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Have you played go? When you play Go you start to appreciate how complex it is, how strong Lee Sedol is and what this achievement means. I could play my entire

Have you played go? When you play Go you start to appreciate how complex it is, how strong Lee Sedol is and what this achievement means. I could play my entire life and never achieve the level of play Lee Sedol has. AlphaGo surpassed that level in a matter of months. As a matter of fact, many professionals that play the game for a living as their main occupation, since they're kids, will never achieve the level that Lee Sedol has. Also from here, they can add more infrastructure and feed the bot with more training data and let it train for a longer time, and it will be distancing it more and more from human level play. Then, this task specifically is not just any random task... it's one of the most complex games, one the oldest games, and is also professionally played and studied. Then, AGI is not a requirement for disrupting our world. You can have ANI for one specific task that is performed by millions of humans and you have disrupted the world enough to make it a problem.

shostack 11y ago
I think that velocity is the fascinating part. I remember being intrigued while reading "Influx" by Daniel Suarez (great tin foil hat thriller that is hitting scarily close to home these days) about the training of the various AIs that were used by the government. I wonder what happens once we grow an AI whose purpose is to excel at training other AI's. Will we see an exponential leap forward in the speed with which they are trained?
- catshirt 11y ago
  start here https://en.m.wikipedia.org/wiki/Technological_singularity https://en.m.wikipedia.org/wiki/Technological_singularity
- pdkl95 11y ago
  > exponential leap forward A recent(-ish) Computerphile video has a good overview of the AI-self-improvement problem. https://www.youtube.com/watch?v=5qfIgCiYlfY https://www.youtube.com/watch?v=5qfIgCiYlfY I recommend watching the previous video first, which introduces the problem of strong-AI rapidly finding solutions (especially with poorly-specified questions) that may not be in humanity's best interest. https://www.youtube.com/watch?v=tcdVC4e6EV4 https://www.youtube.com/watch?v=tcdVC4e6EV4 Before anybody jumps to overly-sensational conclusions, note that the last video in that series, Rob Miles explains how exponential self-improvement is an extreme point in the space of possible AI development. We don't know how to predict discoveries[1], so we need more research into AI, so we can hopefully make something that isn't exponentially growing beyond our understanding. https://www.youtube.com/watch?v=IB1OvoCNnWY https://www.youtube.com/watch?v=IB1OvoCNnWY [1] see James Burke's non-teleological view of change
  - Retra 11y ago
    Finding solutions doesn't imply that those solutions are implemented. I find a dozen solutions that are not in humanities interest every day. Does that put anybody at risk? Maybe if you were stupid enough to make me Dictator of the World or something. And you certainly shouldn't do that until I convincingly demonstrate that I am very very committed to finding solutions that are in humanity's best interests. I'm baffled at how easily people assuming a computer thinking something means it's going to happen. There are a trillion pieces that have to fall in place for that to happen accidentally, and if it doesn't happen accidentally, your problem is a human social problem, not an AI problem. And I know what someone is going to think: "The AI might be smart enough to figure out how to make those trillion pieces fall in place." But then, who cares? So what if it figures out how to do something. It still has to be done. And we're the ones who have to do it.
    - AgentME 11y ago
      The AI could come up with ideas that benefit the people that enact them in the short term. If the AI can earn its own money, then it's not that hard for it to use that money to pay people. For an extreme example, you could imagine an AI getting rich from the stock market (or mechanical turk, etc), then buying up ridiculous amounts of land for paperclip factories and paying workers. The people that want to feed their families or get rich from selling their factories are the ones who will enact the AI's plans. How many conscientious objectors do you expect?
      - nefitty 11y ago
        The "rich AI" problem didn't seem feasible to me until I realized how powerful and flexible bitcoin is. Now add the power of contracts through ethereum and a machine could actually harness a significant amount of leverage over human actors. With traditional contracts, enforcement would have left the power in government hands, and with traditional banking as well. We've now stepped into an era where we might literally have built the bat and shovel AI will use to get humanity into its grave. /panic
      - Retra 11y ago
edanm 11y ago
I think you put too much emphasis on the vast gulph between most players and Lee Seedol. This is true of pretty much any game/sport where there are professionals. Most footballers will never be Messi, most hockey players will never be Gretzky, etc. This is true in every field where you have that kind of power-law distribution.
- bduerst 11y ago
  For physical sports, it's easy to robotically build a machine to play and train some computer vision models to follow the ball/puck. For Go, the competition is on a different level that you couldn't just build a machine to play.
devishard 11y ago
> I could play my entire life and never achieve the level of play Lee Sedol has. AlphaGo surpassed that level in a matter of months. In one, very tiny area, sure. But step outside the very specific task of playing winning moves in go, and Lee Sedol wins every time. AlphaGo can't explain its moves to someone learning go, let alone cognitive tasks we take for granted like moving feet in a way that produces transportation. AlphaGo can't even do things that other programs can do, like play chess. Sure, you can "disrupt" with Monte Carlo algorithms like AlphaGo in the San Francisco startup sense of the word "disrupt", but that's also not new. AIs have been doing that for years in finance, genetics, chemistry. We've been able to write code that does one thing well for decades--what we lack is code with breadth of application, and AlphaGo doesn't even make a dent in that.
- Joeri 11y ago
  > AlphaGo can't explain its moves to someone learning go, let alone cognitive tasks we take for granted like moving feet in a way that produces transportation. I'm pretty sure lee sedol cannot explain his own moves either. Yes, he can rationalize why that move is a good move, but he won't be able to explain the complete reasoning process that led to that move, because it relies on intuition. He cannot train a random person to play at the level of lee sedol. If you look at the human brain, every part learns to do just one thing. Alphago is like having just the part of the brain that plays go. Moving from there to something more general is a question of assembling the parts. Building a biped robot that walks and plays go is not a hard problem. The fundamental question is whether us humans are more than the sum of our parts. If you add task-specific AI bits together, will you at some point have something that becomes a person, or will it always be a collection of features?
  - gcp 11y ago
    I'm pretty sure lee sedol cannot explain his own moves either. Yes, he can rationalize why that move is a good move, but he won't be able to explain the complete reasoning process that led to that move, because it relies on intuition. He cannot train a random person to play at the level of lee sedol. I think you're wrong here. I don't see why that wouldn't be the case? The "intuition" just means he arrives at the move quickly without consciously going through the thought process, but why wouldn't he be able to reconstruct it?
rimantas 11y ago
I wonder how the 13x13 or 21x21 match would go. IIRC AlphaGo was tuned to 19x19 board.
TheLogothete 11y ago
Go might be a complex game, but in the grand scheme of things it is a trivial problem to solve, very narrow in scope. You perform an infinitely more complex task everyday when you make breakfast. AlphaGo's achievement is that it learned how to open the fridge with great efficiency.
- partycoder 11y ago
  Go play a single game of Go and tell me how it goes.
  - TheLogothete 11y ago
    I don't think you understand how machine learning works. Better not to opine on something you don't understand.
    - partycoder 11y ago
      It is a narrow application of general purpose machine learning approaches. Convolutional neural networks, supervised/reinforcement learning are not limited to Go. They've found success in a variety of applications. And while those applications are narrow, they're significant progress in a short period of time. In this game from only 4 years ago you could see one of the state of the art bots getting humilliated by an anonymous professional player: http://gosensations.com/?id=2&server_id=1&new_id=1392 http://gosensations.com/?id=2&server_id=1&new_id=1392
gcp 11y ago
Have you played go? When you play Go you start to appreciate how complex it is, how strong Lee Sedol is and what this achievement means. In 2006 a paper was published (by Remi Coulom) showing that just playing random moves and combining those random playouts with a tree search scaled up in strength arbitrarily as you throw more computational resources at the problem. Google made them a little less random and threw a ton of hardware and optimizations at it. (Basically 20 times as much as anyone before them, which goes a long way of explaining the performance jump) Don't make this something it is not.

