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It may not be "the solution", but no one in Deep Mind said it was. It doesn't particularly matter how journalists spin this stuff, as they clearly don't underst

It may not be "the solution", but no one in Deep Mind said it was. It doesn't particularly matter how journalists spin this stuff, as they clearly don't understand it anyway. By the way, if Monte Carlo search worked, then we would have seen MC beating 9-dan pro's long ago, which clearly didn't happen, so the author's definition of "effective" lacks pragmatic insight - the real limiting factor in this sort of evaluative judgment. The author doesn't seem to have watched any of the matches of AlphaGo vs Lee Sedol, though, as much of the pre-game and post-game discussion brings to light some important details surrounding specific implementation features in AlphaGo by Deep Mind. Glossing over that makes this article remarkably uncritical and uninformative. Edit: it's one thing to combat hype, but representing the facts as they are in contradistinction to that hype is another matter entirely. Anti-hype is perhaps more pernicious in bringing about an "AI Winter" than is drumming up interest in what AI, or more accurately AGI, will be capable of in the not-too-distant future. And given current developments, I think the only AI Winter we have to fear is the one where uninformed persons accost us with misguided fears about AI and what its capabilities will be.

ktRolster 11y ago
It doesn't particularly matter how journalists spin this stuff, as they clearly don't understand it anyway. It matters because it could lead to another AI winter when the hype falls through.
- tim333 11y ago
  I don't think there will be another winter in a hurry regardless of press hype because there are too many real results - Amazon echo, self driving cars and so on.
  - colordrops 11y ago
    Voice recognition and self-driving cars aren't solved problems. That last 10% may be 90% of the work.
    - jheriko 11y ago
      I would agree and disagree... within constraints both are very well solved... and that's what the press tends to overstate
    - davnn 11y ago
      Really hard to tell when something is solved, because it's always going to get improved. As Elon puts it with self driving cars: They must be more secure than human level until we can expect broad acceptance. However both technologies are already useful as of now.
  - rasz_pl 11y ago
    Previous winter didnt happen only because scientists underestimated difficulty, it happened because they and the corporations couldnt deal with mediocre results and started selling a LIE. NNs recognising sky color instead of tanks, talking car computers, terrible speech recognition that only worked in presentations with microphone up your mouth, whole Smalltalk fiasco. It is happening again, "Dear Aunt, Let's Set So Double The Killer Delete Select All", Honda selling lane following and adaptive cruise control labeled as self driving (so does tesla btw), etc. There are plenty of scam products and misleading marketing surrounding AI right now.
  - romaniv 11y ago
    Are you implying that AI research in the past decades didn't have any "real" results? This is a popular meta-narrative these days, but it's mostly a product of marketing that tries to magnify the significance of current work at the expense of everyone who worked on similar things in the past.
- qrendel 11y ago
  There's not going to be another AI winter. The past AI winters occurred because people drastically underestimated how difficult AI would be. MIT (Seymour Papert specifically) thought computer vision could be solved by some graduate students as a summer project. Same story for other AI problems, e.g. NLP, speech recognition, general reasoning and inference, etc. Once the difficulty of those problems started to be understood, of course funding dried up. Industry is focused on short-term ROI, so it's hard to get funding if the profits won't be seen for 50 years. The difference is that now there's an entire string of profitable markets for solving near-term AI problems. AI is fundamental to the business models of some of the world's largest companies (i.e. Google). There's basically zero risk of an AI winter when we're on the verge of advanced robotics, Watson-style Q&A systems, self-driving cars, large-scale genomics, etc. An AGI winter is another story, but most AI work isn't really focused on serious, full-scale AGI right now anyway. That winter never ended and is ongoing. Everyone is focused on incremental AI improvements because no one really knows what's required for full AGI, and are in the meantime hoping they'll hit on it while building on known techniques like deep learning, comp neurosci, etc. tl;dr: As long as investors continue to see marketable products for new AI developments anywhere in the next ~10 years, funding won't dry up again.
