I love the idea of tea ceremony before a lesson. I'd hate for that culture to be lost entirely.
Ironically, the AI-Explainability problem is, at least for now, an asset. The model can't explain its strategy, only that it judges some positions stronger than others based on a huge amount of experience.
That reduction of aggregate experience to comprehensible language is still (mostly) in the realm of humans and so should provide a bridge for talented, insightful teachers to teach the next generation.
Top chess engines are atleast 700-800 points above top human. Engines have been absolutely unbeatable for humans for 10 years. Still I don't see the culture lost. In fact, it is easier for anyone to see mistakes after every match in less than a minute and you don't need a coach to point it for you.
Seeing mistakes after the fact is one thing. Developing the thought process you need to see the mistakes during the game is a whole different thing, and that's where a coach helps. Also a computer won't tell you its plan and why it chose it.
I wonder if there's actually any kind of line between "inventing explanations" and actually theorising and studying the game's mechanics.
Obviously it's mostly pattern recognition, but that pattern recognition is partitioning parts of the board into classifications of what those sections are doing and whether to prioritise them or let it go in favour of something else, the immediate decisions within those sections are fairly logical. The more complex broader pattern recognition is how those sections will interact as they approach each other, which if there is that much inventing explanations where I imagine it comes in.
I think this might hit on the key as to why demand for teaching is dropping even though AIs don't really replace good teaching:
> Lee seems to combine these two with the silent assumption that the professionals’ goal is to strive for the ‘highest possible level of go’, which is no longer possible because the ai cannot be beat; incidentally, Lee Sedol remarked something similar when he announced his retirement. To me, on the other hand, it seems like nothing has changed because I have always reached for the ‘highest possible personal level of go’, and this should be the same for most players who are not near the top of the world.
Perhaps it has less to do with being at "the top of the world" and more to do with the role of the game in the broader culture. If what attracts you is not just your personal drive to improve, but the sense of overall societal contribution, that will surely wane if training goes towards study of AI rather than active research in play. On the other hand in the US/Europe such a thing hardly exists at all even for professional players.
Maybe a comparison point: What happened to voice and instrument lessons once recorded music became commonplace? In the fairly recent past any social group that wanted to listen to decent music must have had a member who was at least a slightly-above-average musician; and once they didn't need that anymore, that skill may have significantly dropped in social standing. That doesn't mean no one takes lessons anymore, but it seems like the number would have greatly reduced. (On the other hand, the median interest level and drive among people taking lessons probably increased.)
(Does that mean the go world is going to have to find its equivalent of Kurt Cobain to get lessons started again?)
> Could you explain the Kurt Cobain statement? Not being snarky, just unfamiliar with what you're referencing.
I believe they are saying that Cobain's work (and the popularity of Grunge generally) brought new life to the Rock music scene, leading to an uptick in garage bands forming (and therefore demand for music lessons?) as a second order effect.
The article is an interesting discussion by a Go insider in Korea.
Regarding the impact on pro-level teaching:
“The demand for pro-level teaching games and private lessons has plummeted. Professional players used to command a high price for teaching games and lessons, and this has been a critical source of income for many pros.”
I think the community would also be interested in articles which discuss the impact of AI on professionals in other fields, obviously chess, but also more distant ones such as e-sports and language translation.
——
On language, I would guess that the demand for non-certified translation might plummet between many major languages and English since automatic translation of documents is now quite close to human level when there is sufficient training data.
Down the line, might this reduce the demand for advanced foreign language skills and thus change the career paths of language majors as well?
I think you can. Especially when you can browse the tree which is similar to asking your teacher "what if" questions.
Many players I know learned how to play pretty well on 9x9 by playing a computer program.
I'd argue you can even learn much faster playing a program, because for high level players many sequences were based purely on memorization (including point values) but now seeing different results quickly you can understand it better.
And the level where your play is based on high level abstractions like influence and group strength is not that hard to reach and it wouldn't take much to reason them out just from the games.
People I know that went studying go to Asia had output from their teachers likely worse than you get from the program given the language barrier and general approach. It was about playing games with strong players (playing different styles, which I admit may teach a bit more than Alpha) and having teacher to point you which of your moves were bad and what to do instead (and that's it, only seeing some sequences and hearing "good", "bad"). Because knowing how and when you lost the game is huge. And doing shitloads of go problems of course.
That said, it still may be worth having a teacher/trainer I guess. To motivate you, give you that dopamine in person and all that jazz.
I would say it's more like learning to code yourself rather than at the university than learning assembly from gcc.
The article addresses this. Unfortunately the "teachers" were being paid mostly just to be strong opponents for the "students". So AI ruins their market.
From the article: "the Go school recruits a professional player, who would agree to play a fixed number of games, like five games, one per week, at a certain price.... Of course there is still room for lower level classes and teaching, but pros are often better at playing teaching games than explaining easy concepts."
