On the other hand, chess is more popular than ever. It's huge in high schools. I see people playing it everywhere. I know that for me, I love being able to play a game and then view the computer analysis afterwards and see exactly what I did wrong (granted, sometimes a move can be good for a computer who will know how to follow through on the next 10 moves, but not necessarily good for me... but most of the time I can see where I made a mistake when the computer points it out).

Side note: I play on LIChess and it's great. Is there an equivalent app for Go?

Compare this to regular high level chess in the Grand Chess Tour: It's where most of your money is going to come from if you are a top player. Invitation to the tour as a regular is by rating, and there's enough money at the bottom of the tour than the difference between qualifying or not is massive. Therefore, the most important thing is to stay in the tour train. Lose 20 points of rating, and barring Rex Sinquefield deciding to sponsor your life out of the goodness of his heart, you might as well spend time coaching, because there are so few tournaments where there's a lot of money.

This also shows in the big difficulties for youngsters that reach 2650 or so: They are only going to find good enough opponents to move up quickly in a handful of events a year where people with higher rating end up risking their rating against them. See how something like the US championship is a big risk for the top US professionals, because all the young players that show are at least 50 points underrated, if not more.

This is what causes draws, not computer prep. Anand was better at just drawing every game in every tournament back when he was still on the tour, and yet computers were far worse than today, especially with opening theory.

So maybe in chess they could give points for each win, less than half of those points for a draw, zero for a loss.

Tradition is very important so they should keep the ELO and keep updating it according to who wins against whom, but qualifications to tournaments and seeding (if that's a thing in chess) would be based on the other score. There could be wild cards to let some strong or popular players play even if they don't have a good score. Tennis pro associations have provisions in place for players that are forced to miss tournaments because of injuries, etc.

I think that would increase spectator interest even more. In football, relegation battles can be almost as compelling as the title race..

The article linked at the bottom of the source is a WSJ piece about how Magnus beats the best players because of the "human element".

A lot about the games today are about opening preparation, where the goal is to out-prepare and surprise your opponent by studying opening lines and esoteric responses (somewhere computer play has drastically opened up new fields). Similarly, during the middle/end-games, the best players will try to force uncomfortable decisions on their opponents, knowing what positions their opponents tend to not prefer. For example, in the candidates game round 1, Fabiano took Hikari into a position that had very little in the way of aggressive counter-play, effectively taking away a big advantage that Hikaru would otherwise have had.

Watching these games feels somewhat akin to watching generals develop strategies trying to out maneuver their counterparts on the other side, taking into consideration their strengths and weaknesses as much as the tactics/deployment of troops/etc.

It makes the risk of going for a risky strategy lower (you only drop 0.2 pts instead of 0.5 vs getting an easy draw) and it makes the rewards much greater... a single win and 4 losses scores the same as 5 draws.

you wont see players doing intentional draws anymore either

Some chess tournaments have experimented with giving 1/3 point for draws instead of 1/2 and it didn't really change much. Mostly it acted as a tiebreaker, which you could have done by just using "most wins" as a tiebreaker anyway.

My favorite idea (not mine) for creating decisive results in chess is that when a draw is agreed, you switch sides and start a new game, but don't reset the clocks.

Might be an interesting variant of chess where 2 players just decide how much of the point they get each via negotiation, and if they disagree, they go to "court" by playing the chess game.

Before that is was 2 points for a win, 1 point each for a draw.

In 1981 they made it 3 points for a win, and the sport has had substantially more offensive play since.

Needless to say, no one likes this idea because it throws out of the window centuries of game theory. Endgames would be completely different. I'm not convinced it would be a less interesting game, though.

Some positions simply do not allow for a win. Yes, you could say do it on time. But then it becomes about mechanical dexterity as people try to be faster than their opponent in a pointless piece shuffle.

There are no "on-board" rule changes you can make which won't destroy the game of chess. Any rule-changes have to be "meta" changes affecting points in a tournament, the ELO system, or ways to encourage players to play a wider variety of opponents. That's why everyone's talking about what it might look like to modify the points system in tournaments, because it's the most practical thing to actually change.

For example a King vs King endgame. Even really weak players will never accidentally put their king next to the opponent.

Getting rid of check does make for a better game at beginner levels.

It's both easier to teach and leads to exciting finishes ss noobs hang their king.

It depends on the locations of the kings relative to the pawn ( you generally want it in-front of your own pawn ), and the concept of opposition.

