos, might find a particular branch in which they win out against AG and the likes of top AI Go programs in the future.

Since humans are by definition fallible and machines are by definition do not get tired or sleepy and do not slip up and make “miscalculations” etc, it would only actually be “fair” for a human vs computer competition to allow the humans to have a set number of attempts per game.

Or if that is not acceptable, then a more natural way to level the playing field to be fundamentally more equitable would be for AI programs to rely ONLY on the neural network component for play and not use any MCTS or anything remotely like it. Another possibility would be to allow the human to have a real board to do analysis, to let him put down stones and to analyze the different variations in a manner that does not prone to victimize him to the fallibility of his own mind and memory and visualization which cannot ever compare to the brute force ‘speed of light’ search that a computer could instantly perform.

Finally, once GO AI programs have cast aside the MCTS and other functional equals to the MCTS methods and the brute force components that humans could never compete against and was never really ‘artificial intelligence’ anyway and then to rely solely and exclusively on the emulation of neural networks instead, then the next step would be to reduce the dataset the AI has access to in a way that is quantitatively fair to the human counterpart. Train an AI from scratch and only give it access to play a very limited number of games, on par with what a human would be exposed to in his lifetime.

What is intuition? It is ability to extrapolate and learn from a subset of data. AlphaGo has played hundreds of millions of games within the span of a year or two. Is this true intuition when a top pro could only ever hope to play a tiny fraction of a fraction of that in his entire lifetime?

So the ultimate goal in AI would be to reach energy parity with human brain itself, to only use Nueral Network and no other brute force searching and whatnot, and exposing it to a limited dataset of games, comparable to what a human would be exposed to.

In human vs computer games, humans should be given a generous number of redo’s. The human should be given much more time than the computer, the computer should be capped to only a few seconds thinking per move, and not allowed to think on human time, no komi should be given to the computer, human should be allowed to pick which color, and finally the human should be given access and ability to consult with other humans during the game, and as well as ability to use a real go board in live match to physically play out and experiment with variations.

Once automobiles became faster than runners I don't think too much time was spent on designing contrived races where the human might win...

Is there anything wrong with having the future Human vs Computer Go games becoming futile or meaningless? For me it's totally fine

I see two additional ways to make things interesting:

• Having human top pro playing against other human top pro
• Having supercomputer playing against other supercomputer

So then, we are left with 3 possibilities:

• Top pro VS supercomputer, while putting things on more common ground without giving a handicap or a surplus of moves or stones (ie purposely degrading the computer performance)
• Top pro VS top pro
• Supercomputer VS supercomputer

As far as I am concerned, options 2 and 3 already fulfill all my needs

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Now that I think about it, I would probably prefer watching Top Pro VS Supercomputer games with handicap (stones or komi) than with supercomputer degraded performance

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DeepMind promised to release so many things such as paper, self play games, analysis tool, but still sad that AlphaGo will step back from 'competitive play', is handicap game really too controversial to play against top pro?

Don't forget Pro/Bot Rengos! The AlphaGo-augmented game between Gu Li and Lian Xiao was quite fun.

No need for all these creative solutions. A strong enough computer could just decide randomly at the start of the game who wins and play deliberately suboptimal moves to keep the game balanced and achieve a loss when required.

If we take the impact of computer chess onto human chess play as template, the future might be something like:

• Massive cheating in online go. On the popular chess sites they have difficulties keeping the percentage of cheaters below 10%, this despite massive amounts of banned accounts. This even if there are some refined statistical methods for detecting cheaters.
• Pros might withdraw from playing online to avoid playing against some punk who cheats just for the lulz of having crushed Sai with 20.5 points. Google youtube for "How I checkmated the world champion" for videos by teenagers who let their computer beat Magnus Carlsen in online games.
• Tournament go (esp. in very competitive Asian cultures) will also get infested with cheaters. This is a problem in chess, and there are no real remedies known yet. It will get worse with wearable devices getting smaller and more powerful.
• Computer prepared fuseki/joseki/traps. In chess all game preparation is done with computers today. In go this might lead to lines so complex that would leave even the (currently) most complicated joseki in the dust. An unprepared human could lose games straight of the bat, and memorization of lines, which already is a bigger part of the game than in chess, would become even more important.

Yes, I take a quite pessimistic view on the future of go. Guess we still have some 10 years before we have AG+ performance on wearable devices.

