UC Berkeley professor Michael Jordan addresses the gap between commercial expectations for big data personalization and the underlying statistical limits of sparse user data.
“A lot of these business models are failing. People actually can't personalize very well, and it's Because of statistical issues. You've got huge amounts of data about some people, and very little about lots of people, and you don't know how to transfer the statistics about, around that.”
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More from Michael Jordan
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Jordan predicts principled personalized big data systems remain decades away
“So I think we're decades away from being able to do what this boss is asking us to do in some principle way. You can occasionally build a one-off system that does some of these things, but we're decades from having the real principles.”
Jordan warns high-dimensional Bayesian inference is overly sensitive to unknown priors
“And a lot of times you have no idea what the prior should be. You don't know what the tails should be in particular. And you're in high dimensions, you really have no idea how the tail behavior should be. And the whole inference is highly sensitive to the tail…”
Jordan argues meaningful real-world decisions require statistical error bars
“And real life decisions that mean something, you need error bars. If they don't mean anything, you're just trying to serve customers and hope that everybody comes to your website, you know, I don't know, who cares? But in real life, you need error bars.”
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