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What AlphaGo Zero teaches us about what’s going wrong with innovation

timharford.com

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Re: What AlphaGo Zero teaches us about what’s going wrong with innovation

#41

Post by JustFinishedBSG »

dsacco wrote: post #38
GuB-42 wrote: post #35

Projects like DeepBlue and AlphaGo are not fundamental innovation nor research, they are just PR stunts that show the expertise of the company making them. TBH, winning a game of chess or go has little value in itself, except for the limited market of selling chess or go software. The reason they are doing that is mostly for publicity. IBM makes computers, and they show how good they are at it by having one beat top…

While I understand your cynicism in the practical applicability of a chess or go-playing AI, I think you are significantly underestimating the theoretical innovations contributed to the field every time these models are substantially improved. Much of the work that goes into improving something like AlphaGo is cross-applicable and cross-pollinated to other research projects, and gradually trickles out into other doma…

> I think you are significantly underestimating the theoretical innovations contributed to the field every time these models are substantially improved.

I think you are overestimating, there isn't a single interesting theoretical insight in AlphaGo's papers.

Re: What AlphaGo Zero teaches us about what’s going wrong with innovation

#42

Post by nine_k »

wallflower wrote: post #34
d--b wrote: post #5

Mmmh, I am not a specialist and I don't know the numbers, but it seems to me that fundamental research is not less active than it used to be. Physics made a lot of progress in materials (nano tech, weird polymers, and so on), in building batteries, in finding the higgs boson and gravitational waves, and I'm sure plenty of other fields. Medical research has advanced a lot with the invention of CRISPR. CS has grown a l…

If you read the science fiction series "The Three-Body Problem" (highly recommended), it makes a very compelling argument that fundamental research is the most important investment in the future. For example, fusion drives, not traditional stored rocket propellant engines, will be necessary to navigate between planets and the outer solar system. Also, existing known behaviors/laws of physics aside, the book posits th…

The mother planet reaps no material benefits from colonizing another star. Those groups who colonize it get a world if their own.

So interests of Earth governments are not aligned with star travel, and only marginally aligned with colonizing e.g. Mars. Those groups who want to ho there will have to do it themselves. (See Elon Musk.)

Re: What AlphaGo Zero teaches us about what’s going wrong with innovation

#43

Post by choonway »

l3robot wrote: post #40

"It was a time when companies weren’t afraid to invest in basic science." No they were probably afraid, but they were forced to invest in science by states. AT&T did not decide to invest massively in science and risky projects like Unix, they were forced to. Please stop thinking companies are behind innovation. A great piece of article that demestify this myth: https://www.theguardian.com/technology/2017/may/11/tech-…

The push came most likely from the pressures of the cold war. Now that it is long over, threats like Russia/Syria/ISIS/North Korea/China don't provide the same level of urgency to compete as before.

Re: What AlphaGo Zero teaches us about what’s going wrong with innovation

#44

Post by lustig »

nottorp wrote: post #37

I'm not current, so please enlighten me: Is "deep learning" just a new buzzword for neural networks, or is there something extra?

Technically yes, most often it's about stacking more layers in neural networks, making them "deep". However, there is some merit to the new hype since stacking more layers worked way better than anyone previously working with neural networks and ML thought it would. But in theory you could generalize deep learning to other methods than neural networks, it's basically about creating way more complex models than those used in previous research and feeding them lots of data. Thereby assuming less about the problem and letting the model figure it out.

Re: What AlphaGo Zero teaches us about what’s going wrong with innovation

#45

Post by mda »

GuB-42 wrote: post #35

Projects like DeepBlue and AlphaGo are not fundamental innovation nor research, they are just PR stunts that show the expertise of the company making them. TBH, winning a game of chess or go has little value in itself, except for the limited market of selling chess or go software. The reason they are doing that is mostly for publicity. IBM makes computers, and they show how good they are at it by having one beat top…

Funny that up until 2016 go was regarded as one of the most difficult games that computers could master, and now that it is solved it becomes a PR stunt? Would you claim the same in 2015?

Re: What AlphaGo Zero teaches us about what’s going wrong with innovation

#46

Post by mirashii »

GuB-42 wrote: post #35

Projects like DeepBlue and AlphaGo are not fundamental innovation nor research, they are just PR stunts that show the expertise of the company making them. TBH, winning a game of chess or go has little value in itself, except for the limited market of selling chess or go software. The reason they are doing that is mostly for publicity. IBM makes computers, and they show how good they are at it by having one beat top…

> Chess and go don't drive innovation, they are just a side effect of real innovation.