I don't think this is a good comparison because there's so much about language that's subjective or dependent on context. if you could translate something with perfect denotation, you still need a human touch for connotation. the nuances of word choice and sentence structure that give something a flavor of emotionality, or cool logic, or confidence, or aloofness, etc. to say nothing of localization, how to handle meter, all the various tradeoffs a translator has to make because translation is inherently lossy. games don't have any of these problems, you either win or you don't
I think ai will prove "good enough" for most applications in the near future, but this won't eliminate human translation (or journalism, or copywriting, or whatever you might want to apply similar technology/techniques to). but it may well eliminate the bottom 80%
If you haven't seen it, the documentary on DeepMind's AlphaGo is very well done. The climax revolves around the AI expressing what we humans call "creativity" in a match against decorated player Lee Sedol. Watching Lee Sedol's reaction is deeply moving.
This comment, that AlphaGo showed "creativity", seems so ridiculous to me. Alphago is operating purely on bayesian statistics (with very good priors) so it's decisions are exactly the opposite of creativity.
It's an invitation to define (or re-define) creativity, my friend :)
If it seems ridiculous to you, that's a good signal that you'd get a lot out of digging into philosophy of mind.
Where do our thoughts come from? Creativity? Is it so preposterous that these too arise from probabilistic electrical storms - but in wet matter as opposed to hard?
Where thoughts come from may be too complex for us to model now, maybe ever. But if you look deep enough, I think you'll find at heart deterministic processes driven by networks that were trained through nature + nurture.
Maybe creativity is less ethereal than we perceive. Maybe it would benefit us to start considering how non-sentient intelligence can achieve it.
I am not making, and do not explicitly agree with, the statement that only human brains are imbued with 'creativity'.
However, the core of the alphaGo algorithm is just a monte-carlo tree search of the game initially populated with prior estimates of the value of each game state(with the generation of the prior value estimates being carried out by the DNN). I see no space in this simplistic statistical game analysis for concepts such as creativity. The move in question was chosen because it's prior value estimate by alphaGo was favorable and upon further analysis was concluded to be the most likely to result in a game victory relative to all other available moves. If alphaGo was only trained on human matches and subsequently was able to discover moves that drastically deviate from typical human strategies, I would be more open to labeling it as creative. However, most of alphaGo's training is carried out against itself, it's entirely possible that alphaGo has made this particular(or similar) move many times against itself during training.
Much of human creativity is likely linked to some sort of Bayesian analysis, but it also can happen based on limited sample sets and unseen situations. As you point out, the Alpha Go likely just saw that scenario out of millions of random scenarios never explored by humans before. Given the enormous compute resources used, it's possible AlphaGo has made more Go moves than all of humanity ever combined.
Reminds me of a quip about Richard Feynman that he'd spend many hours honing a new way of doing a physics problem, but carefully present it as if he just did it off handed. So we just need to link human brains with cybernetic implants to compete. ;)
This criticism reminds me a bit of one of my least favorite interview questions I've had: what was your most creative solution to a problem? I found it awkward because creativity tends to be what we recognize in others rather than ourselves. If I came up with a solution I probably wouldn't consider it that creative.
You can think of probabilistic tree search as creativity and the neural network as intuition. Like, what are you doing when you think of a cool new idea? You think of a lot of ideas and narrow them down slowly until you reach a cool new one. It’s not magic.
We have literally no idea what thoughts are or how they are implemented in human brains. This is such a silly argument at this point. For 50 years people have been saying "obviously humans are just doing a probabilistic search" or some other such evidence-free assertion, claiming "it's not magic", an obvious strawman since no one is claiming it is. And for 50 years we have not created any system capable of creating new explanatory knowledge. Yet still the same claims are made, week after week.
I’m not saying humans are doing probabilistic search (not saying they are not either). I’m saying that if probabilistic search produces a result that people see as creative, then we can regard probabilistic search as something that produces creativity.
Furthermore, just because we don’t have the computational capability yet to replicate human thought processes using, for example probabilistic search, does not mean that probabilistic search is not part of replicating human thought processes.
IMHO creativity isn't really as conscious as people seem to think. I have had a handful of hyper productive songwriting sessions and by the end it always felt more like I was the horse than the rider. Neither was anything made so much as it was found.[1]
There are constraints and limits and rules: rhyme scheme, meter, key, chord progression, genre, structure, and whether or not those things will support the consistency and coherence of the content... that's barely scratching the surface. The "choices" and the "thinking" are highly constrained. Few things are going to work. It's often either "this way" or "wo, back it up".
Even in something like Jazz there are still intricate rules at play and the true greats were eccentrics and/or taking loads of drugs in order to push the boundaries where they could.
