4 ms·

For me, that move in game 2 marked the official start of the Singularity, although in one sense it's all the same exponential curve we've been riding since, wel

by taneq 3y ago

For me, that move in game 2 marked the official start of the Singularity, although in one sense it's all the same exponential curve we've been riding since, well... That's the neat thing about exponential curves, you always feel like you're at the fun part of them.

Gud 3y ago
Maybe for you it did, but not for the rest of the world. The singularity is defined as a moment in time when the A.I. improves itself to such a degree that humanity can no longer keep up. Throwing more compute power vs a single opponent is not the same thing. How would this computer fare against the top 10 best players collaborating(or even top 90-100)? I would bet it would lose big time.
- bondarchuk 3y ago
  I agree with taneq actually, just after the match I attended an impromptu lecture by a professor who also had some go knowledge, at the time I really felt like I was witnessing something new and important. In retrospect I still think of that as the kickoff of the current AI wave.
- vecter 3y ago
  How would this computer fare against the top 10 best players collaborating(or even top 90-100)? I would bet it would lose big time. It would destroy them in the same way. The marginal value of each additional human brain quickly approaches zero (or perhaps even negative as the team tries to communicate/collaborate). The top 10 chess grandmasters in the world working together could not beat the best (or even "mediocre") chess engine. Not even close. They're practically playing different games.
- blaufuchs 3y ago
  >How would this computer fare against the top 10 best players collaborating(or even top 90-100)? Against the AlphaGo of 2015? They might win, but probably not (I think you're overestimating how much collaboration would help). Against today's AlphaGo/KataGo/FineArt/etc there's literally zero chance, even with a two stone handicap. Same goes for 100 GMs playing collaborative chess against Stockfish. (that said, I agree calling this the singularity is overkill)
- toolz 3y ago
  I don't know much about go, but AI is so much better at chess than humans there is no number of humans you could throw at the problem to beat the engine.
  - Kranar 3y ago
    You are correct. The closest thing there is is called Centaur Chess where a human and computer work together. At least as of 2021, a human and computer combination could outperform just a computer indicating that a human still provides value to a chess engine. I believe in Go that is no longer the case, however. A human provides no additional value to a Go engine.
- helpfulmountain 3y ago
  There is no chance that humans can beat the best Go AI anymore, since the paradigm of AlphaZero (which was trained in the absence of human game records, and beat the version which beat Lee Sedol essentially 100-0) It is unlikely, also, that a committee of players would be significantly better than a single master, due to lack of coherence -- but that's an interesting idea! I wonder if a committee of the top 100 go players playing a game by vote could beat someone in the top 10 more than 20-0 or something; i doubt it -- it might even go the other way (that the single player would win the series) I don't think this counts as the real "start of the singularity" because Alphazero was not able to (or capable of) altering its own algorithm, but rather just adjusting its weights. Something more akin to being in the long march toward general AI. As a personal note the whole issue of large LLM's capacity for intelligence, beauty, humanity, morality, logic, etc etc was softened in my mind and heart by witnessing with rapt attention this epochal shift in computing. I had held Go up as a paragon of human brilliance and beauty -- to see that standard fall was a complex process of grief and discovery for me, which I feel has better prepared me for understanding and appreciating the emergence of LLMs
  - the8472 3y ago
    It has been tried in chess at least. https://en.wikipedia.org/wiki/Kasparov_versus_the_World https://en.wikipedia.org/wiki/Kasparov_versus_the_World
    - cool_dude85 3y ago
      This is a different kind of setup. I'm not sure if the idea of 2-3 super GMs able to consult with one another has been tried but given the estimated rating difference I doubt it would matter. The difference is estimated around 800 points, or the difference between a strong untitled player and Magnus.
  - hosh 3y ago
    Like many other crafts and arts where machines can do better, Go has a deeper role in being transformative for the human learning it -- in the case of Go, developing strategic thinking, being able to make decisions balancing long term and short term gain, uniting reasoning and intuition, an arena for exercising emotional equanimity. Winning, I think, is secondary to this. It's a useful measure of how one has progressed in that transformation, but I think the lessons and principles from Go that I can apply in guiding my my day-to-day life are more valuable.
- kadoban 3y ago
  At Go? Humans cannot win anymore, the AIs are _very_ good. Katago can give handicap stones to pros and win. It's as much better than pros as pros are better than unserious amateurs. It's not even a matter of compute power, katago is very good with 0 playouts.
- PaulRobinson 3y ago
  Deep Mind's big thing with the game of go, and in general to be honest, is Reinforcement Learning (RL), a branch of ML that until very recently was mostly ignored by industry, and only now gets love because of its perceived utility in some parts of the GenAI tooling chain. I think even with that in account, RL has only reached a tiny fraction of its potential. We have focused so much on supervised and unsupervised learning for so many years, and then been wow'ed by LLMs we have only see RL start to impact industries in self-driving/flying vehicles, and forget about all the other potential. The thing about RL that people don't seem to understand is that it is mathematically proven to find the optimal control policy. In the context of go, that means the only way it can be beaten is through variance (or "luck", if you prefer). As there is no dice or random element in go, the top players in the World basically have to be optimal in every move to get a draw. And then again, and again, and again. And that's the best they can do if the RL algorithm has stopped learning - it's found an optimal strategy, and it can't be beaten, only matched. Think about all the optimisation and control problems out there that could benefit from this. And yet still we seem to think it's like supervised/unsupervised learning and only "accurate" to ~90+%, and so it doesn't get the attention it deserves. Or perhaps I'm a dreamer and an optimist and you're right. I would happily take the other side of that bet though. At even money, I have all the EV, I'm confident of it.
- raincole 3y ago
  > How would this computer fare against the top 10 best players collaborating(or even top 90-100)? I would bet it would lose big time. That's why you shouldn't bet money on things you don't know about...

