jfv

An all-female recruitment cycle could also be called a no-male recruitment cycle, but I suppose that wouldn't sound good.

Direction from the top of the company followed by accountability from everyone will probably move toward any desired outcome, but I contend that the main issue is that most companies secretly don't believe that "percentage of female engineers" is a goal worth pursuing to the exclusion of hiring as much top talent as possible.

My guess on this experiment is that the company didn't hire as much talent as they could have otherwise because of such strict quota measures, and that this isn't something most companies are going to jump to do. Most companies just want to succeed.

I've heard a similar argument before but I'm skeptical. As a science-trained person with only a little exposure to philosophy, while philosophy uses rational arguments, etc., it seems like it's all too easy to bullshit your way out of.

My question: what does graduating with a degree in philosophy say about one's abilities, and how can you show it? (I would argue the types of problems that an upper-level science/math/CS person is expected to solve very clearly shows analytical and creative ability to anyone with a little knowledge of the subject)

My problem is that what people seem to be saying now is that "computers are smart enough to solve Go now, thanks to Google".

1) Too much credit is given to Google for this result. What I see as already a huge brain drain on society is only going to get bigger.

2) People are going to expect that if "computers are smart enough to play Go", they're smart enough to do ____. What goes in the blank? Very little right now, but I guarantee you investors and the public have a lot of ideas and think it's around the corner.

Hopefully a lot of good things will come out of this wave of AI, but who knows what or when. I think the point is that there may be a panic and contraction before anything really awesome happens, and a lot of that is going to be because of "stunts" (for lack of a better word) like this being overhyped.

So is image recognition, but I think the main contention is that solving Go required little additional cleverness to what has already existed. The ideas of self-play, Monte Carto methods, and neural networks are not new and not novel for the problem. Not that it's so trivial -- I'm sure it took a while to work out the exact architecture and details, but what of that work actually teaches you something?