[1] And then that one time where I finally cracked the melody at 3am, only to realize in the morning that it was Neil Young's "Heart of Gold". Even my creation was not my creation!
Chess went through a similar transition in the last 10-20 years when computers became much stronger than the best human. Some of her points are valid, some of them are temporary.
1. Professional gameplay changes a lot.
There are no more secret openings/midgames/endgames, you will hardly be able to cheese a top pro (not that it was easy before).
Professional gameplay requires memorizing tons of lines and then adding on to add it. There are many chess openings which require memorization till the end result (50 moves) or you risk losing the game. We are going to see a similar trend of memorization in Go.
2. Online casual gameplay changes a lot.
There are lot of cheaters in online forums. Cheat detection has to be built in to the sites or you risk being taken over by cheaters.
Easy availability to best moves allows the dedicated player to learn a lot faster than previously where there was a discoverability issue. We see this in the professional chess world where its not uncommon to see 14-16 yr old GMs nowadays.
3. Professional teaching still survives.
Engine can tell you the best move but it doesn't give you a teaching path from beginner to amateur professional to grand master. I am not a master but based on anecdotal evidence people still employ coaches but for much more focussed tasks and more specialized skills (e.g. openings, midgames, endgames).
I disagree about memorization. That may be true for chess, but the go board is too large and the game to long for that to be viable. In the opening, bots will generally recommend several places per move that lose 0 or -0.1 points compared to its preferred move. You would have to memorize thousands of opening just to have a chance of still being in book for the entire opening, and even then your opponent can take your out of your preparations at a cost of less than 1 point.
After he fell out of the pro scene he became way more interested in chess variants than vanilla chess (more specifically his Fischer Random Chess). I can't imagine being at the level he was at, where you needed to hundreds or thousands of hours memorizing and practicing openings... so not surprised he appreciated a game that valued more on-the-fly decision making than heavy preparation.
You don't actually spend 100,000s of hours memorizing openings. You study the basic opening principles and key variants, but for the most part it comes very naturally. You play 100,000s of hours of chess, and EVERY chess game has an opening.
I can't find many good resources, but at least the joseki explorer on OGS has been listing some pretty heavy favoring for particular moves on a given joseki. Couldn't we see specific joseki lines get more entrenched and go further?
AI have certainly been changing which joseki are popular and which aren't, but this isn't the same as increasing the amount of memorization in the game since joseki aren't new.
The AI favored lines are likely to get heavily entrenched. I wouldn't expect them to go further because they generally end when becomes better to play elsewhere. There are a few fighting joseki where this isn't the case, but AI doesn't seem to like them much, although there are a few exceptions.
I think some of the AI are trained by forcing them to play random moves every so often. That might bias them towards larger, more flexible openings where there are large regions of the board that make sense to play in rather than specific move sequences.
But if I were guessing, the AI probably doesn't like fighting joseki because it knows how the fight will end. AIs are much, much better than humans at working out who won a fight.
Go already required memorizing 'joseki' or standard patterns that crop up again and again (particularly in the opening). The usual mantra is 'don't learn joseki, learn _from_ joseki'. But the truth is - like openings in chess - if you don't know them and you play someone who does, you're often at a disadvantage.
Short time controls and live streaming of blitz games by GMs is fun. Maybe this is less “pure” but it’s great entertainment, and doesn’t seem like engine use is a huge problem when the stakes are low (it’s usually pretty obvious to a GM when they start losing to weird moves, they call it out and move on).
Depends. It’s probably easier to get away with it longer at lower levels and some people want to experience that sweet sweet win at whatever level they are in.
They have a huge database of most common moves + chess engine moves they cross reference to, then using math they see if your time to move + accuracy of moves is close to a chess engine, especially if it's not the opening (since being accurate for the first 4-12 moves is trivial). If you play too fast and with too much accuracy relative to your ELO you get banned pretty quick.
re: move speed - it's also often that low rated players using engines take too long on trivial moves - many chess engines will take a set amount of time to look for the best move, even if there's something completely obvious on the board like capturing a queen that you've spent the last three moves trapping.
Afaik they analyze the deviation (in centipawns) of each played move to the optimal move calculated by popular chess engines (like Stockfish, Houdini, Komodo, etc); and aggregate in a sort of weighted Brier score.
If a player is constantly making optimal moves, they're likely cheating (they can analyze top level professional matches for upper skill benchmarks). There is some lower weighting for opening moves (which is where the database part comes in just knowing when a game moves from theoretical 'book' openings into original territory).
Each of the sites have different implementations of this: chess24 for example even partially gives your accuracy of book moves and then differentiates between Blunder / Mistake / Good / Excellent / Best move based on the deviation from optimal; but it doesn't show you how it's aggregated into a single value.
If I do it to make a living, that’s one thing.
If I do it to be “the best,” that’s another thing.
