Yahoo! have* posted their list of key scientific challenges in machine learning. I don't work on and hardly know anything at all about any of these topics. In fact, I think I understand what the question is in only three out of five cases.
Funny. I've always seen myself as working on some sort of machine learning, using computational intelligence methods. But if this is machine learning, I'm certainly not working on machine learning - it's about as related to my work as meteorology or linguistics is. So I should probably not say that I work on machine learning any more than I say that I work on meteorology or linguistics.
I'm actually OK with this, as I can still claim that I'm a computational intelligence researcher. Good enough for me.
But still... who gets to set the agenda? Ten years ago, what I do was machine learning; at least if Tom Mitchell's book is anything to go by. Nowadays, the important "machine learning" conferences such as NIPS and ICML wouldn't even look at the sort of stuff I do, irrespective of its quality. This is mildly annoying, as these conferences somehow have more prestige than CEC, Gecco and PPSN (probably because of ridiculously low acceptance rates).
And, most importantly: how does this semantic drift affect who gets the grant money?
* My intuition is really to write "Yahoo! has posted" here, as Yahoo! is a corporate entity usually referred to as it rather than they. However, British English seems to want to have it otherwise.