AI News, Benedict Evans
- On Thursday, October 4, 2018
- By Read More
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?’
This means that image sensors (and microphones) become a whole new input mechanism - less a ‘camera’ than a new, powerful and flexible sensor that generates a stream of (potentially) machine-readable data.
I met a company recently that supplies seats to the car industry, which has put a neural network on a cheap DSP chip with a cheap smartphone image sensor, to detect whether there’s a wrinkle in the fabric (we should expect all sorts of similar uses for machine learning in very small, cheap widgets, doing just one thing, as described here).
A ten year old could sort them into men and women, a fifteen year old into cool and uncool and an intern could say ‘this one’s really interesting’.
It might never get to the intern.But what would you do if you had a million fifteen year olds to look at your data?What calls would you listen to, what images would you look at, and what file transfers or credit card payments would you inspect?
Where this metaphor breaks down (as all metaphors do) is in the sense that in some fields, machine learning can not just find things we can already recognize, but find things that humans can’t recognize, or find levels of pattern, inference or implication that no ten year old (or 50 year old) would recognize.
That is, this not so much a thousand interns as one intern that’s very very fast, and you give your intern 10 million images and they come back and say ‘it’s a funny thing, but when I looked at the third million images, this pattern really started coming out’.
Equally, the only reason we’re talking about autonomous cars and mixed reality is because machine learning (probably) enables them - ML offers a path for cars to work out what’s around them and what human drivers might be going to do, and offers mixed reality a way to work out what I should be seeing, if I’m looking though a pair of glasses that could show anything.
But after we’ve talked about wrinkles in fabric or sentiment analysis in the call center, these companies tend to sit back and ask, ‘well, what else?’ What are the other things that this will enable, and what are the unknown unknowns that it will find?
- On Thursday, January 17, 2019
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