If I do it for the pleasure of learning to do a thing, that’s a third thing.
It’s the same with games, but the emphasis on winning skews the culture towards wanting to be “the best.” But for those following the third path, AI beating humans changes nothing for them, just as synth bass and sequencing changes nothing for me.
What is the incentive to run faster, when a car can drive faster, or build strength when a tractor can lift more?
What is the incentive to do any artistic endeavor, if you are not planning on being one of the world's best pianists, or painters, or singers, or furniture-makers?
Go was probably already solved by some distant species on a planet far away. Did that make it any less enjoyable for us to play? What's the difference?
Slight tangent, but the barrier of entry to play Go is quite high. I've been learning over the past few months and it's very frustrating.
My biggest complaint is that the jargon is just out of control. For whatever reason, instead of localizing go jargon like the chess community has done, the Go community just uses native japanese terms for everything. This makes it super confusing as a beginner and you basically have to have the wiki[0] open all the time if you have any hope of understanding what people are saying.
For example, when reading a beginner's article:
"In this case you can't move here because of ko, so you would instead look for other pieces that are atari or close to atari - just be careful of seki"
Would a bit of localization killed the go community?
"In this case you can't move here because of the repetition rule, so you would instead look for other groups of pieces that can be captured or are close to being captured - just be careful not to get in a capture-stalemate scenario"
My second biggest complaint is that scoring is not straightforward at all and there are 2 rule sets (Chinese, Japanese). Which ruleset should I use? Are there any subtle strategy changes you need to make when switching between rulesets? When playing I have no idea who is currently winning if it looks close (i.e. roughly equal white/black areas) and I basically just wait for the computer to announce the winner at the end.
Anyway, just venting about my frustrations learning Go. Chess, by comparison, was much easier to learn. I didn't feel swamped with jargon and it was much easier for me to grasp which side was winning (though occasionally good chess players can pull a checkmate out of a seeming disadvantaged situation that I didn't see coming!)
The English-speaking Go community is just many times smaller than the English-speaking chess community. Many of the best books for Go are translated versions of foreign ones, there aren’t many popular streamers to learn from, all sort of educational material is not there because the market isn’t there.
Biggest community is in Asia. When you visit tournaments common language for what's happening on the board is really nice to have. If you stick around that's like <0.01% of what you need to learn anyway. Plus you've learned where Atari name came from :)
Japanese counting is more popular but you really don't need to worry about it until some high dan level if you play on 19x19. It takes a long, long time before you will be able to say confidently who is winning on 19x19 if the game is close.
I suck at Chess, but to me Go was much easier to learn. You can have quite a lot of fun without studying any openings.
> For whatever reason, instead of localizing go jargon like the chess community has done, the Go community just uses native japanese terms for everything.
I think we got the better trade. Just my opinion though. Games which are more popular in the far-east will naturally use far-east terminology.
Similarly, things Americans do will get American-terms associated with them (IE: Programming Java). Its the nature of international trade. Even a Japanese-centric language, like Ruby, is largely composed of English terms to continue to appeal to the greater programming community. (Japanese programmers would learn "for" and "while" loops anyway, despite it being foreign terms).
Semaphores are "P" and "V" canonically meaning something in Dutch. Someone tried to explain it to me once, but you pretty much have to learn Dutch for the meaning of it all.
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Anyway, its the nature of working internationally. Gotta learn tidbits of the other people's language to really understand something.
> Semaphores are "P" and "V" canonically meaning something in Dutch.
"P" stands for "Prolaag", which is a word Dijkstra made up. Both Raise (Verhoog) and lower (Verlaag) start with a V in dutch. So knowing dutch doesn't really help with that one.
> instead of localizing go jargon like the chess community has done,
Yah, we never say Zugzwang or J'adoube or Desperado or En Passant or Fianchetto or Zwischenzug.
There's not so many foreign words to learn to understand Go compared to chess. For lots of the fundamental Go words, they'd need a new English word coined anyway or an imperfect metaphor.
> For lots of the fundamental Go words, they'd need a new English word coined anyway or an imperfect metaphor.
This. Even the chess words that are anglicised have special meanings in chess. Being a native speaker of English doesn't help you understand what a "Bishop" is or what is "Checkmate" (OK, that's an anglicised Persian word) or the difference between a "Pin" and a "Skewer".
Also, most of the Japanese terms don't have a meaning outside Go, so it's not like Japanese people get a leg up there.
Think about Japanese rules as a shortcut to the real rules, the Chinese ones. Counting with Japanese rules (at the end and during the game) is much simpler. However Chinese rules allow for a straightforward resolution of all the border cases, starting with the bent four in the corner. Just keep playing under normal rules.
About localization, I started playing in a go club and the people I played with told me the Japanese names and their meaning. I already knew all the words when I started reading books. I understand that if you started reading books and watching videos from day one, that's a problem.
