How to think about machine learning?

One of the challenges in talking about machine learning is to find the middle ground between a mechanistic explanation of the mathematics on one hand and fantasies about general AI on the other. Machine learning is not going to create HAL 9000 (at least, very few people in the field think that it will do so any time soon), but it’s also not useful to call it ‘just statistics’.
Returning to the parallels with relational databases, this might be rather like talking about SQL in 1980 – how do you get from explaining table joins to thinking about Salesforce.com? It’s all very well to say ‘this lets you ask these new kinds of questions’, but it isn’t always very obvious what questions.
You can do impressive demos of voice recognition and image recognition, but again, what would a normal company do with that? As a team at a major US media company said to me a while ago: ‘well, we know we can use ML to index ten years of video of our talent interviewing athletes – but what do we look for?’

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