shostack 11y ago

I think that velocity is the fascinating part. I remember being intrigued while reading "Influx" by Daniel Suarez (great tin foil hat thriller that is hitting scarily close to home these days) about the training of the various AIs that were used by the government. I wonder what happens once we grow an AI whose purpose is to excel at training other AI's. Will we see an exponential leap forward in the speed with which they are trained?

catshirt 11y ago
start here https://en.m.wikipedia.org/wiki/Technological_singularity https://en.m.wikipedia.org/wiki/Technological_singularity
pdkl95 11y ago
> exponential leap forward A recent(-ish) Computerphile video has a good overview of the AI-self-improvement problem. https://www.youtube.com/watch?v=5qfIgCiYlfY https://www.youtube.com/watch?v=5qfIgCiYlfY I recommend watching the previous video first, which introduces the problem of strong-AI rapidly finding solutions (especially with poorly-specified questions) that may not be in humanity's best interest. https://www.youtube.com/watch?v=tcdVC4e6EV4 https://www.youtube.com/watch?v=tcdVC4e6EV4 Before anybody jumps to overly-sensational conclusions, note that the last video in that series, Rob Miles explains how exponential self-improvement is an extreme point in the space of possible AI development. We don't know how to predict discoveries[1], so we need more research into AI, so we can hopefully make something that isn't exponentially growing beyond our understanding. https://www.youtube.com/watch?v=IB1OvoCNnWY https://www.youtube.com/watch?v=IB1OvoCNnWY [1] see James Burke's non-teleological view of change
- Retra 11y ago
  Finding solutions doesn't imply that those solutions are implemented. I find a dozen solutions that are not in humanities interest every day. Does that put anybody at risk? Maybe if you were stupid enough to make me Dictator of the World or something. And you certainly shouldn't do that until I convincingly demonstrate that I am very very committed to finding solutions that are in humanity's best interests. I'm baffled at how easily people assuming a computer thinking something means it's going to happen. There are a trillion pieces that have to fall in place for that to happen accidentally, and if it doesn't happen accidentally, your problem is a human social problem, not an AI problem. And I know what someone is going to think: "The AI might be smart enough to figure out how to make those trillion pieces fall in place." But then, who cares? So what if it figures out how to do something. It still has to be done. And we're the ones who have to do it.
  - AgentME 11y ago
    The AI could come up with ideas that benefit the people that enact them in the short term. If the AI can earn its own money, then it's not that hard for it to use that money to pay people. For an extreme example, you could imagine an AI getting rich from the stock market (or mechanical turk, etc), then buying up ridiculous amounts of land for paperclip factories and paying workers. The people that want to feed their families or get rich from selling their factories are the ones who will enact the AI's plans. How many conscientious objectors do you expect?
    - nefitty 11y ago
      The "rich AI" problem didn't seem feasible to me until I realized how powerful and flexible bitcoin is. Now add the power of contracts through ethereum and a machine could actually harness a significant amount of leverage over human actors. With traditional contracts, enforcement would have left the power in government hands, and with traditional banking as well. We've now stepped into an era where we might literally have built the bat and shovel AI will use to get humanity into its grave. /panic
    - Retra 11y ago