And about learning Go, the very first months are a very dense fog. Somebody never emerge from the fog.
At a high level, all counting schemes are basically streamlined approximations of stone counting. It turns out that codifying all the edge cases is nontrivial and so the simplified scoring schemes come up with subtly "wrong" results at the margin, but this is something of an acceptable sacrifice that doesn't (isn't supposed to) change the essential nature of the game.
The main difference that Japanese rules make on scoring is, essentially, codifying a matter of etiquette directly into the scoring: they aggressively penalize extending the game with unnecessary moves, which to be sure has some distortionary effects, but again, they don't essentially change what people considered the meat of the game; you'd play 90% the same game regardless of counting method, with differences cropping up basically once a player starts passing. This has far more ... pedagogical value outside the game, than the impact it has on the game itself.
By far the most important rules in a ruleset, in terms of how they affect core gameplay, are the repetition rules, and even the choice of suicide rule has a more substantial impact than the differences between scoring systems.
You're getting downvoted but I think this is a fair criticism. There was a recent boom of chess on twitch.tv, and I think a big part of why it took off is that the various GMs and commentators consciously simplified the terminology without sacrificing accuracy in what they were saying. It's not a binary thing either. English Go commentators could simply adopt a dual vocabulary approach where they say things like "watch out for seki or dual life." They do this already on "beginners streams" for some of the bigger events.
> My biggest complaint is that the jargon is just out of control.
I don't see how this is true unless you are memorizing the wiki like a dictionary. I can only think of 9 words that I use commonly in games (sente, gote, seki, ko, atari, hane, aji, tenuki, komi). Sure, I think the English Go community could try (and probably has tried) to reinvent translations for each of these, but they probably wouldn't catch on. "Seki" is easier to say than "mutual life", "sente" is more precise and easier to say than "initiative", "ko" is just ko.
I just don't feel like learning new words is any more difficult than keeping track of algebraic notation in chess, and I think it's just part of the commentating culture of both games.
> Which ruleset should I use?
As a beginner, you shouldn't worry about which one you use. They're almost the same except on some edge cases.
I picked up some of the terms watching Hikaru No Go a japanese anime about a middle school kid who learns to play Go with the help of a ghost. Good clean fun. It's on Crunchyroll and probably other platforms.
This is one of the unfortunately reality of advances in technology specially AI. It started with games like Chess and Go, wait and watch as AI will eat into other creative human endeavors - both logical like accounting, programming and creative like art, music etc. How humans will deal with watching AI get better and better than us in many areas will be interesting to see.
The thing is games are relatively innocuous but then when it works well you'll see ai applied to higher stakes decision: why not trading by example? Is AI trading a thing? Does it provide an advantage yet? And what is the impact on trading when all of it is automated?
Trading is harder because people start with different amounts of capital and different risk tolerance. The more capital you have the more illiquid the market becomes and less predictable.
Capital and risk level seem to me something that AIs would be extremely good at adjusting for -- more so that humans, who will bring irrational thoughts into it.
I'm told that in the 70s, when backgammon players had a dispute about the correctness of a move (usually a double), they would agree to continually setup the board from that position and play game after game, until somebody capitulates (or runs out of money).
Nowadays we just make a side-bet about what XG mobile will think about the move.
Lol. This a pointless comment to the people interested, but I'm so amused with my stupidity I had to share.
I opened this thread with absolutely no thought about the _game_ Go, and read the top comment about how chess changed in the last 10-20 years thinking it was tangentially related because of Watson and whatnot playing AI chess.
I came here thinking I was going to read comments about how the Go language has been impacted with the introduction of ML and AI packages that attract data science types from Python and R. I thought it would be interesting to see how a newer/smaller language was impacted by a group co-opting the the development and platform.
I have the opposite experience on this site. I am not a software engineer, so every time I see the word "go" in a submission title, my first thought is of the game.
I remember the first time something like this happened: Drum machines. All my drummer friends were seriously in their cups. OTOH, they complained that the machines were too perfect, without the human touch. OTOH, they complained that it was impossible to play that perfectly, so they were out of a job.
In the end, there were still drummers, and it is generally recognized that live drumming is, aesthetically speaking, a different thing than sequenced, sampled drum tracks.
I've never heard a drum machine that can truly capture the sound and feel of John Bonham, Keith Moon, Stuart Copeland, or Ringo Starr. It's just a different thing.
>I've never heard a drum machine that can truly capture the sound and feel of John Bonham, Keith Moon, Stuart Copeland, or Ringo Starr. It's just a different thing
The only way to establish that would be through blind listening tests.
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[ 2.1 ms ] story [ 169 ms ] threadIronically, the AI-Explainability problem is, at least for now, an asset. The model can't explain its strategy, only that it judges some positions stronger than others based on a huge amount of experience.
