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I think you are being hyperbolic here. Go is in a lot of respect much more challenging than machine translation, speech to text and auto-driving. All of the lat

I think you are being hyperbolic here. Go is in a lot of respect much more challenging than machine translation, speech to text and auto-driving. All of the latter problems you described have had solutions for decades whereas Alpha Go is literally state of the art. It even uses proprietary hardware (TPU's) and was essentially engineered by one if not the most elite ML lab in the world. Their solution consists of an ensemble of many models including MCTS, supervised learning, reinforcement learning. Much more complicated than what you are thinking. Go read the paper, it's free. Go is a lot harder than you think for machines to play.

mcintyre1994 9y ago
I'm not disagreeing because I don't know how to even start comparing those challenges to Go, but just a comment - it's interesting that you chose areas where Google's elite ML teams are state of the art or close to it and where they're probably using TPUs or will be soon.
deleted 9y ago
[deleted]
Cybiote 9y ago
I agree, Go is a challenging game for computers but even more so for humans. However, for computers, Go is not more challenging than translation, speech to text or driving or understanding a comic book. The progression of superhuman play in famous board games has gone as checker->chess-> go. The more a game relies on memory,tactics and evaluation speed, the quicker it falls. In terms of commonly played perfect information games, Go is most difficult both from having a large branching factor and no simple evaluation heuristic. In each of these, before there was superhuman play, there was super amateur human play for decades (including in Go). Translation still has many failure cases. Speech to text cannot yet handle intonation and auto-driving cannot yet handle driving in places like India. And reading then summarizing a page of a comic book while walking across a room is currently impossible.
- NhanH 9y ago
  10 years ago when I was learning Go. I can beat the strongest bot within months of learning the rule. Super amateur human play in Go AI is barely 5 years ago, if that.
  - Cybiote 9y ago
    That doesn't affect my core point: many of the things that humans have commonly associated with intelligence have been the first to fall. In hindsight it makes sense, we mistakenly assumed that there was such a thing as a universal rank of difficulty centered on what humans find hard to reason about. More to your point, my decades remark had a weaker notion of amateur. For each game, we've had something that could beat most humans for decades. But you're right, that's not a useful distinction. If we look just at Go the decades remark is somewhat of a stretch. Go has been especially difficult, requiring more intelligent algorithms in order to solve branching and state evaluation (and the latter in particular, is a function too complex to fit in human consciousness). But progress has been occurring for years. On 9x9 boards MCTS bots have been stronger than most humans since about 2007, 10 years ago. For 19x19 it's true, if we pick 4 or 5 dan as better than most amateurs then that's 6/7 years.
- computerex 9y ago
  You ignored my post entirely. The fact that it took the most elite ML lab in the world to engineer this solution using proprietary hardware never seen before that's orders of magnitudes faster at evaluating than what's available to the rest of us is a testament to how hard it is to beat Go. For a long time it was believed to be impossible. I am not comparing translation or machine vision to AlphaGo, I am merely pointing out that it comes with a broad set of challenges that you aren't even aware of and is a lot harder thank you think. AlphaGo can beat the next best go playing bot purely using its neural net ensemble without using MCTS, for example. That's a pleasantly surprising result never before seen, to think that it can beat another bot without doing a single tree search during play and evaluation is also a testament to how impressive it is.
  - Cybiote 9y ago
hyperbovine 9y ago
Autodriving has been solved for decades?
- computerex 9y ago
  https://en.m.wikipedia.org/wiki/History_of_autonomous_cars https://en.m.wikipedia.org/wiki/History_of_autonomous_cars
nickpsecurity 9y ago
The author isn't hyperbolic. The problems that require what we call common sense continue to defeat all AI's. These board games don't require that. It comes down to pattern recognition, heuristic search, and good hardware. The examples of language and driving are great as the AI's only seem to do well on really narrow situations that require no greater understanding of context. And far as common sense, there's only a handful of projects like Cyc that were trying to teach it to machines. Humans take 10-20 years of semi-supervised learning to acquire this combination of common sense, knowledge, and problem-solving. It also happens in stages where the infants or especially young children have brains in overdrive taking in everything followed by stages that are more selective about what they take in and solidify. Training AI's to be smart for real, common sense and all, might take over a decade of data for the first one unless this problem can be decomposed. Still will take years of similar experiences.
- computerex 9y ago
  Modern AI, things like AlphaGo are examples of applied AI. "Common sense" falls within the realm of artificial general intelligence, which is a line of research that's largely abandoned now in favor of applied AI. Modern AI solutions are engineered to solve very very specific problems. You are never going to see attempts to teach "common sense". https://arxiv.org/abs/1705.08807 https://arxiv.org/abs/1705.08807 With that said, the above is what the world's AI researchers think is possible hopefully within my lifetime using just applied AI without the notion of "common sense". Common sense is AGI. That's not the goal anymore. The goal is to do things like self driving cars. Both Google and Tesla have placed vehicles on the road that have driven for literally millions of miles. The idea is to build a bunch of classifiers and regression models and use them together in an ensemble to solve your problem. The same approach is being applied successfully to a lot of unrelated fields where deep learning is concerned. Also, modern AI doesn't even pretend to be biological in nature, in fact we'll known researchers like Andrew Ng make a point in saying that they are only biologically inspires and that's where the commonalities end. There are other models like HTM that are way more ambitious and want to come up with a single generalized scheme to solve a broad range of problems, AGI style. These guys think biology is important and are trying to emulate the neocortex. They ARE going for AGI, common sense, etc.