edanm 11y ago

I think you put too much emphasis on the vast gulph between most players and Lee Seedol. This is true of pretty much any game/sport where there are professionals. Most footballers will never be Messi, most hockey players will never be Gretzky, etc. This is true in every field where you have that kind of power-law distribution.

bduerst 11y ago
For physical sports, it's easy to robotically build a machine to play and train some computer vision models to follow the ball/puck. For Go, the competition is on a different level that you couldn't just build a machine to play.

devishard 11y ago

> I could play my entire life and never achieve the level of play Lee Sedol has. AlphaGo surpassed that level in a matter of months. In one, very tiny area, sure. But step outside the very specific task of playing winning moves in go, and Lee Sedol wins every time. AlphaGo can't explain its moves to someone learning go, let alone cognitive tasks we take for granted like moving feet in a way that produces transportation. AlphaGo can't even do things that other programs can do, like play chess. Sure, you can "disrupt" with Monte Carlo algorithms like AlphaGo in the San Francisco startup sense of the word "disrupt", but that's also not new. AIs have been doing that for years in finance, genetics, chemistry. We've been able to write code that does one thing well for decades--what we lack is code with breadth of application, and AlphaGo doesn't even make a dent in that.

Joeri 11y ago
> AlphaGo can't explain its moves to someone learning go, let alone cognitive tasks we take for granted like moving feet in a way that produces transportation. I'm pretty sure lee sedol cannot explain his own moves either. Yes, he can rationalize why that move is a good move, but he won't be able to explain the complete reasoning process that led to that move, because it relies on intuition. He cannot train a random person to play at the level of lee sedol. If you look at the human brain, every part learns to do just one thing. Alphago is like having just the part of the brain that plays go. Moving from there to something more general is a question of assembling the parts. Building a biped robot that walks and plays go is not a hard problem. The fundamental question is whether us humans are more than the sum of our parts. If you add task-specific AI bits together, will you at some point have something that becomes a person, or will it always be a collection of features?
- gcp 11y ago
  I'm pretty sure lee sedol cannot explain his own moves either. Yes, he can rationalize why that move is a good move, but he won't be able to explain the complete reasoning process that led to that move, because it relies on intuition. He cannot train a random person to play at the level of lee sedol. I think you're wrong here. I don't see why that wouldn't be the case? The "intuition" just means he arrives at the move quickly without consciously going through the thought process, but why wouldn't he be able to reconstruct it?

rimantas 11y ago

I wonder how the 13x13 or 21x21 match would go. IIRC AlphaGo was tuned to 19x19 board.

TheLogothete 11y ago

Go might be a complex game, but in the grand scheme of things it is a trivial problem to solve, very narrow in scope. You perform an infinitely more complex task everyday when you make breakfast. AlphaGo's achievement is that it learned how to open the fridge with great efficiency.

partycoder 11y ago
Go play a single game of Go and tell me how it goes.
- TheLogothete 11y ago
  I don't think you understand how machine learning works. Better not to opine on something you don't understand.
  - partycoder 11y ago
    It is a narrow application of general purpose machine learning approaches. Convolutional neural networks, supervised/reinforcement learning are not limited to Go. They've found success in a variety of applications. And while those applications are narrow, they're significant progress in a short period of time. In this game from only 4 years ago you could see one of the state of the art bots getting humilliated by an anonymous professional player: http://gosensations.com/?id=2&server_id=1&new_id=1392 http://gosensations.com/?id=2&server_id=1&new_id=1392

gcp 11y ago

Have you played go? When you play Go you start to appreciate how complex it is, how strong Lee Sedol is and what this achievement means. In 2006 a paper was published (by Remi Coulom) showing that just playing random moves and combining those random playouts with a tree search scaled up in strength arbitrarily as you throw more computational resources at the problem. Google made them a little less random and threw a ton of hardware and optimizations at it. (Basically 20 times as much as anyone before them, which goes a long way of explaining the performance jump) Don't make this something it is not.