That reduction of aggregate experience to comprehensible language is still (mostly) in the realm of humans and so should provide a bridge for talented, insightful teachers to teach the next generation.
For strategic/positional issues, it is not nearly as good a teacher.
It's actually the same as Douglas Adams'. Now we know the answer is 42 and now we need to solve what the question really was :)
I think it's ironic how he foresaw the problem we are experiencing with AI right now.
Obviously it's mostly pattern recognition, but that pattern recognition is partitioning parts of the board into classifications of what those sections are doing and whether to prioritise them or let it go in favour of something else, the immediate decisions within those sections are fairly logical. The more complex broader pattern recognition is how those sections will interact as they approach each other, which if there is that much inventing explanations where I imagine it comes in.
I am a double digit kyu player, but I enjoyed clicking through the most recent game with it: http://www.yss-aya.com/cgos/viewer.cgi?19x19/SGF/2020/08/21/...
The battle in the upper left was fascinating!
> Lee seems to combine these two with the silent assumption that the professionals’ goal is to strive for the ‘highest possible level of go’, which is no longer possible because the ai cannot be beat; incidentally, Lee Sedol remarked something similar when he announced his retirement. To me, on the other hand, it seems like nothing has changed because I have always reached for the ‘highest possible personal level of go’, and this should be the same for most players who are not near the top of the world.
Perhaps it has less to do with being at "the top of the world" and more to do with the role of the game in the broader culture. If what attracts you is not just your personal drive to improve, but the sense of overall societal contribution, that will surely wane if training goes towards study of AI rather than active research in play. On the other hand in the US/Europe such a thing hardly exists at all even for professional players.
Maybe a comparison point: What happened to voice and instrument lessons once recorded music became commonplace? In the fairly recent past any social group that wanted to listen to decent music must have had a member who was at least a slightly-above-average musician; and once they didn't need that anymore, that skill may have significantly dropped in social standing. That doesn't mean no one takes lessons anymore, but it seems like the number would have greatly reduced. (On the other hand, the median interest level and drive among people taking lessons probably increased.)
(Does that mean the go world is going to have to find its equivalent of Kurt Cobain to get lessons started again?)
I believe they are saying that Cobain's work (and the popularity of Grunge generally) brought new life to the Rock music scene, leading to an uptick in garage bands forming (and therefore demand for music lessons?) as a second order effect.
Regarding the impact on pro-level teaching:
“The demand for pro-level teaching games and private lessons has plummeted. Professional players used to command a high price for teaching games and lessons, and this has been a critical source of income for many pros.”
I think the community would also be interested in articles which discuss the impact of AI on professionals in other fields, obviously chess, but also more distant ones such as e-sports and language translation.
——
On language, I would guess that the demand for non-certified translation might plummet between many major languages and English since automatic translation of documents is now quite close to human level when there is sufficient training data.
Down the line, might this reduce the demand for advanced foreign language skills and thus change the career paths of language majors as well?
Many players I know learned how to play pretty well on 9x9 by playing a computer program.
I'd argue you can even learn much faster playing a program, because for high level players many sequences were based purely on memorization (including point values) but now seeing different results quickly you can understand it better.
And the level where your play is based on high level abstractions like influence and group strength is not that hard to reach and it wouldn't take much to reason them out just from the games.
That said, it still may be worth having a teacher/trainer I guess. To motivate you, give you that dopamine in person and all that jazz.
I would say it's more like learning to code yourself rather than at the university than learning assembly from gcc.
From the article: "the Go school recruits a professional player, who would agree to play a fixed number of games, like five games, one per week, at a certain price.... Of course there is still room for lower level classes and teaching, but pros are often better at playing teaching games than explaining easy concepts."
I think ai will prove "good enough" for most applications in the near future, but this won't eliminate human translation (or journalism, or copywriting, or whatever you might want to apply similar technology/techniques to). but it may well eliminate the bottom 80%
Free to watch on YouTube: https://www.youtube.com/watch?v=WXuK6gekU1Y
If it seems ridiculous to you, that's a good signal that you'd get a lot out of digging into philosophy of mind.
Where do our thoughts come from? Creativity? Is it so preposterous that these too arise from probabilistic electrical storms - but in wet matter as opposed to hard?
Where thoughts come from may be too complex for us to model now, maybe ever. But if you look deep enough, I think you'll find at heart deterministic processes driven by networks that were trained through nature + nurture.
Maybe creativity is less ethereal than we perceive. Maybe it would benefit us to start considering how non-sentient intelligence can achieve it.
Reminds me of a quip about Richard Feynman that he'd spend many hours honing a new way of doing a physics problem, but carefully present it as if he just did it off handed. So we just need to link human brains with cybernetic implants to compete. ;)
Of course I still made something up.