I'm not certain this is true. Take OpenAI as a potential counterexample. While not chess or go (at least, at the moment), they are likely to be considered innovating while still working on problems that would be considered roughly equivalent. Much of the field that has been called AI for a long time (not the current deep learning approaches) were pioneered by working on chess and go. It may well be the case that proving that a new class of techniques work on these well studied games is the first step in the innovation process, where those techniques are taken and applied to other problems.

Re: What AlphaGo Zero teaches us about what’s going wrong with innovation

#47

Post by nine_k »

watwut wrote: post #19
eru wrote: post #10

Earlier quoted context omitted.

I think it's relatively easy to set up a charity to do your basic research for you, and reap the tax benefits? (I'm not an American, and I assume you are talking about American tax system? Other countries are different.) Universities are notorious for spinning their basic research into good PR (and often overblown press releases), even if it would only be interesting to specialists normally. I think companies like Go…

"I think it's relatively easy to set up a charity to do your basic research for you, and reap the tax benefits?" That would likely qualify as tax fraud.

This depends on what you do with the results. To qualify as a charity, such an org should probably make all the results public domain, instead of e.g. patenting them.

Re: What AlphaGo Zero teaches us about what’s going wrong with innovation

#48

Post by alenmilk »

dagw wrote: post #20
dingo_bat wrote: post #16

I don't think there is a very sharp distinction between results oriented R&D and "basic research". In the article, IBM's deep blue is dismissed as a dead-end victory but apparently alphago is not? Why? They both seem identical to me in goals and research methodology. On a side note, I cannot wait for general super intelligence. It cannot come soon enough. I'm tired of being poor and stuck in a fucking rut, and contem…

Why? They both seem identical to me in goals and research methodology. In theory taking the work done on AlphaGo (and more importantly AlphaGo Zero) and generalizing it to non-Go related problems should be a lot easier than taking the work done on deep blue and generalizing to non chess related problem.

Yes, AlpaGo Zero is mainly self taught. It means it learned to play through the game mechanics. There are no databases of moves or smart optimizations that are based on our understanding of go.

Re: What AlphaGo Zero teaches us about what’s going wrong with innovation

#49

Post by [deleted] »

JustFinishedBSG wrote: post #41
dsacco wrote: post #38

Earlier quoted context omitted.

While I understand your cynicism in the practical applicability of a chess or go-playing AI, I think you are significantly underestimating the theoretical innovations contributed to the field every time these models are substantially improved. Much of the work that goes into improving something like AlphaGo is cross-applicable and cross-pollinated to other research projects, and gradually trickles out into other doma…

> I think you are significantly underestimating the theoretical innovations contributed to the field every time these models are substantially improved. I think you are overestimating, there isn't a single interesting theoretical insight in AlphaGo's papers.

[deleted]

Re: What AlphaGo Zero teaches us about what’s going wrong with innovation

#50

Post by dsacco »

JustFinishedBSG wrote: post #41
dsacco wrote: post #38

Earlier quoted context omitted.

While I understand your cynicism in the practical applicability of a chess or go-playing AI, I think you are significantly underestimating the theoretical innovations contributed to the field every time these models are substantially improved. Much of the work that goes into improving something like AlphaGo is cross-applicable and cross-pollinated to other research projects, and gradually trickles out into other doma…

> I think you are significantly underestimating the theoretical innovations contributed to the field every time these models are substantially improved. I think you are overestimating, there isn't a single interesting theoretical insight in AlphaGo's papers.

Can you define what you mean by “theoretical insight”? It’s true that AlphaGo was built using previously existing techniques (supervised learning, large dataset for training, reinforcement learning and monte carlo tree search). But if you consider something to not be a breakthrough because it does not literally introduce a novel fundamental technique, you have a very narrow view of research (in my opinion).

Here are a few points to consider:

1. The combination of the aforementioned techniques in AlphaGo was non-standard. Reinforcement learning bootstrapped supervised learning, before passing a value function to the monte carlo tree search.

2. AlphaGo represents a new achievement in solving perfect information games. The research team has moved on to Starcraft, which is not perfect information, but they didn’t try to tackle that before conquering a complex perfect knowledge game first.

3. AlphaGo’s research team improved upon the original AlphaGo with a novel algorithm for self-learning and mastering games using minimal policy improvement. The new AlphaGo Zero does not utilize human training data or supervised learning, and it was capable of defeating the original AlphaGo 100-0.

Beyond self-play, I think that AlphaGo’s methodologies can generalize to combinatorial search problems even if they don’t generalize to broader domains like partially observed games or robotics.

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