So essentially it's a lost position for one player, but due to stalemate rule it will count as a draw.

The idea that the large amount of draws is because players are so strong now, is mostly a myth. It's really just psychology and game theory at work.

For a perfect illustration of all my points, look at Aronian vs Grischuk from the 2018 candidates tournament. Here both players chose to play into complications, and the resulting game was wildly complex, with both players making several suboptimal moves simply because the position was just too complex even for two of the strongest calculators in the game at the time.

And in the end, they still ended up constructing a draw by repetition when all 3 results were still possible. Both players had good winning chances, yet the fear of losing finally overtook them and they collectively bailed out of the game.

It's not that players are now so strong it's almost impossible to win, the players just aren't as willing to seek out the necessary positions.

AlphaGo isn't available for anyone to train against like Stockfish is though, what are Go players using? Has another powerful Go engine been developed since then?

KaTrain is a good frontend.

Not really important to your point, but it's not really just that it uses more game knowledge. Mostly it's that a small but dedicated community (especially lightvector) worked hard to build on what AlphaGo and LeelaZero did.

Lightvector is a genius and put a lot of effort into KataGo. It wasn't just add some game knowledge and that's it. https://github.com/lightvector/KataGo?tab=readme-ov-file#tra... has a bunch of info if you're interested.

OGS might be more accessible to new players with one click sign in and a better web app, but I think KGS has a higher population of true dan+ strength players, and has a stronger "culture" around community reviews and studying.

It used to be even better, but there are less total people playing on KGS than the previous peak.

I've read about KGS but I've never figured out how to engage with it. (I know it to be the OG, srs bzns venue, though).

Yeah, I feel the same thing about Magic formats when the pros play. When a format is new and people are discovering, and they have to rely on their gut, and make educated guesses. That's when it's fun to play and watch.

I think I improved a lot when I stopped 3-3ing (it opened up different style of game for me)

Noobies love 3-3 (I definitely did), because it’s kind of simple and familiar move. (Especially at the start of the game, when board is empty, there is gadzilion of possibilities and most of them unknown and possibly risky)

If not discouraging 3-3, I would still recommend starting without it, to learn that way of play (if for nothing else, to deal with 3-3 invasions)

There's a big difference between those two and "the hacker ethos" will lead to a lot of the latter. However, now computers can simulate enough games to give a relatively high degree of confidence that a variation in strategy is truly better.

The real challenge with AI helping experts is whether it can correctly help them balance their own value function for what "better" means. And whether we can still train human experts who can think about that independently with good judgement, if we've automated away the things that beginners would normally do to train their judgement with black boxes that they can't interrogate.

Will be interesting for sure.

It does hit its limits, but it's been so useful - it's a funny cycle of training AI and having it train us, a great symbiotic relationship.

The humans are still way worse than the Go programs. People are still willing to pay them to play a game as entertainment. Are lots of people willing to pay you to do whatever it is you do even when AIs do it much better, out of sheer entertainment value & sentimentality? If they are willing to pay you in particular, how many other people like you are they also willing to pay for, and is that number much greater than or much less than the current number paid to do it?

There are not many Usain Bolts out there: just one, as a matter of fact. Who is Usain Bolt #100? Or #1000?

Of course people still do some chores today even on days off, but it's a lot less than you need to do on a farm, ask basically any farmer.

It is still the case, though, that AI dominates humans at Go; humans didn't get so creative about the game that they put AI back on its toes (though some did discover exploitable AI "strategy bugs").

Keel in mind that if you had a variation of Go where there was a "hole" in the middle of the board, both Lee Sedol and a competent amateur would be able to play competent "Doughnut Go" without any prior experience. But AlphaGo and its successors would certainly make a ton of dumb unforced errors unless it practiced at least a few hundred games. (I am basing this observation on similar experiments with a similar Breakout AI, not sure if these experiments have been done with Go.)

Mammals, including humans, have advanced brains because we evolved to solve weird and unexpected problems with moderate reliability, not to optimize well-known benchmarks with high reliability. (This is also why plants are green instead of black.) By contrast, AlphaGo is a machine designed to solve a highly specific problem. The whole point of machines is that they dominate humans at specific tasks, otherwise we would just use a human. But we don't describe bulldozers as "superhuman" unless we're being intentionally obscure; the same should apply to AI. Otherwise we risk assuming the AI is capable of things it probably can't do without retraining.