Furthermore, just because we don’t have the computational capability yet to replicate human thought processes using, for example probabilistic search, does not mean that probabilistic search is not part of replicating human thought processes.
There are constraints and limits and rules: rhyme scheme, meter, key, chord progression, genre, structure, and whether or not those things will support the consistency and coherence of the content... that's barely scratching the surface. The "choices" and the "thinking" are highly constrained. Few things are going to work. It's often either "this way" or "wo, back it up".
Even in something like Jazz there are still intricate rules at play and the true greats were eccentrics and/or taking loads of drugs in order to push the boundaries where they could.
[1] And then that one time where I finally cracked the melody at 3am, only to realize in the morning that it was Neil Young's "Heart of Gold". Even my creation was not my creation!
1. Professional gameplay changes a lot.
There are no more secret openings/midgames/endgames, you will hardly be able to cheese a top pro (not that it was easy before). Professional gameplay requires memorizing tons of lines and then adding on to add it. There are many chess openings which require memorization till the end result (50 moves) or you risk losing the game. We are going to see a similar trend of memorization in Go.
2. Online casual gameplay changes a lot.
There are lot of cheaters in online forums. Cheat detection has to be built in to the sites or you risk being taken over by cheaters. Easy availability to best moves allows the dedicated player to learn a lot faster than previously where there was a discoverability issue. We see this in the professional chess world where its not uncommon to see 14-16 yr old GMs nowadays.
3. Professional teaching still survives.
Engine can tell you the best move but it doesn't give you a teaching path from beginner to amateur professional to grand master. I am not a master but based on anecdotal evidence people still employ coaches but for much more focussed tasks and more specialized skills (e.g. openings, midgames, endgames).
100k hours is 11.4 years straight.
Just sayin’...
The AI favored lines are likely to get heavily entrenched. I wouldn't expect them to go further because they generally end when becomes better to play elsewhere. There are a few fighting joseki where this isn't the case, but AI doesn't seem to like them much, although there are a few exceptions.
Do we know if those moves are actually weaker overall, or is there some other sort of selection bias at play?
But if I were guessing, the AI probably doesn't like fighting joseki because it knows how the fight will end. AIs are much, much better than humans at working out who won a fight.
Short time controls and live streaming of blitz games by GMs is fun. Maybe this is less “pure” but it’s great entertainment, and doesn’t seem like engine use is a huge problem when the stakes are low (it’s usually pretty obvious to a GM when they start losing to weird moves, they call it out and move on).
As a mid level chump at chess.com, any cheater will be ranked way over me.
It's of course possible that some people only cheats occasionally, for whatever reason. People are weird and do weird things...
Afaik they analyze the deviation (in centipawns) of each played move to the optimal move calculated by popular chess engines (like Stockfish, Houdini, Komodo, etc); and aggregate in a sort of weighted Brier score.
If a player is constantly making optimal moves, they're likely cheating (they can analyze top level professional matches for upper skill benchmarks). There is some lower weighting for opening moves (which is where the database part comes in just knowing when a game moves from theoretical 'book' openings into original territory).
Each of the sites have different implementations of this: chess24 for example even partially gives your accuracy of book moves and then differentiates between Blunder / Mistake / Good / Excellent / Best move based on the deviation from optimal; but it doesn't show you how it's aggregated into a single value.
If I do it to make a living, that’s one thing. If I do it to be “the best,” that’s another thing. If I do it for the pleasure of learning to do a thing, that’s a third thing.
It’s the same with games, but the emphasis on winning skews the culture towards wanting to be “the best.” But for those following the third path, AI beating humans changes nothing for them, just as synth bass and sequencing changes nothing for me.
What is the incentive to do any artistic endeavor, if you are not planning on being one of the world's best pianists, or painters, or singers, or furniture-makers?
Go was probably already solved by some distant species on a planet far away. Did that make it any less enjoyable for us to play? What's the difference?
If suddenly computers can create perfect, beautiful music, I will still play an instrument.
I didn't downvote you because I guess it's a fair question to ask, but it is a rather naive one. Why learn arithmetic if a computer can do it?
https://brantondemoss.com/writing/kata/
My biggest complaint is that the jargon is just out of control. For whatever reason, instead of localizing go jargon like the chess community has done, the Go community just uses native japanese terms for everything. This makes it super confusing as a beginner and you basically have to have the wiki[0] open all the time if you have any hope of understanding what people are saying.
For example, when reading a beginner's article:
"In this case you can't move here because of ko, so you would instead look for other pieces that are atari or close to atari - just be careful of seki"
Would a bit of localization killed the go community?
