AI Speed Bumps
March 5, 2018
If you read anything about artificial intelligence these days, it’s usually about how efficiency will go through the roof because of it. Either that, or how it’s the most destructive invention since the nuclear bomb. Both of those might be a moot point because the science behind AI is being seriously questioned. This came from a recent Science Magazine story, “Missing Data Hinders Replication of Artificial Intelligence Studies.”
According to the piece:
“The booming field of artificial intelligence (AI) is grappling with a replication crisis, much like the ones that have afflicted psychology, medicine, and other fields over the past decade. AI researchers have found it difficult to reproduce many key results, and that is leading to a new conscientiousness about research methods and publication protocols.”
The source stems from scientists’ need to publish quickly and get the upper hand in a competitive field. That means algorithms are not tested in every situation and that cracks begin to form in their theories. This feels like a growing pain for this rapidly expanding field. This comes on the heels of another study that claims we are thinking about AI all wrong. We humans tend to think of AI as working like a human brain, but it works like something totally different. We suddenly don’t have a lot of faith that scientists will get to the bottom of what that is, exactly.
As an example, the dust up between Elon Musk and Steven Pinker does little to reassure neutral observers about smart software.
Patrick Roland, March 5, 2018
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2 Responses to “AI Speed Bumps”
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Scientific quality and practical application are quite different.
I’m all for researchers’ advancing the state of the art and being critical.
But that doesn’t preclude results from being useful. The Simplex algorithm for linear programming is provably inefficient in general, but turns out to be very practical for most applications.
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