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Deep Mind continues the venerable tradition of defining their own frontiers of AI research based on what will sound good in a PR release when they inevitably ac

Deep Mind continues the venerable tradition of defining their own frontiers of AI research based on what will sound good in a PR release when they inevitably achieve superhuman performance by modests means of a single datacenter stuffed with TPUS and $10K video cards. I guess we should just accept that in the society of the future robots will play board/computers games and draw paintings while humans will all be slavering on integration of endless badly written web services.

bsaul 8y ago
That would make for a good novel :))) we’ve always expected superhuman AIs to free us from work, but maybe we’ll be the ones working while they’ll have all the fun. Damn, i wish general AI never acquires personnal taste !
pure-awesome 8y ago
Before you can solve general and difficult problems, it is often useful to solve toy versions of the same problems with many of the parameters simplified, or only focusing on individual aspects of the full problem at a time. It would be incredibly difficult to attempt to solve the general problem of having an AI making decisions in the world, including taking into account the goals and mind-states of other humans (and general agents). Much more practical to restrict the problem space very narrowly - can you build something which can learn to strategize and predict future outcomes in a difficult, full-information competitive game? (Go) Okay, can it do something similar without complete information, and with less discrete-looking steps and game states (1v1 Dota) - can it do so whilst cooperating with other similar agents with asymmetric abilities but common knowledge to achieve a common goal? (5v5 Dota) Okay, now can it cooperate with other agents with symmetric abilities but asymmetric knowledge, and both convey necessary knowledge to other agents via its actions, as well as deduce knowledge from the actions of other agents? (Hanobi) These are all necessary steps on the path to creating a general AI - if your AI is not at least able to do the above, it definitely won't be able to navigate a general environment. In the absence of an obvious alternative strategy, it seems to me the most productive approach is to tackle the small, important-but-doable seeming problems, one at a time, until hopefully you are able to unify them or else have a breakthrough of some sort.
leesec 8y ago
This is so cynical it is laughable. They are using games to develop algorithms as they are easily defined problems, but they have in turn used those algorithms on real world problems, notably AlphaFold and their data center improvements [1]. I'm sorry breaking world records every year isn't quite impressive enough for you. Furthermore, their investment into TPU's is going to bring the price down in the long run, which is incredibly beneficial to everyone. And yes we should assume if AGI is possible it will require a lot of compute. [1] https://deepmind.com/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-40/ https://deepmind.com/blog/deepmind-ai-reduces-google-data-ce...
- gambler 8y ago
  I am cynical for a reason. >They are using games to develop algorithms as they are easily defined problems I believe they use games because it makes good PR. I believed that before they chose StarCraft, but now I am more convinced in it. Also, because their algorithms are extremely data-hungry and it's easy to generate tons of training data for a game simply by running it. And because it is possible to evaluate performance of an algorithm over and over again. >they have in turn used those algorithms on real world problems, notably AlphaFold AlphaFold does not use their board game AI[1]. Naming it similarly is just a marketing tactic. AlphaFold might be a genuinely useful application of their resources, in which case they deserve praise in credit for it. I have to do more research on it, which is made very hard by all the hyperbole and hype around their work. >Furthermore, their investment into TPU's is going to bring the price down in the long run Overall, they do the exact opposite of democratizing AI. They invest heavily into insanely data-hungry and hardware-hungry approaches that a normal company will not be able to use (directly, not through Google's cloud services) for decades if ever. > And yes we should assume if AGI is possible it will require a lot of compute. What you're saying here (overall) does not make sense. There is absolutely no reason to assume that breaking records in games without substantial improvements in AI theory makes us any closer to "AGI". Ignoring the completely unsubstantiated statements about AGI, we are left with the question of practical applications. I am not convinced that the societal benefits of investing in complex and computationally intensive machine learning is higher than investing in improvements of simpler algorithms. Most practical applications of AI outside of a few ubercorps involve simpler, older algorithms. Even someone as big as SAP relies mostly on decision trees and SVMs. If anything, the hype around deep learning likely means it's harder to get funding for old-school AI research that is far likely to benefit smaller companies. --- [1] http://predictioncenter.org/casp13/doc/CASP13_Abstracts.pdf http://predictioncenter.org/casp13/doc/CASP13_Abstracts.pdf Page 11, A7D.

