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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