mcintyre1994 9y ago

I'm not disagreeing because I don't know how to even start comparing those challenges to Go, but just a comment - it's interesting that you chose areas where Google's elite ML teams are state of the art or close to it and where they're probably using TPUs or will be soon.

deleted 9y ago

[deleted]

Cybiote 9y ago

I agree, Go is a challenging game for computers but even more so for humans. However, for computers, Go is not more challenging than translation, speech to text or driving or understanding a comic book. The progression of superhuman play in famous board games has gone as checker->chess-> go. The more a game relies on memory,tactics and evaluation speed, the quicker it falls. In terms of commonly played perfect information games, Go is most difficult both from having a large branching factor and no simple evaluation heuristic. In each of these, before there was superhuman play, there was super amateur human play for decades (including in Go). Translation still has many failure cases. Speech to text cannot yet handle intonation and auto-driving cannot yet handle driving in places like India. And reading then summarizing a page of a comic book while walking across a room is currently impossible.

NhanH 9y ago
10 years ago when I was learning Go. I can beat the strongest bot within months of learning the rule. Super amateur human play in Go AI is barely 5 years ago, if that.
- Cybiote 9y ago
  That doesn't affect my core point: many of the things that humans have commonly associated with intelligence have been the first to fall. In hindsight it makes sense, we mistakenly assumed that there was such a thing as a universal rank of difficulty centered on what humans find hard to reason about. More to your point, my decades remark had a weaker notion of amateur. For each game, we've had something that could beat most humans for decades. But you're right, that's not a useful distinction. If we look just at Go the decades remark is somewhat of a stretch. Go has been especially difficult, requiring more intelligent algorithms in order to solve branching and state evaluation (and the latter in particular, is a function too complex to fit in human consciousness). But progress has been occurring for years. On 9x9 boards MCTS bots have been stronger than most humans since about 2007, 10 years ago. For 19x19 it's true, if we pick 4 or 5 dan as better than most amateurs then that's 6/7 years.
computerex 9y ago
You ignored my post entirely. The fact that it took the most elite ML lab in the world to engineer this solution using proprietary hardware never seen before that's orders of magnitudes faster at evaluating than what's available to the rest of us is a testament to how hard it is to beat Go. For a long time it was believed to be impossible. I am not comparing translation or machine vision to AlphaGo, I am merely pointing out that it comes with a broad set of challenges that you aren't even aware of and is a lot harder thank you think. AlphaGo can beat the next best go playing bot purely using its neural net ensemble without using MCTS, for example. That's a pleasantly surprising result never before seen, to think that it can beat another bot without doing a single tree search during play and evaluation is also a testament to how impressive it is.
- Cybiote 9y ago

hyperbovine 9y ago

Autodriving has been solved for decades?

computerex 9y ago
https://en.m.wikipedia.org/wiki/History_of_autonomous_cars https://en.m.wikipedia.org/wiki/History_of_autonomous_cars

nickpsecurity 9y ago

The author isn't hyperbolic. The problems that require what we call common sense continue to defeat all AI's. These board games don't require that. It comes down to pattern recognition, heuristic search, and good hardware. The examples of language and driving are great as the AI's only seem to do well on really narrow situations that require no greater understanding of context. And far as common sense, there's only a handful of projects like Cyc that were trying to teach it to machines. Humans take 10-20 years of semi-supervised learning to acquire this combination of common sense, knowledge, and problem-solving. It also happens in stages where the infants or especially young children have brains in overdrive taking in everything followed by stages that are more selective about what they take in and solidify. Training AI's to be smart for real, common sense and all, might take over a decade of data for the first one unless this problem can be decomposed. Still will take years of similar experiences.

computerex 9y ago
Modern AI, things like AlphaGo are examples of applied AI. "Common sense" falls within the realm of artificial general intelligence, which is a line of research that's largely abandoned now in favor of applied AI. Modern AI solutions are engineered to solve very very specific problems. You are never going to see attempts to teach "common sense". https://arxiv.org/abs/1705.08807 https://arxiv.org/abs/1705.08807 With that said, the above is what the world's AI researchers think is possible hopefully within my lifetime using just applied AI without the notion of "common sense". Common sense is AGI. That's not the goal anymore. The goal is to do things like self driving cars. Both Google and Tesla have placed vehicles on the road that have driven for literally millions of miles. The idea is to build a bunch of classifiers and regression models and use them together in an ensemble to solve your problem. The same approach is being applied successfully to a lot of unrelated fields where deep learning is concerned. Also, modern AI doesn't even pretend to be biological in nature, in fact we'll known researchers like Andrew Ng make a point in saying that they are only biologically inspires and that's where the commonalities end. There are other models like HTM that are way more ambitious and want to come up with a single generalized scheme to solve a broad range of problems, AGI style. These guys think biology is important and are trying to emulate the neocortex. They ARE going for AGI, common sense, etc.