Re: [Computer-go] Learning related stuff

On 11/29/2017 6:15 PM, Dave Dyer wrote: My question is this; people have been messing around with neural nets and machine learning for 40 years; what was the breakthrough that made alphago succeed so spectacularly. maybe it was

Re: [Computer-go] Learning related stuff

> My question is this; people have been messing around with neural nets > and machine learning for 40 years; what was the breakthrough that made > alphago succeed so spectacularly. 5 or 6 orders more magnitude CPU power (relative to the late 90s) (*). This means you can try out ideas to see if

Re: [Computer-go] Learning related stuff

My question is this; people have been messing around with neural nets and machine learning for 40 years; what was the breakthrough that made alphago succeed so spectacularly. ___ Computer-go mailing list Computer-go@computer-go.org

Re: [Computer-go] Learning related stuff

It's nearly comic to imagine a player at 1,1 trying to figure things out. It's not a diss on you; I honestly want for people to relax, take a minute, and treat badmouthing the alpha go team's ideas as a secondary consideration. They did good work. Probably arguing about the essentials won't prove

Re: [Computer-go] Learning related stuff

Could you be reading too much into my comment? AlphaGo Zero is an amazing achievement, and I might guess its programmers will succeed in applying their methods to other fields. Nonetheless, I thought it was interesting, and it would appear the programmers did too, that before improving to

Re: [Computer-go] Learning related stuff

This is starting to feel like asking along the lines of, "how can I explain this to myself or improve on what's already been done in a way that will make this whole process work faster on my hardware". It really doesn't look like there are a bunch of obvious shortcuts. That's the whole point of

Re: [Computer-go] Learning related stuff

I imagine implementation determines whether transferred knowledge is helpful. It's like asking whether forgetting is a problem -- it often is, but evidently not for AlphaGo Zero. One crude way to encourage stability is to include an explicit or implicit age parameter that forces the program to

Re: [Computer-go] Learning related stuff

2017-11-21 23:27 UTC+01:00, "Ingo Althöfer" <3-hirn-ver...@gmx.de>: > My understanding is that the AlphaGo hardware is standing > somewhere in London, idle and waitung for new action... > > Ingo. The announcement at https://deepmind.com/blog/applying-machine-learning-mammography/ seems to

Re: [Computer-go] Learning related stuff

Le 21/11/2017 à 23:27, "Ingo Althöfer" a écrit : > Hi Erik, > >> No need for AlphaGo hardware to find out; any >> toy problem will suffice to explore different >> initialization schemes... > I know that. > > My intention with the question is a different one: > I am thinking how humans are

Re: [Computer-go] Learning related stuff

In my experience people who are first taught variant a) and after a short while move on to b) remain overly fixated on capturing and are much slower to grasp the real game. So in this case I would argue that people really do have trouble unlearning when the games are too close … particularly

Re: [Computer-go] Learning related stuff

Hello Stephan, > Another option for your experiment might be to take the 72-hour-old > network, but only retain the first layers, and initialize randomly the > last layers. yes, or many others. Not all of them have to be fantastic, but when you/we get some experience and have a new try every 3

Re: [Computer-go] Learning related stuff

2017-11-22 15:17 UTC+01:00, "Ingo Althöfer" <3-hirn-ver...@gmx.de>: > For instance, with respect to the 72-hour run of AlphaGo Zero > one might start several runs for Go(with komi=5.5), > the first one starting from fresh, the second one from the > 72-hour process after 1 hour, the next one after

Re: [Computer-go] Learning related stuff

Hi Petri,   "Petri Pitkanen" > >>But again: For instance, when a eight year old child starts >>to play violin, is it helpful or not when it had played >>say a trumpet before? >  > It would be and this is well known in practice. Logic > around the music is the same so

Re: [Computer-go] Learning related stuff

Hi Alvaro, Von: "Álvaro Begué" > The term you are looking for is "transfer learning":  > https://en.wikipedia.org/wiki/Transfer_learning   thanks for that interesting hint. However, it is not exactly what I am looking at. My question was more in observing and

Re: [Computer-go] Learning related stuff

>But again: For instance, when a eight year old child starts >to play violin, is it helpful or not when it had played >say a trumpet before? It would be and this is well known in practice. Logic around the music is the same so hw would learn faster. In the very long run there might be no wanted

Re: [Computer-go] Learning related stuff

The term you are looking for is "transfer learning": https://en.wikipedia.org/wiki/Transfer_learning On Tue, Nov 21, 2017 at 5:27 PM, "Ingo Althöfer" <3-hirn-ver...@gmx.de> wrote: > Hi Erik, > > > No need for AlphaGo hardware to find out; any > > toy problem will suffice to explore different >

Re: [Computer-go] Learning related stuff

Hi Darren, > Can I correctly rephrase your question as: if you take a well-trained > komi 7.5 network, then give it komi 5.5 training data, will it adapt > quickly, or would it be faster/better to start over from scratch? (From > the point of view of creating a strong komi 5.5 program.) (?) in

Re: [Computer-go] Learning related stuff

Hi Erik, > No need for AlphaGo hardware to find out; any > toy problem will suffice to explore different > initialization schemes... I know that. My intention with the question is a different one: I am thinking how humans are learning. Is it beneficial to have learnt related - but different

Re: [Computer-go] Learning related stuff

> Would it typically help or disrupt to start > instead with values that are non-random? > What I have in mind concretely: Can I correctly rephrase your question as: if you take a well-trained komi 7.5 network, then give it komi 5.5 training data, will it adapt quickly, or would it be

Re: [Computer-go] Learning related stuff

No need for AlphaGo hardware to find out; any toy problem will suffice to explore different initialization schemes... The main benefit of starting random is to break symmetries (otherwise individual neurons cannot specialize), but there are other approaches that can work even better. Further you