AI News, Machine-Learning Maestro Michael Jordan on the Delusions of Big Data and Other Huge Engineering Efforts
- On Sunday, June 3, 2018
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Machine-Learning Maestro Michael Jordan on the Delusions of Big Data and Other Huge Engineering Efforts
IEEE Spectrum: I infer from your writing that you believe there’s a lot of misinformation out there about deep learning, big data, computer vision, and the like.
And one of the problems with both the previous wave, that has unfortunately persisted in the current wave, is that people continue to infer that something involving neuroscience is behind it, and 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.
Spectrum: It’s always been my impression that when people in computer science describe how the brain works, they are making horribly reductionist statements that you would never hear from neuroscientists.
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.
Spectrum: In addition to criticizing cartoon models of the brain, you actually go further and criticize the whole idea of “neural realism”—the belief that just because a particular hardware or software system shares some putative characteristic of the brain, it’s going to be more intelligent.
I think in the early 1980s, computer science was dominated by sequential architectures, by the von Neumann paradigm of a stored program that was executed sequentially, and as a consequence, there was a need to try to break out of that.
But people tend to lump that particular success story together with all the other attempts to build brainlike systems that haven’t been nearly as successful.
A second area involves what you were describing and is aiming to get closer to a simulation of an actual brain, or at least to a simplified model of actual neural circuitry, if I understand correctly.
As I alluded to before, back in the 1980s, it was actually helpful to say, “Let’s move out of the sequential, von Neumann paradigm and think more about highly parallel systems.” But in this current era, where it’s clear that the detailed processing the brain is doing is not informing algorithmic process, I think it’s inappropriate to use the brain to make claims about what we’ve achieved.
- On Thursday, January 17, 2019
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