I am still so impressed that Lee Sedol beat Alpha Go 1 game out of 5 way back when AI made its breakout. I was sad he felt so sheepish afterward for losing. In hindsight, I think it was an amazing accomplishment even if today an AI could beat Shin Jin-Seo (#1 player) 100 out of 100 times!

Yes and no. The biggest takeaway from AI is that learning all the joseki doesn't actually matter that much, which has freed up players (except for the pros) to spend more of their time focusing on the more fun and interesting parts of the game.

There are a lot of videos showing what josekis and strategies the AI recommends, but as a human you're likely not going to be any better off following them. This is for the same reason why AI analysis of fights is largely useless. That is, the reason why you lost the big fight (and the game) isn't that you didn't find that one obscure 9P move that could have saved you, but rather than you let yourself get cut 50 moves earlier. But the AI will never show you the move where you got cut as being the reason why you lost the game, it will only show you the one random move that you'd never in a million years actually be able to find.

This video from Shygost sums up the most important strategy stuff that you actually need to know in order to get strong: https://www.youtube.com/watch?v=ig8cWuDSHTg

When I was playing seriously there were strong players who played a ton over a board and had deep intuition about what made plays good and what made plays bad. In the late 1990s/ early 2000s there started to be a lot more in the way of computer simulation and analysis and some very strong computer players.

One (general) example was that older players liked the idea of making longer plays using more tiles to "win" a race to the S and blank tiles (the best tiles in the bag). Computer simulations generally show that turnover (as this is called) isn't optimal and you're better off holding strong combinations of letters rather than playing them off hoping to draw something better.

Now younger players are better than ever because all of their training came with the help of computer analysis and simulation.

Of course in Scrabble a huge part of it comes down to just memorizing the words in the dictionary.

It's a partial-information game, but the search space can't be as big as Go, and an AI has an advantage over human players in that the entire valid string family can be encoded into a trie or some other efficient data structure, it's never going to forget a word or think it can play one that isn't valid.

My intuition is that AI should be able to crush the best human players at this point in time, but I'm open to being corrected on that if there's some aspect of the game which I'm not modeling correctly.

The reason is that you need to apply some rules, like when to trade vs. making a play, balancing consonants and vowels for future plays, what parts of the board are too dangerous to make certain plays in, etc. It’s because of the distribution of unplayed tiles, the high-scoring spots on the board, and the 50-point bonus for using all of your tiles.

Because of that, generally, you’ll do better by building towards a 50-point bonus play every 3 or 4 turns than by maxing your score on each turn.

I’d be curious about letting a human player play with the assistance of the best bot available and seeing how much better that would make them. I guess part of the issue though is that in a 13 play game maybe 3 plays are meaningfully difficult. So it’d take awhile to see if the human is improving on the bot or not.

I’ve been following Mack Meller’s YT channel and he’s getting beaten pretty handily in his series.

I’d put the over/under on people playing today who would beat BestBot in a 100-game series at say 3.5. What side would you take?

I don't often play Go myself but a number of my friends do. Among non-professional players, it is really common to see game play being not as exciting as before because there's now an easy way: just memorize and copy what the AI does. I don't doubt that professional players still have a ton of creativity, but a lot of non-pros don't really have too much creativity and the whole game becomes memorizing and replicating AI moves.

This is just… not true.

Unless one is playing at high dan ranks, it’s trivially easy to induce a “memorized sequence” that your opponent either will not have memorized or will leave them with a situation that they don’t understand well enough to capitalize on.

The “slack moves” in the openings that pros talk about are often worth 1.5 points or less (often a fraction of a point), and that assumes pro-level follow up.

This pro-level follow up is laughably rare outside of strong amateur dan levels and pro levels (and even within those ranks there are substantial differences).

That makes no sense. After 10-20 moves you are surely in a position that has never been played before. How do you memorize moves after that?

If you are playing someone who is worse at fighting then playing good-enough joseki and making up any theoretical difference in the middle game is a fine strategy. But even choosing good-enough joseki requires thought (or instinct) that does beyond what can reasonably be called memorising. It is critical to recognise when a framework is getting too big and invade before the opportune moment passes.

As thomasahle notes, pretty much every game is unique and a memorised sequence unbacked by an algorithm cannot hope to be optimal.

