The Machine Learning delusion

This morning my personal biological computer detected a correlation between these two articles:

Sony’s SenseMe™ – A Superior Smart Shuffle

Machine learning: why we mustn’t be slaves to the algorithm

In the first article, the author is praising a “smart shuffle” algorithm that sequences tracks in your music collection with various themes such as “energetic, relax, upbeat”. It does this by analysing the music’s mood and tempo. It sounds amazing:

“I would never think of playing Steve Earl’s “Loretta” right after listening to the Boulder Philharmonic’s performance of “Olvidala,” or Ry Cooder’s “Crazy About an Automobile” followed by Doc and Merle Watson playing “Take Me Out to the Ballgame,” but I enjoyed not only the selections themselves but the way SensMe™ juxtaposes one after another, like a DJ who knows your collection better than you do…what will “he” play next? Surprise! It’s all good.”

And the algorithm’s effects go beyond mere music:

“SenseMe™ has brought domestic harmony – interesting selections for me and music with a similar mood for her. That’s better than marriage counseling! “

The author of the second article takes a more sceptical view. He notes the dumbness of Machine LearningTM algorithms, but says that

“…because these outputs are computer-generated, they are currently regarded with awe and amazement by bemused citizens …”

He quotes someone who is aware of the limitations:

“Machine learning is like a deep-fat fryer. If you’ve never deep-fried something before, you think to yourself: ‘This is amazing! I bet this would work on anything!’ And it kind of does. In our case, the deep fryer is a toolbox of statistical techniques. The names keep changing – it used to be unsupervised learning, now it’s called big data or deep learning or AI. Next year it will be called something else. But the core ideas don’t change. You train a computer on lots of data, and it learns to recognise structure.”

“But,” continues Cegłowski, “the fact that the same generic approach works across a wide range of domains should make you suspicious about how much insight it’s adding.”

I have been there. Machine learning is one of the most seductive branches of computer science, and in my experience is a very “easy sell” to people – I use it in my job in actual engineering applications where it can be eerily effective.

But if algorithms are so clever and know us so well, why are we using them only to shuffle the order of music? Why not cut out the middleman and get the computer to compose the music for us directly? The answer is obvious: it doesn’t work because we don’t know how the human brain works, and it is not predictable. By extension, the algorithms that purport to help us in matters of taste don’t actually work either. As the Guardian article says, all we are responding to is the novelty of the idea.

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