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When I first started doing this kind of thing, there were like two Tory Lawsons on the internet, one of whom was I think actually a Victoria. Now there are lots of us, and I am sorry if you were looking for one of the other ones. We have an annual retreat thing, so if you want to get a hold of one of the others, let me know.

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Notes from MLconf 2017 in NYC (part 2)

Evan Estola from Meetup gave probably the second-best talk of the day (I have to give the best talk to Corinna Cortes) talking about our responsibility as wranglers of these learners to protect them from learning bigoted or otherwise harmful patterns. He noted that while some people believe that anything the learner infers from the data is somehow pure and bias-free, it is more accurate to say that while we are tinkering with every other constraint and parameter of a model we might as well fix the stuff that yields socially unwelcome inferences. He said it more elegantly of course, but it was neat that he kind of spanked everybody with that.

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Notes from MLconf 2017 in NYC

When Ben Hamner from Kaggle came up to the stage everyone in the room kind of straightened up and made little waking-up sounds, since Kaggle was recently acquired by Google, and because having the guy from Kaggle in a room full of ML people is a bit like having the guy from Easy Mac in a room full of undergraduate students. He didn't really get into that much in terms of the practice of machine learning, but did mention that winning learners are nearly always ensembles.

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