Machine Learning for Predicting the Unknown

Great interview with Courtenay Cotton of n-Join. Here are some key tibits I found interesting.

  • People develop new algorithms and have breakthroughs, but it’s always that you’re optimizing algorithms, you’re solving for functions.

  • Data cleaning and data wrangling, as the first step doing any of this stuff, is a giant part of this field. There’s almost never not errors in your data.

  • In the tech community about 10 years ago, there was a cliché — not always true — that everyone was a college dropout. But it seems like machine learning is really driven by academics.via Medium

  • There’s always an air of mystery because, in reality, even for us researchers, a lot of these algorithms are black boxes.

  • Some AI researchers are legitimately trying to figure out how you would get a machine that learned like a human child. But in general, most of the work is I need this very specific thing that just does this one thing, and I’m going to throw all the data in the world that I can get my hands on at it.” At the end, it will be pretty good at that one thing—if we have the right data.

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