by taneq 3y ago

Gud 3y ago

Maybe for you it did, but not for the rest of the world. The singularity is defined as a moment in time when the A.I. improves itself to such a degree that humanity can no longer keep up. Throwing more compute power vs a single opponent is not the same thing. How would this computer fare against the top 10 best players collaborating(or even top 90-100)? I would bet it would lose big time.

bondarchuk 3y ago
I agree with taneq actually, just after the match I attended an impromptu lecture by a professor who also had some go knowledge, at the time I really felt like I was witnessing something new and important. In retrospect I still think of that as the kickoff of the current AI wave.
vecter 3y ago
How would this computer fare against the top 10 best players collaborating(or even top 90-100)? I would bet it would lose big time. It would destroy them in the same way. The marginal value of each additional human brain quickly approaches zero (or perhaps even negative as the team tries to communicate/collaborate). The top 10 chess grandmasters in the world working together could not beat the best (or even "mediocre") chess engine. Not even close. They're practically playing different games.
blaufuchs 3y ago
>How would this computer fare against the top 10 best players collaborating(or even top 90-100)? Against the AlphaGo of 2015? They might win, but probably not (I think you're overestimating how much collaboration would help). Against today's AlphaGo/KataGo/FineArt/etc there's literally zero chance, even with a two stone handicap. Same goes for 100 GMs playing collaborative chess against Stockfish. (that said, I agree calling this the singularity is overkill)
toolz 3y ago
I don't know much about go, but AI is so much better at chess than humans there is no number of humans you could throw at the problem to beat the engine.
- Kranar 3y ago
  You are correct. The closest thing there is is called Centaur Chess where a human and computer work together. At least as of 2021, a human and computer combination could outperform just a computer indicating that a human still provides value to a chess engine. I believe in Go that is no longer the case, however. A human provides no additional value to a Go engine.
helpfulmountain 3y ago
There is no chance that humans can beat the best Go AI anymore, since the paradigm of AlphaZero (which was trained in the absence of human game records, and beat the version which beat Lee Sedol essentially 100-0) It is unlikely, also, that a committee of players would be significantly better than a single master, due to lack of coherence -- but that's an interesting idea! I wonder if a committee of the top 100 go players playing a game by vote could beat someone in the top 10 more than 20-0 or something; i doubt it -- it might even go the other way (that the single player would win the series) I don't think this counts as the real "start of the singularity" because Alphazero was not able to (or capable of) altering its own algorithm, but rather just adjusting its weights. Something more akin to being in the long march toward general AI. As a personal note the whole issue of large LLM's capacity for intelligence, beauty, humanity, morality, logic, etc etc was softened in my mind and heart by witnessing with rapt attention this epochal shift in computing. I had held Go up as a paragon of human brilliance and beauty -- to see that standard fall was a complex process of grief and discovery for me, which I feel has better prepared me for understanding and appreciating the emergence of LLMs
- the8472 3y ago
  It has been tried in chess at least. https://en.wikipedia.org/wiki/Kasparov_versus_the_World https://en.wikipedia.org/wiki/Kasparov_versus_the_World
  - cool_dude85 3y ago
    This is a different kind of setup. I'm not sure if the idea of 2-3 super GMs able to consult with one another has been tried but given the estimated rating difference I doubt it would matter. The difference is estimated around 800 points, or the difference between a strong untitled player and Magnus.
- hosh 3y ago
  Like many other crafts and arts where machines can do better, Go has a deeper role in being transformative for the human learning it -- in the case of Go, developing strategic thinking, being able to make decisions balancing long term and short term gain, uniting reasoning and intuition, an arena for exercising emotional equanimity. Winning, I think, is secondary to this. It's a useful measure of how one has progressed in that transformation, but I think the lessons and principles from Go that I can apply in guiding my my day-to-day life are more valuable.
kadoban 3y ago
At Go? Humans cannot win anymore, the AIs are _very_ good. Katago can give handicap stones to pros and win. It's as much better than pros as pros are better than unserious amateurs. It's not even a matter of compute power, katago is very good with 0 playouts.
PaulRobinson 3y ago
Deep Mind's big thing with the game of go, and in general to be honest, is Reinforcement Learning (RL), a branch of ML that until very recently was mostly ignored by industry, and only now gets love because of its perceived utility in some parts of the GenAI tooling chain. I think even with that in account, RL has only reached a tiny fraction of its potential. We have focused so much on supervised and unsupervised learning for so many years, and then been wow'ed by LLMs we have only see RL start to impact industries in self-driving/flying vehicles, and forget about all the other potential. The thing about RL that people don't seem to understand is that it is mathematically proven to find the optimal control policy. In the context of go, that means the only way it can be beaten is through variance (or "luck", if you prefer). As there is no dice or random element in go, the top players in the World basically have to be optimal in every move to get a draw. And then again, and again, and again. And that's the best they can do if the RL algorithm has stopped learning - it's found an optimal strategy, and it can't be beaten, only matched. Think about all the optimisation and control problems out there that could benefit from this. And yet still we seem to think it's like supervised/unsupervised learning and only "accurate" to ~90+%, and so it doesn't get the attention it deserves. Or perhaps I'm a dreamer and an optimist and you're right. I would happily take the other side of that bet though. At even money, I have all the EV, I'm confident of it.
raincole 3y ago
> How would this computer fare against the top 10 best players collaborating(or even top 90-100)? I would bet it would lose big time. That's why you shouldn't bet money on things you don't know about...