I had to make a few simplifications to spell out the differences clearly and avoid making the text infinitely long. It's true that most current gradient descent algorithms are stochastic because they are computed in batch mode, and that sophisticated evolution strategies approximate the gradient. I still think the differences are significant, in that evolution updates less often and the direction of the update is much less (if at all) dependent on the feedback.
Now, your point about to what extent this is really about neural networks is a good one. Could a network learn F=ma, even if we could not interpret it? Maybe. With the right data, represented the right way.
Good point, I could have used "hacker" instead, though that's more specific to writing software. I didn't perceive "tinkerer" as a word with negative connotations when I wrote it.
Come to think of it, many outside the hacker community would perceive "hacker" more negatively than "tinkerer", because many people still think of hackers as criminal. At least that's my perception. I guess words have different values in different contexts.
Oh yes, I'm a terrible hacker, I rarely write code anymore. An unfortunate byproduct of academia is that you get "promoted out of the job", and all the hands-on work is done by your lab members.
I didn't mean to imply that tinkering was inferior to research - the whole premise was just to tease out how they're different, with different audiences, as you say. Interestingly, the discussion here has been dominated by people who think that I look down on them. People who've discussed it in other fora have not read the post that way.
"a systematic enterprise that builds and organizes knowledge"
That is exactly my point. Doing your scholarship is doing your part to organize knowledge. Systematically building knowledge means building on others, and knowing what you build on.
So the quote (which you don't attribute to anyone other than "wiki" - a citation here would be useful) really proves my point. If you're doing not doing your scholarship, you are not doing science, or research, you are tinkering.
I'm the author, and I'm somewhat amused by the negative tone of many of the comments. Seems there are plenty of people with resentments towards research here ;)
This is all true. However, the large publishers (IEEE, ACM, Springer) these days all allow you to self-archive on your own webpage and typically also on ArXiv. So you can (and should) make your papers freely available. I have done that since the start of my career. It's just a matter of looking.
He mentions NEAT, which is good, but not any of the work on playing Mario, much less any work on evolving neural nets to play Mario. There's quite a few papers on the topic, not just mine.
Note that I don't say that SethBling is wrong. Seen as tinkering, this is perfectly fine, and he did a great job with the video. No hard feelings. I just use it as an example of how it is not research, because of the lack of scholarship.
Like basically all researchers in CS these days, I keep all my papers freely downloadable from my webpage. Easily findable via Google Scholar, or just a regular Google search, for anyone who cares to look.
I am an AI researcher and faculty member at a large and famous university. I probably know less math than that Reddit poster. Math is important if you are specifically interested in the math of AI. If you are interested in inventing algorithms and solutions you mostly don't need the math.
Meta-heuristics as conceptualized by many (most?) does not include selection among algorithms. Hyper-heuristics does. It's fine if some of the algorithms selected among are meta-heuristics. Yes, this is very confusing, and yes, better terminology is needed. In particular, we should probably stop using the term meta-heuristics, because it has too many incompatible meanings.
Glad you liked it! We have done some work on trying to generate complete games - an overview of it can be found in this book chapter:
http://pcgbook.com/wp-content/uploads/chapter06.pdf
The most similar to what you describe is probably the ASP approach to game generation.
The difference between meta-heuristics and hyper-heuristics is that the former is a much broader concept, including such things as genetic algorithms. Hyper-heuristics is specifically about selecting among heuristics, which excludes e.g. evolutionary algorithms. Graham Kendall, one of the inventors of the concept, explains that they would have called it meta-heuristics if that name was not already taken.
The source of data is a lot of simulations we ran ourselves. All of the games can be won or lost - there's also a score measure as well, but that was not used in producing that graphic.
The fitness function is pretty simple, essentially a weighted piece counter. It's true that it does not look ahead and take future states into account so might fall into a trap, but empirically it's doing very well!
Now, your point about to what extent this is really about neural networks is a good one. Could a network learn F=ma, even if we could not interpret it? Maybe. With the right data, represented the right way.