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I think 'machine learning' is so complex that people just don't feel like trying to explain it. That, or their business secrets are tied up in it, and they don

I think 'machine learning' is so complex that people just don't feel like trying to explain it. That, or their business secrets are tied up in it, and they don't want to give away the golden goose.

_delirium 15y ago
That's an explanation for some of the examples, but I think a lot of the times it's actually really simple, along the lines of, "we sift through some data and correlate it". The odd thing is, that often works, especially for user-facing perceptual stuff where there's a strong placebo effect, even more especially if you salt liberally with some hand-tuned biasing. Sort of how The Sims is able to use some super-simple algorithms to give the impression of interesting characters. However, if you do need some real magic to be done, and your product really won't work without it, then things get trickier; bad statistics, or at least statistics not really used correctly, is really common in the innards of these kinds of products.
hooande 15y ago
The problem with explaining machine learning is that it's not only complex, but it goes against the way that people normally think. Humans generally aren't built for making statistical calculations and going with highest probable outcomes. People are wired to understand narratives and compelling stories. To explain machine learning, you have to bridge a conceptual gap while also discussing a very technical idea. Personally, I think that the benefits of a product should be so evident that people don't care if machine learning was used or not. The pitch shouldn't be "This aggregator is awesome because of machine learning", but "This aggregrator is awesome (oh and we used machine learning)"
stonemetal 15y ago
I think 'machine learning' is so complex that people just don't feel like trying to explain it. I think it is very simple(outside of the secret sauce part) to people who know it, so they don't feel the need to explain it. People who haven't sat down and thought about it see it as magical. As an example(lifted from Programming Collaborative Intelligence by Segaran ) say you want to recommend movies to people. You have them rate movies. Then you take people in pairs and compare movies they have both rated to produce a distance between those two people. When you want to recommend a movie to Joe, you take the people who are closest to Joe and then find a movie that they rate highly that Joe has not rated, and suggest that to Joe. The secret sauce is in coming up with the distance function.

I think 'machine learning' is so complex that people just don't feel like trying to explain it. That, or their business secrets are tied up in it, and they don't want to give away the golden goose.

_delirium 15y ago

That's an explanation for some of the examples, but I think a lot of the times it's actually really simple, along the lines of, "we sift through some data and correlate it". The odd thing is, that often works, especially for user-facing perceptual stuff where there's a strong placebo effect, even more especially if you salt liberally with some hand-tuned biasing. Sort of how The Sims is able to use some super-simple algorithms to give the impression of interesting characters. However, if you do need some real magic to be done, and your product really won't work without it, then things get trickier; bad statistics, or at least statistics not really used correctly, is really common in the innards of these kinds of products.

hooande 15y ago

The problem with explaining machine learning is that it's not only complex, but it goes against the way that people normally think. Humans generally aren't built for making statistical calculations and going with highest probable outcomes. People are wired to understand narratives and compelling stories. To explain machine learning, you have to bridge a conceptual gap while also discussing a very technical idea. Personally, I think that the benefits of a product should be so evident that people don't care if machine learning was used or not. The pitch shouldn't be "This aggregator is awesome because of machine learning", but "This aggregrator is awesome (oh and we used machine learning)"

stonemetal 15y ago

I think 'machine learning' is so complex that people just don't feel like trying to explain it. I think it is very simple(outside of the secret sauce part) to people who know it, so they don't feel the need to explain it. People who haven't sat down and thought about it see it as magical. As an example(lifted from Programming Collaborative Intelligence by Segaran ) say you want to recommend movies to people. You have them rate movies. Then you take people in pairs and compare movies they have both rated to produce a distance between those two people. When you want to recommend a movie to Joe, you take the people who are closest to Joe and then find a movie that they rate highly that Joe has not rated, and suggest that to Joe. The secret sauce is in coming up with the distance function.