bsaul 8y ago

That would make for a good novel :))) we’ve always expected superhuman AIs to free us from work, but maybe we’ll be the ones working while they’ll have all the fun. Damn, i wish general AI never acquires personnal taste !

pure-awesome 8y ago

Before you can solve general and difficult problems, it is often useful to solve toy versions of the same problems with many of the parameters simplified, or only focusing on individual aspects of the full problem at a time. It would be incredibly difficult to attempt to solve the general problem of having an AI making decisions in the world, including taking into account the goals and mind-states of other humans (and general agents). Much more practical to restrict the problem space very narrowly - can you build something which can learn to strategize and predict future outcomes in a difficult, full-information competitive game? (Go) Okay, can it do something similar without complete information, and with less discrete-looking steps and game states (1v1 Dota) - can it do so whilst cooperating with other similar agents with asymmetric abilities but common knowledge to achieve a common goal? (5v5 Dota) Okay, now can it cooperate with other agents with symmetric abilities but asymmetric knowledge, and both convey necessary knowledge to other agents via its actions, as well as deduce knowledge from the actions of other agents? (Hanobi) These are all necessary steps on the path to creating a general AI - if your AI is not at least able to do the above, it definitely won't be able to navigate a general environment. In the absence of an obvious alternative strategy, it seems to me the most productive approach is to tackle the small, important-but-doable seeming problems, one at a time, until hopefully you are able to unify them or else have a breakthrough of some sort.

leesec 8y ago

This is so cynical it is laughable. They are using games to develop algorithms as they are easily defined problems, but they have in turn used those algorithms on real world problems, notably AlphaFold and their data center improvements [1]. I'm sorry breaking world records every year isn't quite impressive enough for you. Furthermore, their investment into TPU's is going to bring the price down in the long run, which is incredibly beneficial to everyone. And yes we should assume if AGI is possible it will require a lot of compute. [1] https://deepmind.com/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-40/ https://deepmind.com/blog/deepmind-ai-reduces-google-data-ce...

gambler 8y ago
I am cynical for a reason. >They are using games to develop algorithms as they are easily defined problems I believe they use games because it makes good PR. I believed that before they chose StarCraft, but now I am more convinced in it. Also, because their algorithms are extremely data-hungry and it's easy to generate tons of training data for a game simply by running it. And because it is possible to evaluate performance of an algorithm over and over again. >they have in turn used those algorithms on real world problems, notably AlphaFold AlphaFold does not use their board game AI[1]. Naming it similarly is just a marketing tactic. AlphaFold might be a genuinely useful application of their resources, in which case they deserve praise in credit for it. I have to do more research on it, which is made very hard by all the hyperbole and hype around their work. >Furthermore, their investment into TPU's is going to bring the price down in the long run Overall, they do the exact opposite of democratizing AI. They invest heavily into insanely data-hungry and hardware-hungry approaches that a normal company will not be able to use (directly, not through Google's cloud services) for decades if ever. > And yes we should assume if AGI is possible it will require a lot of compute. What you're saying here (overall) does not make sense. There is absolutely no reason to assume that breaking records in games without substantial improvements in AI theory makes us any closer to "AGI". Ignoring the completely unsubstantiated statements about AGI, we are left with the question of practical applications. I am not convinced that the societal benefits of investing in complex and computationally intensive machine learning is higher than investing in improvements of simpler algorithms. Most practical applications of AI outside of a few ubercorps involve simpler, older algorithms. Even someone as big as SAP relies mostly on decision trees and SVMs. If anything, the hype around deep learning likely means it's harder to get funding for old-school AI research that is far likely to benefit smaller companies. --- [1] http://predictioncenter.org/casp13/doc/CASP13_Abstracts.pdf http://predictioncenter.org/casp13/doc/CASP13_Abstracts.pdf Page 11, A7D.