Great Interview in IEEE Spectrum with machine learning expert, UC Berkeley Professor, and IEEE Fellow Michael Jordan:
“…people continue to infer… that deep learning is taking advantage of an understanding of how the brain processes information, learns, makes decisions, or copes with large amounts of data. And that is just patently false.”
“There is progress at the very lowest levels of neuroscience. But for issues of higher cognition—how we perceive, how we remember, how we act—we have no idea how neurons are storing information, how they are computing, what the rules are, what the algorithms are, what the representations are, and the like. So we are not yet in an era in which we can be using an understanding of the brain to guide us in the construction of intelligent systems.”
“…with big data, it will take decades, I suspect, to get a real engineering approach, so that you can say with some assurance that you are giving out reasonable answers and are quantifying the likelihood of errors.”
“The main [adverse consequences if we remain on the big data trajectory we are on] will be a ‘big-data winter.’ After a bubble, when people invested and a lot of companies overpromised without providing serious analysis, it will bust. And soon, in a two- to five-year span, people will say, “The whole big-data thing came and went. It died. It was wrong.” I am predicting that. It’s what happens in these cycles when there is too much hype, i.e., assertions not based on an understanding of what the real problems are or on an understanding that solving the problems will take decades, that we will make steady progress but that we haven’t had a major leap in technical progress. And then there will be a period during which it will be very hard to get resources to do data analysis. The field will continue to go forward, because it’s real, and it’s needed. But the backlash will hurt a large number of important projects.”
Note that Jordan took issue with the title and the lead-in to the IEEE Spectrum article.
More on what needs to be done to avoid a big data winter is in Jordan’s Reddit AMA and in the Frontiers in Massive Data Analysis report from the US National Research Council’s Committee on the Analysis of Massive Data (which Jordan chaired).
Reblogged this on Leaders in Pharmaceutical Business Intelligence.
In the mid 90’s we used Prof. Michael Jordan’s developments in Analytics, I.e., Brownian motion algorithms in a consultancy assignment for Fidelity Investment’s Derivatives Analytics Department, for prediction of Bond Yields under certain dynamics parameter definition. Great textbook by him on that type of statistical modeling technology.
His views on Big Data are of a Statistician, though he is in EE and a colleague of Prof. H. Lev-Ari since the Stanford days. Namely, big data, is real, is a must form all discipline to use as a measurement environment, the existing modeling technology is not adequate yet for the real challenge of “Massive Databases” more math development is needed, Media runs the course of Hype.
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