You think opening memorization is more important in Go than Chess? I, and I think most others (including some friends who used to be competitive Chess players) take the opposite view. Whilst there are some whole-board Go openings they are easy to avoid (e.g. white approaches on move 4, no Chinese/mini-Chinese/Kobayashi) with many chances to go reasonably off-piste. Yes there are lots of joseki, but the most fiendish lines requires both players' willing participation, and they are only 1 of 4 corners so less impact on the game as a whole than a Chess opening.

Why do we even care about computer programs' being stronger players than any human being? I only play go for enjoyment and improving my own understanding. Why does everything have to be oriented towards competition? I pretty much only play in person, face-to-face, not on line. It is a social activity and it is very enjoyable. One can enjoy watching the AlphaGo games (and those of other top engines' games) just as one can learn something from studying solutions of super-difficult tsumego problems and be amazed. A game between two people is a kind of conversation (hand talk). That person to person conversation via the game is absent from engine vs. engine or engine vs. person games. So I think the real success of Alphago and other top engines is the big step toward other problems such as natural language use and translation, medical diagnosis, automation of industries, etc.

I agree with Uberdude. Go is less tactical than chess and memorization plays a small part in go. Sure, there are traps, which serious players study. There are complicated joseki, but in general you don't want to overstay your welcome in one small region of the board. There were some difficult and complicated joseki over 1500 years ago that nobody remembers now. They were abandoned, not because of some tactical refutation, but because they were strategically inferior. Today there are whole board openings that are in vogue, but you don't have to play them. It is not unusual, if you don't, to be in new territory by move 8 (move 4 in chess terms).

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The Adkins Principle:
At some point, doesn't thinking have to go on?
— Winona Adkins

Visualize whirled peas.

Everything with love. Stay safe.

Didn't Jesse Owens race a horse once?

_________________
The Adkins Principle:
At some point, doesn't thinking have to go on?
— Winona Adkins

Visualize whirled peas.

Everything with love. Stay safe.

I personally think there is at least as much memorization in Go as in chess. I include not just joseki but also life and death statuses and sequences, standard middlegame sequences, endgame tesujis and values, etc. (I realize that the specific conversation here is just about openings, though).

Chess opening memorization can thankfully be limited by the fact that it is whole-board, so, for example, I don't need to know anything about playing 1 d4 openings as White, or Sicilians as Black, and in any given opening I can specialize in a specific line. In Go, on the other hand, I have to be prepared for a large variety of josekis, and I can't (or at least shouldn't) even just specialize in one variation, because it may be bad in a whole-board context.

That's a trick, but one not unrelated to the automobile vs human.

Horse vs human -- humans can turn much sharper than horses can. They also can accelerate faster. And they have good endurance. The first is how it is usually done (closely spaced pylons). The second is how I suppose the Jesse Owens demonstration went (race short enough). The third is how our remote ancestors caught horses (chased/tracked them on foot till the horses were exhausted)

@Uberdude, (but also @Bill, @dfan),


No. The amount of fuseki I think a go player has to memorize is nowhere close to the amount of chess opening theory serious players have to know.

Let me ellaborate what I ment while risking to go off-topic for this thread. In go there are so many other sequences to learn (dfan got what I had in mind):

• Joseki - not only corner, but edge and enclosure invasion sequences. For example, just learning the invasion sequences for a 4-3 large knight enclosure is about the same as learning a line of the Queen's Gambit Declined.
• Life and death statuses of different eye configurations. Hundreds of positions, with thousands of sequences on how to achieve ko/seki/kill/life for them.
• Middle game invasion sequences, reduction sequences etc. Which ones work depending on what outside stones who has.

Yes there are memorization in chess too. Openings notably, but also endgame techniques (Philidor position, Lucena, Trebuchets) etc. But they are more "ideas" to remember rather than exact move sequences to play.
Unlike in go, in chess there is seldom only one correct move. And this is where I fear computers will step in - to increase the amount of Joseki players have to memorize, having complicated positions with one right move or disaster happens.

Another reason why memorization is more important in tournament go than tournament chess - tournament time controls. To be rated, chess tournaments (even amateur) usually has like 90 minutes per player with a 30 second increment, often more. This allows players to spend some time to figure out how to deal with a situation over the board. Amateur go tournaments (at least here in Taiwan) have 15 minutes per player with one or two 10-20 second buo-yumi periods. There is no time to do much reading - you just have to know what to do.

But I am a (relative) newcomer to go, so maybe my impression is misguided?

I added emphasis to this last sentence in the quote above.