These can be memorized. But that's almost useless without understanding why these patterns are good. You still need to pick a pattern that works well with the rest of the board, in particular which conditions elsewhere on the board influence it (ladders, ko) and you need to know all the punishments for deviations. That's really difficult and often subtle, and it is where strong players as well as AI easily outplay someone who just mindlessly memorized patterns.

Human acting? Eeh, good enough AI and video could do it for me. Same goes for vlogs. Content tailored for me would trump any existing vlogger.

It seems like the exact "paint-by-numbers" stuff that we always see with AI: it's capable of taking existing art and mashing it together, but there's nothing interesting or novel being created here. It's the pinnacle of carbon-copy, technically impressive but still soulless art.

If you played me either of these songs without telling me they were AI, I would think: OK, weird cover of Blackbird? Not particularly moving, but perhaps a stepping off point to something more. What would interest me more than the music itself is the story behind why the song was covered, who was performing it, what were their intended emotions in creating it? And of course, since it's a cover, if they had any original material of their own. Knowing that it's just an audio file being synthesized by an AI takes all of the enjoyment out of it for me (not that it was particularly good to begin with).

Music is not simply good because of the sound entering your ears. The story, the artist behind it, the intended emotion and artistry is part of the experience. AI does not deliver on this, and I doubt it ever will, because human connection is what underlies those extra-musical qualities. I care about the people who made the music; I will never care about the machine.

Yes, I chose the lyrics to Blackbird because they are short, it generates in 2:00 chunks, and they don't have obvious meter. It's not like a limerick or something. I wanted to test the musical capabilities, I wasn't interested in generating lyrics, though that can obviously be done.

As someone who has studied and played music professionally my whole life, this isn't paint by number, at least in the way you think it is.

Most music is very similar. You can write 90% of most popular music using the 6 diatonic trichords, and 80% probably with only 4. There are also only so many ways to rearrange those.

This is not using a book of chord changes or melodies. I can promise you that after listening to 100s of versions of Blackbird, Yesterday, and the ABCs, I do not see the sort of pull the changes from a database that you're implying. In fact, I'd wager I could probably find a dozen songs that use the exact same chord changes and you wouldn't even have realized prior. I know because it's a great wedding gig trick, and we'd do it all the time.

I don't know what changes you think were carbon copied in the first link. It shows really good songwriting skills. Use of tension and surprise. "It sounds like everything else in that genre." Yes, that's what a genre is.

It also shows skill at vocal phrasing. This is really difficult and makes or breaks any song you try to write.

Explaining how difficult it is to write good melodies while putting a hard syllabic constraint on it is beyond the scope here. It's not easy or obvious and usually sounds like shit. Like, try to sing Sweet Home Alabama along with the chord changes at the end of "Layla". After doing essentially that for years (that's exactly what songwriting is) I doubt I could do it and not have it sound idiotic. This thing can do that.

Here's an example of it mimicking Russian to English phrasing: https://app.suno.ai/song/0d2d817f-4bf3-4837-85ab-9ff13abe9b4...

David Bowi-ish phrasing: https://app.suno.ai/song/a6b9e419-2e0f-4208-b66e-929b2076d96...

West Coast Hip-Hop banger alert if you're over 35 https://app.suno.ai/song/82e05d5b-7e0f-4715-8ed4-5f3d22fb81d...

Acid Funk https://app.suno.ai/song/de174b94-1758-4fae-b497-93b79f384a2...

Like I said, I have about 100 of these. This shit is nuts.

Here's some ABC's

Bossa Nova https://app.suno.ai/song/0fe61c85-62be-4327-9f05-5b0865353a6...

90's Grunge https://app.suno.ai/song/321a48e1-611b-4ec6-af0e-6e88815621c...

Baroque https://app.suno.ai/song/68257dae-031f-4910-a013-9bc0281cee2...

If you're thinking "Oh that just sounds like the Brandenburg Concertos" YEAH. THAT'S MY POINT. This level of mimicry is brand new. I've never seen anything close to doing anything like this. If you have, I'm all ears.

Now since you think originality is important, here is a poem I wrote for a girl I dated that worked at a coffee shop. Never intended it to be a song, but I love it.

https://app.suno.ai/song/9346c871-5e7f-439a-9134-d876aca7086...

https://app.suno.ai/song/2be696db-3b3b-48df-9075-66d3388cc11...

When I showed all this stuff to my musician friends this weekend (some who contract with Disney, Netflix, write scores, actively touring, etc), the reactions were actual tears, complete disbelief, shock, and existential dread.