  - comex 11y ago
    AGI Ice Age?
  - efaref 11y ago
    Also, people are in general really bad at understanding how hard some problems are, particularly in relation to how easy we've made some seemingly magical things. Reminds me of this XKCD: http://xkcd.com/1425/ http://xkcd.com/1425/
  - gumby 11y ago
    > There's not going to be another AI winter.[...] > The difference is that now there's an entire string of profitable markets for solving near-term AI problems. As someone who experienced AI winter (and ended up leaving the field) and even attended the AAAI panel where the term was coined on stage, I am not so confident. What happened then looks like it could happen again. At that time there were tons of expert systems being deployed with high promises that they would revolutionize business and practical problem solving. Non-technical people from other fields were flooding in to sell "solutions." Dedicated workstations were proliferating. The self-driving car hype is already at the same level as the expert systems hype was back then. > tl;dr: As long as investors continue to see marketable products for new AI developments anywhere in the next ~10 years, funding won't dry up again. Well sure, that's true, but no different from saying "as long as house prices continue to go up there will be a housing boom."
jheriko 11y ago
Do you work in the field? I agree with the main point about Monte Carlo methods here. Neural networks are a "dumb" way to stack another layer of optimisation on top of many algorithms. I won't lie there is a certain bitterness watching this having used the same principles on less gimmicky problems which I'm sure the author shares
rntz 11y ago
> Anti-hype is perhaps more pernicious in bringing about an "AI Winter" than is drumming up interest in what AI, or more accurately AGI, will be capable of in the not-too-distant future. AI is massively popular in both industry and academia at the moment. It is at no risk of being deinflated by "anti-hype".
- wtbob 11y ago
  > AI is massively popular in both industry and academia at the moment. It is at no risk of being deinflated by "anti-hype". Isn't it precisely when something is massively inflated that it's most at risk of being deflated? Remember those mid-80s films with intelligent computers everywhere? People really thought that AI would be a solved problem within years. Then reality struck and the AI winter fell.
  - rntz 11y ago
    That's a case of deflation due to hype, not due to anti-hype. The whole point of the original article is to avoid overpromising and underdelivering.
yiyus 11y ago
Journalists don't understand almost anything. Still, they are the ones who form the opinion of the general public. So, what they say matters very much. This makes even more important that the people with real authority in some topic correct them when they are wrong, independently of this being the case here or not.
pavelrub 11y ago
Where did the author say that MCTS alone is enough to beat Go? His point was that in Go MCTS seems to significantly improve results (which is true), while for some other games this isn't the case. Therefore an algorithm which strongly relies on MTSC (such as AlphaGo) might not generalise to other games. I don't see anything wrong with this. AlphaGo uses MCTS precisely because it has proved to be effective in Go, at least compared to other search heuristics. It seems perfectly reasonable that a similar approach won't work with other games. Any single component of AlphaGo (like the policy network) can't consistently win against 9p players by itself, but it doesn't follow that those components aren't "effective" or aren't critical to the success of AlphaGo. It fact it is obvious that the opposite is true.

