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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

#81

Post by eli_gottlieb »

0xfffafaCrash wrote: post #33
payne92 wrote: post #17

This article is off in so many dimensions. Fundamental research is not less active, but it's happening in different places (e.g. the Google Brain team). Find the most profitable companies and you'll find the research. And to suggest a computer Go player, taught in a few days, is a "marginal improvement" over decades of "AI research": as my kids say, "wut?" If anything, today's deep learning driven AI is a prime examp…

The articles wasn't saying that AlphaGo Zero was a marginal improvement. On the contrary, I think it was saying that the type of improvement AlphaGo offered is an outlier because it approached the task in a fundamentally different way (especially with AlphaGo Zero where it didn't use training data). I think the primary argument is that much of the industry has generally not been doing that sort of research for ground…

>I've seen this same line of criticism before of the way in which AI systems have been generally designed for decades and to me it rings true. Things like the Turing award arguably lead researchers astray. Essentially, the crux of the argument is that most people focus on climbing trees to achieve success when the real goal is reaching the moon or they build better springs when the real goal is achieving flight. [1] Both show some short-term improvements on certain heuristics, but they are obviously the wrong approach if you want serious breakthroughs.

Basically, we incentivize researchers to work on the most near-term solvable problems, rather than the most difficult problems where we understand how to check for a solution -- let alone to work on developing the solution-properties we can check, to get past wholesale conceptual confusions.

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

#82

Post by ionforce »

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…

Playing devil's advocate. I casually agree that AlphaGo Zero was valuable, but if we were to put the onus on you...

What theoretical innovations did AlphaGo Zero provide?

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

#83

Post by ionforce »

lustig wrote: post #44
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…

I thought all neural networks had layers. Is this not the case?

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

#84

Post by dsacco »

ionforce wrote: post #82
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…

Playing devil's advocate. I casually agree that AlphaGo Zero was valuable, but if we were to put the onus on you... What theoretical innovations did AlphaGo Zero provide?

I gave a brief overview of that in a parallel comment on this thread :)

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

#85

Post by LrnByTeach »

ThomPete wrote: post #75
topmonk wrote: post #72

Earlier quoted context omitted.

When I think about UBI, I keep picturing a group of pidgeons fighting over pieces of bread. I don't see UBI as freedom. It shares a lot with slavery, in that someone else is feeding you, and therefore has control over you. What I hope for in the future is a fully independent machine that each person owns and is capable of caring for them, by providing food, shelter, etc. and is capable of building a clone of itself.…

You are looking at this from the wrong perspective. You are assuming there is someone (other humans) who are feeding you. The point of UBI is that it's built on a realization that technology itself is feeding you. In the end, there isn't going to be any single owners because everything is better solved by the technology we are all going to be owning the means of production so to speak. The post-scarcity society is wh…

Wow!!! one of the best explanations Type 1 Civilization (https://en.wikipedia.org/wiki/Kardashev_scale ) where technological advances results in "The post-scarcity society is where all basic needs are met"

and UBI is one form of Manifestation of 'Type 1 Civilization

> The post-scarcity society is where all basic needs are met.

> The point of UBI is that it's built on a realization that technology itself is feeding you.

> If each person has their own machine you are kind of back to the same problem you have now. Who gets to use what resources?

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

#86

Post by dsacco »

eli_gottlieb wrote: post #80
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…

The basic problem with AlphaGo Zero is that the state of a Go game is fully deterministic, fully Markovian, and fully amenable to quick simulation. The player makes a move, and the simulator computes the next game-state in milliseconds from only the current game-state. This is what lets the AlphaGo Zero agent train so quickly on self-play. If you start requiring high-dimensional empirical data where the generating dy…

I agree that partial observation and imperfect information present computational difficulties to generalization. Do you know of any interesting research offhand for reading about optimizations for this problem?

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

#87

Post by pps43 »

marcosdumay wrote: post #59
lustig wrote: post #44

Earlier quoted context omitted.

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…

> it's basically about creating way more complex models than those used in previous research and feeding them lots of data Those are instructions for over-fitting. Deep learning neural networks escape from this problem somehow, but it's not a given that other models would escape it too.

The traditional way to avoid overfitting is to reduce the number of independent variables, shrink coefficients towards zero, or otherwise limit the complexity of the model.

With deep neural networks the approach is different. Instead of trying to find global maximum (which is too hard, and will also cause the model to be grossly overfit), the algorithm stops much earlier. Such "underfit" models seem to generalize much better.

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

#88

Post by defertoreptar »

makomk wrote: post #22

The big question this fails to ask is: full employment of what kind? Recent research has found that nearly all of the net job growth is in "alternative work", meaning temporary jobs, contract workers, freelancers, etc: https://qz.com/851066/almost-all-the-10-million-jobs-created... Now think about the kind of innovation that companies like Uber and Deliveroo represent. While ostensibly they might be tech companies, i…

>Their innovation is primarily in making more people work for less and take on all the risk.

Work for less than... what? Their side gig had Uber not existed? I see these companies' primary innovation to be that of communication. They allow supply to connect and communicate with demand in much more efficient way than ever before.

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

#89

Post by eru »

nine_k wrote: post #47
watwut wrote: post #19

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?" 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.

Yep. Lots of eg cancer research charities.

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

#90

Post by bluesnowmonkey »

ThomPete wrote: post #75
topmonk wrote: post #72

Earlier quoted context omitted.

When I think about UBI, I keep picturing a group of pidgeons fighting over pieces of bread. I don't see UBI as freedom. It shares a lot with slavery, in that someone else is feeding you, and therefore has control over you. What I hope for in the future is a fully independent machine that each person owns and is capable of caring for them, by providing food, shelter, etc. and is capable of building a clone of itself.…

You are looking at this from the wrong perspective. You are assuming there is someone (other humans) who are feeding you. The point of UBI is that it's built on a realization that technology itself is feeding you. In the end, there isn't going to be any single owners because everything is better solved by the technology we are all going to be owning the means of production so to speak. The post-scarcity society is wh…

Why should technology continue to feed you though? Maybe it's more efficient to let you starve. This isn't a theoretical, what-would-a-currently-uninvented-AI-decide-to-do kind of question. Just look at what corporations do now. It's all about dollars and cents. Non-human entities don't care about what's right for humans. Even ones that are mostly composed of humans.
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