[wiz21c]

The gift Google makes to the community (some games AlphaGo,played ) is nothing. The super tricky thing with neural networks is that you can't reverse engineer them. Once the information is coded into the parameters, you can't base anything useful on them. So it's a super good intellectual property protection... Therefore one more nail in the coffin of knowledge sharing as we know it...

[andrew3726]

Honestly I think you are missing the point. The gift DeepMind made with the published self-play matches is not really aimed at the machine learning but rather to the Go community. And over at /r/baduk they are appreciating it, as it will allow them to gain further insights into the gameplay. So this is definitely _not_ 'nothing'.

[wiz21c]

what I meant (which was not clear) is that, for what I understand, if you compare the greatness of the tool which is AlphaGo and the "gift" of Google, then I'm inclined to thing that Google actually gives almost nothing. Now I understand that they don't have to (a great Go players doesn't have to teach for sure) but I can not thing their gift is meaningful. Basically, if I have 1000$, I'll share 1$. But if I have 1000000$, then...

[baq]

Once they release the new architecture paper it'll be a matter of time to have not only games, but also a working ai of a similar strength.

[Sir_Cmpwn]

The gift is in studying the novel strategies AlphaGo uses and applying them to your own play style.

[yeukhon]

Most importanly, it is almost inevitable they must run on a fam of computers basically mean it becomes a service. Can we ever create a robot who can self-learn but with the super brain power locally without having to call a service for an answer?

[skybrian]

TPU's are available for rent. Also, apparently AlphaGo became 10x more efficient in a year. Who's to say other Go engines won't continue improving efficiency?

Between software and hardware improvements I think it's likely that we'll see very strong Go engines on desktop computers in a few years.

[wiz21c]

This raises a question for me : is there currently some public infrastructure that can rival with AlphaGo ?

[yeukhon]

Public means API? AlphaGo is very specialized in solving Go game. Google, Amazon and IBM have services for various services like image recongition and speech recongition. Startups like Clarifi also exists in that space.

The closest to a generalized AI service would probably be Watson from IBM (but I don't have experiment with it sadly so I am not sure about the usage experience).

[wiz21c]

Public in the sense of non proprietary, in this case most likely universities.

My question was more : Google probably based its tools on existing tools, and those most likely come from universities (research paper, computer infrastructures, etc.). So what are those tools, where are they ?

[Choco31415]

Google originally used Torch7 as their ML library, but shifted to use TensorFlow in April 2016. TensorFlow is written by Google itself.

AlphaGo itself uses a method that combines Monte Carlo tree search with value and policy NN. All NN used are concurrent NN. The specifics are in a paper by David Silver et al: https://dvc0t0mx8dl84.cloudfront.net/wp-content/uploads/2016...

[nl]

> The closest to a generalized AI service would probably be Watson from IBM

Ha ha ha!

[doktrin]

>> The closest to a generalized AI service would probably be Watson from IBM

> Ha ha ha!

can you expand on that for those of us who don't know much about the field?

[yeukhon]

That's not very nice.

[nl]

Sorry, I honestly thought you were joking.

'Watson' as it is sold today is a mishmash of random, separate services. You can get the same from Google, MS or multiple other places.

'Watson' the jeopardy winning thing was an ensemble of search and rule-based NLP techniques.

Neither are much like general AI.