AlphaGo's next move

Whereas in Go, everyone's playing the same game by the same rules, the machines are just way better at it.

The rationale for why a buffer would be good is described by Demis Hassabis here: https://youtu.be/h0962biiZa4?t=11m24s

...the main points are: there may be safety considerations along the way that are costly. More "capitalistic" organizations may decide to shortcut those costs because of the winner-take-all scenario. DeepMind is at least nominally very committed to safety.

Releasing AlphaGo's source code would probably reduce DeepMind's buffer, which in theory, would also reduce safety.

Continuous practice involves putting in the hours at practicing an art or a science, which by itself builds muscle memory about the language syntax/equations etc. It is unlikely that one can remember one but not the other.

Sure, why not? The nice thing about this field is that you don't need a lot of specialized and expensive equipment to work. The biggest obstacle in that regard comes in if you're doing some kind of model training where GPU's are the best choice, and you need (a) super-fast GPU(s) to do model training in a reasonable period of time. So you might want to spend a few thousand dollars on a fairly nice GPU setup.

But wait... even that can be outsourced to "the cloud" given that AWS, GCP, etc. make GPU instances available on an on-demand basis. Yeah, you have to be careful of how much cloud spend you rack up, but the point is that you don't necessarily need a huge up-front investment.

Even beyond that, AWS make FPGA instances available, so if you think you can design your own hardware level logic for doing something more efficiently, you can try that out in the cloud.

And GPU's aside, depending on exactly what you're doing (remember, AI is more than just Deep Learning) maybe you can get by with a basic Spark cluster or Beowulf cluster running MPI. Again, you can do this in your home for pretty cheap, or do it in the cloud.

As for the knowledge / know-how... sure, you'd have to dig in and do some serious catching up (that's the phase I'm in now). But the nice thing is, so much of the output of this field is online and freely available. No, not everything is, but a ton of the important stuff shows up on arXiv.org, or in free journals like JMLR or JAIR. There's also tons of historical stuff available to help get context or to mine for ideas that were prematurely abandoned, etc. Look at the CSAIL archives, or the IJCAI archives. Also, a lot, if not all, of the NIPS papers are freely available. Same for ICML and some others. See:

http://proceedings.mlr.press/

http://jmlr.org/

http://jair.org

http://nips.cc

http://publications.csail.mit.edu/ai/pubs_browse.shtml

https://www.ijcai.org/past_proceedings

https://arxiv.org/list/cs.AI/recent

http://arxiv.org/list/cs.LG/recent

http://arxiv.org/list/cs.MA/recent

http://arxiv.org/list/cs.NE/recent

etc.

Also consider that a significant portion of the important software used in this field is open source and freely available. I won't even try to list the stuff that's out there, but would instead direct your attention to http://mloss.org or Wikipedia (or Google) for some options to explore.

And of course there are forums where you can seek assistance from others, including:

https://discuss.openai.com/

http://stats.stackexchange.com

http://ai.stackexchange.com

http://datascience.stackexchange.com

http://artificial.reddit.com

http://machinelearning.reddit.com

etc.

The other thing that comes up is the need to know some maths stuff. Luckily the level of maths typically used in this field isn't that bad. You're not typically looking at needing Real Analysis, Abstract Algebra, Galois Theory, Topology, etc. A lot of AI/ML can be understood (from a mathematical POV) with just Calculus and Linear Algebra.

And if you don't already know those subjects, there are tons of online resources to help one learn them.

An interesting thing about AI/ML is that it's a very empirical subject. Not that there is no theory, but by and large you can come up with an idea for an approach to cognition / pattern matching that you think might work, and just go implement it, test it against existing approaches, and know if you've accomplished something.

Note that I'm not saying any of this is easy. Just that I think it's possible for somebody who's really motivated.

All the traditional martial arts went through that kind of identity crises. I have a friend who was ex military, practiced xingyi with me and Aikido; he tells me, in this day and age of drones, the idea that you are a walking badass because you know king fu is absurd. If that is the case, why keep going? My personal answer is that it has become about prevailing over myself (not about defeating others) ... and because it is fun.

The reactions among the Go world seems varied. Fan Hui said he learned a lot, and his own workd ranking shot up. After the first match, Ke Jie said he felt bitter, that these are going to be the last games he play against an AI. Yet he agreed to help Deepmind develop an analysis tool. Michael Redmond (one of the commentators) seems really excited in both the Lee Sedol matches and Ke Jie matches. A lot of Go pro players seems to feel more excited, not less, that AlphaGo is opening up possibilities. That the game has even furthur depths to explore.

For myself, I know I will never reach anywhere near the pro dan ranks in Go; I am not even sure I will reach the amature dan ranks. The journey to get where I am at, playing very informally, has helped me a lot with many other parts of my life. One of the biggest influences was practicing life-or-death and learning how my emotions affect my play. When I first started, I used to play moves because it felt safe, not because it was safe. Or I would respond locally because not doing so felt insecure. Reading through those positions helped me to not only read through what is there and the read my arising emotions at the time, that started to seep in other parts of my life. For example, my software programming style started changing. Lessons learned from making good shapes, capture races, living by making two eyes, false eyes -- all basic double-digit kyu stuff -- found their way into how I structure code, how to code less defensively, and a sharpening of my mental focus to read code that is actually there, not just what I think is there.

That change in myself is, I think, a better fruit of my effort.