"In this case you can't move here because of the repetition rule, so you would instead look for other groups of pieces that can be captured or are close to being captured - just be careful not to get in a capture-stalemate scenario"
My second biggest complaint is that scoring is not straightforward at all and there are 2 rule sets (Chinese, Japanese). Which ruleset should I use? Are there any subtle strategy changes you need to make when switching between rulesets? When playing I have no idea who is currently winning if it looks close (i.e. roughly equal white/black areas) and I basically just wait for the computer to announce the winner at the end.
Anyway, just venting about my frustrations learning Go. Chess, by comparison, was much easier to learn. I didn't feel swamped with jargon and it was much easier for me to grasp which side was winning (though occasionally good chess players can pull a checkmate out of a seeming disadvantaged situation that I didn't see coming!)
[0] https://en.wikipedia.org/wiki/List_of_Go_terms
Japanese counting is more popular but you really don't need to worry about it until some high dan level if you play on 19x19. It takes a long, long time before you will be able to say confidently who is winning on 19x19 if the game is close.
I suck at Chess, but to me Go was much easier to learn. You can have quite a lot of fun without studying any openings.
Uhhh... give this Japanese page a looksie: https://docs.oracle.com/javase/jp/8/docs/api/index.html
I think we got the better trade. Just my opinion though. Games which are more popular in the far-east will naturally use far-east terminology.
Similarly, things Americans do will get American-terms associated with them (IE: Programming Java). Its the nature of international trade. Even a Japanese-centric language, like Ruby, is largely composed of English terms to continue to appeal to the greater programming community. (Japanese programmers would learn "for" and "while" loops anyway, despite it being foreign terms).
Semaphores are "P" and "V" canonically meaning something in Dutch. Someone tried to explain it to me once, but you pretty much have to learn Dutch for the meaning of it all.
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Anyway, its the nature of working internationally. Gotta learn tidbits of the other people's language to really understand something.
"P" stands for "Prolaag", which is a word Dijkstra made up. Both Raise (Verhoog) and lower (Verlaag) start with a V in dutch. So knowing dutch doesn't really help with that one.
Yah, we never say Zugzwang or J'adoube or Desperado or En Passant or Fianchetto or Zwischenzug.
There's not so many foreign words to learn to understand Go compared to chess. For lots of the fundamental Go words, they'd need a new English word coined anyway or an imperfect metaphor.
This. Even the chess words that are anglicised have special meanings in chess. Being a native speaker of English doesn't help you understand what a "Bishop" is or what is "Checkmate" (OK, that's an anglicised Persian word) or the difference between a "Pin" and a "Skewer".
Also, most of the Japanese terms don't have a meaning outside Go, so it's not like Japanese people get a leg up there.
About localization, I started playing in a go club and the people I played with told me the Japanese names and their meaning. I already knew all the words when I started reading books. I understand that if you started reading books and watching videos from day one, that's a problem.
And about learning Go, the very first months are a very dense fog. Somebody never emerge from the fog.
The main difference that Japanese rules make on scoring is, essentially, codifying a matter of etiquette directly into the scoring: they aggressively penalize extending the game with unnecessary moves, which to be sure has some distortionary effects, but again, they don't essentially change what people considered the meat of the game; you'd play 90% the same game regardless of counting method, with differences cropping up basically once a player starts passing. This has far more ... pedagogical value outside the game, than the impact it has on the game itself.
By far the most important rules in a ruleset, in terms of how they affect core gameplay, are the repetition rules, and even the choice of suicide rule has a more substantial impact than the differences between scoring systems.
I don't see how this is true unless you are memorizing the wiki like a dictionary. I can only think of 9 words that I use commonly in games (sente, gote, seki, ko, atari, hane, aji, tenuki, komi). Sure, I think the English Go community could try (and probably has tried) to reinvent translations for each of these, but they probably wouldn't catch on. "Seki" is easier to say than "mutual life", "sente" is more precise and easier to say than "initiative", "ko" is just ko.
I just don't feel like learning new words is any more difficult than keeping track of algebraic notation in chess, and I think it's just part of the commentating culture of both games.
> Which ruleset should I use?
As a beginner, you shouldn't worry about which one you use. They're almost the same except on some edge cases.
Nowadays we just make a side-bet about what XG mobile will think about the move.
I opened this thread with absolutely no thought about the _game_ Go, and read the top comment about how chess changed in the last 10-20 years thinking it was tangentially related because of Watson and whatnot playing AI chess.
I came here thinking I was going to read comments about how the Go language has been impacted with the introduction of ML and AI packages that attract data science types from Python and R. I thought it would be interesting to see how a newer/smaller language was impacted by a group co-opting the the development and platform.
Whoops.
In the end, there were still drummers, and it is generally recognized that live drumming is, aesthetically speaking, a different thing than sequenced, sampled drum tracks.
I've never heard a drum machine that can truly capture the sound and feel of John Bonham, Keith Moon, Stuart Copeland, or Ringo Starr. It's just a different thing.
The only way to establish that would be through blind listening tests.