I think all of the examples you list require "ideas" rather than exact sequences in go, too.

• Joseki - Maybe you study a bunch of joseki - they give you an idea of an even result on an empty board. Often, the particular board position influences whether playing a particular joseki is a good idea. Sometimes playing outside of joseki will be a good idea.
• Life and Death - You study life and death, and come to "memorize" certain shapes, but in a real game, you often read out the sequence yourself because of the particular nuances that you'd describing.
• Invasion/reduction sequences - Again, these give you ideas of things you can do - but the strategy, choice of whether to do it, details of moves, or deviations from "standard" are all commonplace.

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I agree there is a lot of knowledge in a strong Go player's brain, but I see knowledge acquired through experience of playing, doing go problems etc as different to memorisation of opening lines, which would be akin to memorising a joseki dictionary. I don't play chess (maybe 20 kyu) so perhaps my impression that this kind of learning is more common/required in chess if one wants to become strong is misguided, but that was the impression I got from a friend (about 2200 elo), and that such learning and the similar games was boring: killing an L group in the same way in a different position on move 150 is not as boring as playing the same 15 chess opening moves yet again.

As for time limits of 15 minutes in Taiwan, here in England 1 hour each is normal, and it's chess where blitz games are more common.

Probably all generalizations are false.

As a better chess player (2000) than go player, I feel that the memorization burdens of go are at least as high as that of chess. But that may be to some extent because I'm more fluent in chess and thus memorization is easier. For example, I find it much easier to recognize, and recall an associated best move in, a chess position, than a go position, even a local one (e.g., a joseki). I find that a chess position has a lot more "texture" (the kinds of pieces still on the board, their relation to each other, what color squares the bishops are on, etc.) and a go position is much more abstract (moving a stone over by one point is rather subtle to detect, even if it has an important effect on the position).

Time limits for both chess and go vary a lot, of course, but even in a relatively fast chess game (say, 40 minutes a side), I feel like I have time a couple of times a game to really settle in for a decent think, and generally don't feel rushed until the end of the game, whereas in go I constantly feel pressure to stay on pace and feel bad about taking 5 minutes to try to calculate something. Of course a lot of this is due just to being more comfortable playing chess.

As far as boringness goes, I will say that one big advantage of go is that every game is interesting. When I memorize pro games, I just pick one at random, and it's always exciting for me. If I picked a random top grandmaster chess game, chances are good that it would be a relatively bloodless draw.

Sorry to drift off the original topic! I'm happy to move to another subforum if desired.

dfan, you're pretty active in the L19 go problem solving competition- do you feel that you're using memorization to identify the best move for your solutions? I want to see if we're on the same page with terminology.

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be immersed

The same is true of go, as Kirby and Uberdude have said.



Where did you get that idea about go?



Go players do not have to memorize joseki. In fact, they are warned against doing so. Learn joseki and lose two stones.

Let me give you a couple of -- I hope!-- enheartening experiences of my own. Once upon a time I took 3 stones from the U. S. Champion. Play started in the open corner, and he chose a complicated joseki, one that lasted well over 40 moves, and one that I had never played. As it turned out, a couple of years earlier I had studied that joseki for maybe 15 minutes. There was one tesuji that I had to learn. I suppose that he was expecting me to miss that tesuji. Anyway, it was an easy game for me because we had come out even in a big fight. But I did not memorize anything, I just learned an idea.

When I was a 2 kyu in Japan I took 4 stones from a 4 dan. In the first corner we reached a point where I knew what the book move was, but another move looked good, and I tried it out. Disaster! Fast forward 7 years, which I had spent in the U. S., and only lived where I could play go for a little more than 2 1/2 years. Back in Japan for the summer, as a weak 3 dan I am taking 4 stones from a pro (a teaching game, OC). In the first corner we reach the same position. I still want to play the same move. This time I see a tesuji for the next play after that one. So I make the same play with the new follow-up. The fighting gets complicated, but I come out OK, I think. (In fact, I am rather pleased with myself. ) During the review I expect the pro to say something about my new move, but he doesn't. A couple of weeks later I see the whole sequence that we played in a go magazine. While I was away it had become joseki.

Now being a weak three dan is no great shakes. And even now I can hardly claim to find correct play or nearly correct play most of the time. But memorize joseki? Memorize life and death? Why bother?

_________________
The Adkins Principle:
At some point, doesn't thinking have to go on?
— Winona Adkins

Visualize whirled peas.

Everything with love. Stay safe.