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I don't think this is really why AlphaGo is a big deal. He's not wrong about the ways that it works vs. DeepBlue, but there's a much simpler way to understand w

I don't think this is really why AlphaGo is a big deal. He's not wrong about the ways that it works vs. DeepBlue, but there's a much simpler way to understand why AlphaGo is a big deal. (I am not an AI researcher, but I work with AI researchers, like by putting on training programs and recruiting for them, and working out of the same office, and this is a summary of [my understanding of] their discussions about this topic.) AGI is a Hard Problem (TM), and no one really knows how hard it is. General intelligence is made of algorithms that are somewhere on a spectrum between dizzyingly complex math that we have decades+ more work to develop, and simple components that someone in their basement could stumble upon at any moment if only we had a couple key insights. (Sorry for the gross oversimplification.) Before AlphaGo there was a whole area of probability space that seemed plausible--a whole area of complexity that had yet to be worked out in detail. AlphaGo proved that more or less current AI tech was sufficient for that problem (current meaning one incremental step forward, as opposed to some revolutionary insight). That's why they "moved the field forward by 10 years"--by proving that the work that AI researchers basically expected to have to do over the next 10 years is not necessary. Said another way, we're closer to the "simple components" end of the spectrum than we collectively thought. AlphaGo proved that, and that's why it's a big deal.

partycoder 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. 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.
th0ma5 11y ago
I think you almost hit the nail on the head, but I believe it is more specific to say that we're more towards the "simple components" end at least for this one problem space ... For instance, a combo of DNN for a generally visual overview strategy, and then tree search, at least for the 2D game of Go.... which is flipping astounding, sure, but I keep seeing daily where people are continuing to play with DNN with varying levels of success, and it is by no means general intelligence. So, we do have this nice tool that sort of lets us "look" in "general" way, but it is not entirely understandable, or traceable, which is the nature of NN... It would be nice to be able to prove exactly why something happened, and to be able to have reproducible, exact training. Anyway... I guess I don't have much of a point beyond these two vague minor thoughts, but I did want to add them in here...
agent008t 11y ago
But AlphaGo didn't prove that we're any closer to general intelligence! In fact, the fact that we have an AI that can beat one of the best human Go player, yet we do not have an AI that is as intelligent as, say, a cat, shows that we may actually be digging in the wrong direction. Cats don't play Go. Elephants don't play chess. And even what we think of as the most intelligent species, humans, aren't particularly good at either chess or Go.

partycoder 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. 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.

th0ma5 11y ago

I think you almost hit the nail on the head, but I believe it is more specific to say that we're more towards the "simple components" end at least for this one problem space ... For instance, a combo of DNN for a generally visual overview strategy, and then tree search, at least for the 2D game of Go.... which is flipping astounding, sure, but I keep seeing daily where people are continuing to play with DNN with varying levels of success, and it is by no means general intelligence. So, we do have this nice tool that sort of lets us "look" in "general" way, but it is not entirely understandable, or traceable, which is the nature of NN... It would be nice to be able to prove exactly why something happened, and to be able to have reproducible, exact training. Anyway... I guess I don't have much of a point beyond these two vague minor thoughts, but I did want to add them in here...

agent008t 11y ago

But AlphaGo didn't prove that we're any closer to general intelligence! In fact, the fact that we have an AI that can beat one of the best human Go player, yet we do not have an AI that is as intelligent as, say, a cat, shows that we may actually be digging in the wrong direction. Cats don't play Go. Elephants don't play chess. And even what we think of as the most intelligent species, humans, aren't particularly good at either chess or Go.