$1000 says there's no way you could tell me whether this is a Chopin original or not played amongst others, and that's my point. And if you say "While sure, but that's not really that impressive because computers", frankly, you don't know what you're talking about and have already made your mind up.

https://app.suno.ai/song/bab48740-b977-4ee3-bdb8-0bc85995047...

AI-generated music will just have AI-generated backstories to provide a narrative and story behind the lyrics for people that want that.

people witness entertainment to trick their minds, and the disbelief and astonishment in the human condition hinges on “there’s no way _i_ could do that” and once they know they literally could not have done it, they’re not impressed. they may pay to be fooled once, but never twice.

there’s a market for what you’re talking about, but that space is b2b and not c2c, which is where entertainment money flows.

tl;dr dollar for dollar, ai vs taylor swift, taylor swift wins every time, no contest.

as a programmer, i create many things that impress people, but when i show them the methods of that creation, i can palpably feel their excitement wane as they lose interest in the nuance of my execution.

beyond the surface, there’s an aspect of being impressed that is also the desire to take part in the recreation of it all.

my claim in the first person, “i’m visually impressed by many image generators, but i’m not interested in fiddling with knobs, buttons, and strings to recreate the image in my mind’s eye”

Art is a continuum of multimedia. At some abstraction you will be able to submit your piece into the collective and draw on increasingly precise inspo.

High budget movies already often have fully CGI characters in which the only human element is the voice, and now AI voice warping is nearly perfect it's an obvious move to eliminate the continuity risks by making voice actors fully interchangeable, or even fully synthetic.

And then even in non-Pixar style movies, many scenes use CGI body doubles for stunts, fully CGI scenes and so on. The human actors often don't appear in all their own scenes, or they're even brought back from death to keep working.

So that leaves things like music, sports, etc. Some music genres totally defocus the humans, like electronic music. They go via frequently changed pseudonyms and you never really see them perform in person. Sports I can see remaining fully human.

Also, most things in life are not two-player zero-sum games where you can construct an evaluation function and build a “decision quality” metric out of it. So I’m not sure what the takeaway should be in those cases.

Otherwise you'd see players who don't train with computers winning in tournaments against those who do.

This can be applied to other kinds of environment, such as code execution, simulation, video games, human-AI chat rooms or robots. But each environment has its own complexity and searching for good strategies can take a long time. It's the same with scientific research, got to validate the theory in the world.

Unfortunately, as an ex poker pro, I find it hard to imagine that AI "lifts people up" in domains like games. Sandholm's bots pretty much destroyed poker.

What can beat a machine? Another machine, sadly. We are well past the point where machines surpasses humans in most poker variants.

Just knowing that you could do better is a big deal, but if you have an AI showing you how to do better, then further perfection will become inevitable.

2) I really wish we could shake the ant-farm with chess and go Fischer random chess. There's something nice about not having to memorize openings.

Imagine completely random WoW battlegrounds. Part of the fun is knowing the territory and strategies rather than having to make them up from scratch each game.

it's good seeing that that wasn't the case for all players.

Just like in mathematics many professional Go player peak before 40, after which the slowly become weaker and weaker.

that's why you play against HUMANS.

But it did not used to apply for every single game. In fact in Go it was famously true just a few years ago that the best humans were waay waay better than even the best computers.

At uni I hung out with Go players and they had all kind of theories why Go is particularly hard for computers. Some of them well quite well reasoned, and some were just a bunch of magical thinking. What was not in doubt is that even at that admitedly medium level the players were better than the computers they had access to.

Just a few years ago it went from “we are competing to be the best Go players period” to “we are competing to be the best human Go players”. That is a change in mindset, and it seems at least for that one particular player they couldn’t make the change.

Imagine that you grew up in a world where aimbots are just worse than mediocre players, always. And you build up your personal motivations with this fact. And then suddenly the aimbots get better than even the best players. Some systems of motivation will crumble when this happens. Some will manage to adapt. This is just how it is.

Imagine if you grow up where there are no monkeys, mountain goats or helicopter rides to the top. You never heard of them, they are not a thing in your world. And you put in hard work to become a very very good rock climber. You kinda fancy yourself an apex climber. Maybe your mate George is a bit faster than you sometimes, but sometimes you are faster than him. Sometimes Sarah beaths the both of you, but sometimes you beat her times. You are kinda up there with the bests as far as you know. And then suddenly someone brings a monkey to your rock climbing gym and the monkey smokes all of you. It climbs walls much better than you ever could. Now you have to adapt. Will you change your viewpoint and start seeing yourself as “best among humans” and keep competing like that? Or will you see yourself as “clearly outcompeted so badly I might even give up”? Some people will go one way, some the other way. And then the new generation will grow up with the knowledge of monkeys, and they all naturally will be only the first kind of people, who understand that they can’t be the best only best among their class.