ktRolster 11y ago

It doesn't particularly matter how journalists spin this stuff, as they clearly don't understand it anyway. It matters because it could lead to another AI winter when the hype falls through.

tim333 11y ago
I don't think there will be another winter in a hurry regardless of press hype because there are too many real results - Amazon echo, self driving cars and so on.
- colordrops 11y ago
  Voice recognition and self-driving cars aren't solved problems. That last 10% may be 90% of the work.
  - jheriko 11y ago
    I would agree and disagree... within constraints both are very well solved... and that's what the press tends to overstate
  - davnn 11y ago
    Really hard to tell when something is solved, because it's always going to get improved. As Elon puts it with self driving cars: They must be more secure than human level until we can expect broad acceptance. However both technologies are already useful as of now.
- rasz_pl 11y ago
  Previous winter didnt happen only because scientists underestimated difficulty, it happened because they and the corporations couldnt deal with mediocre results and started selling a LIE. NNs recognising sky color instead of tanks, talking car computers, terrible speech recognition that only worked in presentations with microphone up your mouth, whole Smalltalk fiasco. It is happening again, "Dear Aunt, Let's Set So Double The Killer Delete Select All", Honda selling lane following and adaptive cruise control labeled as self driving (so does tesla btw), etc. There are plenty of scam products and misleading marketing surrounding AI right now.
- romaniv 11y ago
  Are you implying that AI research in the past decades didn't have any "real" results? This is a popular meta-narrative these days, but it's mostly a product of marketing that tries to magnify the significance of current work at the expense of everyone who worked on similar things in the past.
qrendel 11y ago
There's not going to be another AI winter. The past AI winters occurred because people drastically underestimated how difficult AI would be. MIT (Seymour Papert specifically) thought computer vision could be solved by some graduate students as a summer project. Same story for other AI problems, e.g. NLP, speech recognition, general reasoning and inference, etc. Once the difficulty of those problems started to be understood, of course funding dried up. Industry is focused on short-term ROI, so it's hard to get funding if the profits won't be seen for 50 years. The difference is that now there's an entire string of profitable markets for solving near-term AI problems. AI is fundamental to the business models of some of the world's largest companies (i.e. Google). There's basically zero risk of an AI winter when we're on the verge of advanced robotics, Watson-style Q&A systems, self-driving cars, large-scale genomics, etc. An AGI winter is another story, but most AI work isn't really focused on serious, full-scale AGI right now anyway. That winter never ended and is ongoing. Everyone is focused on incremental AI improvements because no one really knows what's required for full AGI, and are in the meantime hoping they'll hit on it while building on known techniques like deep learning, comp neurosci, etc. tl;dr: As long as investors continue to see marketable products for new AI developments anywhere in the next ~10 years, funding won't dry up again.
- comex 11y ago
  AGI Ice Age?
- efaref 11y ago
  Also, people are in general really bad at understanding how hard some problems are, particularly in relation to how easy we've made some seemingly magical things. Reminds me of this XKCD: http://xkcd.com/1425/ http://xkcd.com/1425/
- gumby 11y ago
  > There's not going to be another AI winter.[...] > The difference is that now there's an entire string of profitable markets for solving near-term AI problems. As someone who experienced AI winter (and ended up leaving the field) and even attended the AAAI panel where the term was coined on stage, I am not so confident. What happened then looks like it could happen again. At that time there were tons of expert systems being deployed with high promises that they would revolutionize business and practical problem solving. Non-technical people from other fields were flooding in to sell "solutions." Dedicated workstations were proliferating. The self-driving car hype is already at the same level as the expert systems hype was back then. > tl;dr: As long as investors continue to see marketable products for new AI developments anywhere in the next ~10 years, funding won't dry up again. Well sure, that's true, but no different from saying "as long as house prices continue to go up there will be a housing boom."

jheriko 11y ago

Do you work in the field? I agree with the main point about Monte Carlo methods here. Neural networks are a "dumb" way to stack another layer of optimisation on top of many algorithms. I won't lie there is a certain bitterness watching this having used the same principles on less gimmicky problems which I'm sure the author shares

rntz 11y ago

> Anti-hype is perhaps more pernicious in bringing about an "AI Winter" than is drumming up interest in what AI, or more accurately AGI, will be capable of in the not-too-distant future. AI is massively popular in both industry and academia at the moment. It is at no risk of being deinflated by "anti-hype".

wtbob 11y ago
> AI is massively popular in both industry and academia at the moment. It is at no risk of being deinflated by "anti-hype". Isn't it precisely when something is massively inflated that it's most at risk of being deflated? Remember those mid-80s films with intelligent computers everywhere? People really thought that AI would be a solved problem within years. Then reality struck and the AI winter fell.
- rntz 11y ago
  That's a case of deflation due to hype, not due to anti-hype. The whole point of the original article is to avoid overpromising and underdelivering.

yiyus 11y ago

Journalists don't understand almost anything. Still, they are the ones who form the opinion of the general public. So, what they say matters very much. This makes even more important that the people with real authority in some topic correct them when they are wrong, independently of this being the case here or not.

pavelrub 11y ago

Where did the author say that MCTS alone is enough to beat Go? His point was that in Go MCTS seems to significantly improve results (which is true), while for some other games this isn't the case. Therefore an algorithm which strongly relies on MTSC (such as AlphaGo) might not generalise to other games. I don't see anything wrong with this. AlphaGo uses MCTS precisely because it has proved to be effective in Go, at least compared to other search heuristics. It seems perfectly reasonable that a similar approach won't work with other games. Any single component of AlphaGo (like the policy network) can't consistently win against 9p players by itself, but it doesn't follow that those components aren't "effective" or aren't critical to the success of AlphaGo. It fact it is obvious that the opposite is true.