Go players had their “the first monkey shows up in the climbing gym” moment during our lifetime. That is why you see some of the players react like that. That is a very different world from rock climbing where everyone already knows about monkeys, and mountain goats and helicopter rides since forever. Every person currently climbing rocks started climbing with the existence of monkeys, mountain goats and helicopters already incorporated into their thinking way before they climbed their first wall.

At the highest level of play, exploration of the game space eclipses point capture.

Points don't matter because it's play, so your emotional attachment to the outcome is a measure of maturity.

Play with infinite boundaries is pointless, so they let us explore freely without having emotional stake in arbitrary accumulation.

To play with an asymmetrical opponent should be some parts learning with sparse competition to test your knowledge of finite play space.

People are deeply concerned about how their livelihoods and identities will survive the next few years. I get it, and while there’s certainly a level of existential dread that feels reasonable, I don’t see many people yet discussing what the visual arts industries will look like on the other side.

If Go play is in any way a creative exercise—which I’ve heard it is— then I’m super interested to see the state of humans in the arts 24 months out from now.

As I see it AI can absolutely find innovative solutions, but only if you can clearly and explicitly define the problem it needs to solve.

Isn't this reminiscent of the arguments that were made at the dawn of photography as an art form? Some were afraid that portraiture was finished as an art form, but we got Impressionism, Cubism, and a host of other innovative forms to take its place. Never mind that portraiture was not in fact killed by photography, nor was any other visual form.

Others swore that cameras and film would never be valid implements of art, but they got awfully quiet when Adams and Weston and others showed up on the scene, and you don't hear much from them at all these days.

If nobody was afraid of AI -- if nobody was screaming bloody murder about how urgent it was to stop it -- only then could we safely say that it will have no role or relevance in art.

On one hand, the art world has been steadily pushing boundaries since the 19th century, and computer technology is just one blip on the vast radar of interesting subjects (other fashionable ones being gender, colonialist history, social practices, and physical properties of paint).

On the other hand, art is mostly created by artists who were professionally trained as artists, i.e. not as scientists. Knowledge about computer technology is typically rather limited with both artists and collectors, leading to fairly bland stuff, or properly misguided hypes such as NFTs.

Having reread this section, and considered it, I’m going to hard disagree. You are severely underrating the technical capabilities of artists. Historical and contemporary.

The chances of an artist having a PhD in machine learning and a masters at Goldsmiths are statistically speaking very low.

The illustrators and digital artists I know would generally disagree.

As an abstract painter, I agree with you.

Significant genre specificity.

When I scroll through the latest highly rated work on Midjourney, for example, I'm reminded mostly of tacky poster shop stuff.

Illustrators and graphic artists are in a hard spot given the commercial work they do. I’ve worked in this industry in the past and can attest that many of the contracts I executed on cared less about the originality of the output and more about the specifics of the prompt.

Generative AI is enough for a lot of clients, if the price is right, even when the output is subjectively bad.

I'm inclined to say that AI companies should have to pay for the training data they use although that does seem to mean only companies with billion dollar warchests can train AIs.

I feel sorry for the illustrators, and wonder how they'll be able to sustain their creative passions.

A common theme is the effects of the computers on the human players in elevating (but maybe also homogenizing) play.

Wait. I know where you are coming from, but this is simply not true.

When AI beats Hollywood...

If the same was true for chess or go then players would be using computer assistance in every top level game.

AI changing conceptions about chess or go is very different than generative Ai which can radically change the means of how something is produced. I'm still going to play go because I love it. Meanwhile, I would happily cut out film studios (as we know them) if it meant I got to watch quality cinema.

Equating intelligence to being good as these games is as silly as equating intelligence to being good at solving differential equations. Computers have bested humans at solving differential equations for many decades now. Nobody said "gee humans are now stupid".

AI, as a knowledge field, is biased in this notion that all it matters when it comes to intelligence is that computers beat humans at Go or Chess.

Human Instrumentality Project

Just be a human, play against other humans. Who cares what AI can do?