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Not entirely clear where this should go, so if the Mods have a better place for it, then be my guest.
CM: Going back to Alex Garland, and apologies for the clunkiness of this connection, but I recently watched his television series about a supercomputer being able to predict the future based on deterministic algorithms. In your ‘day job’ you work as a developer of supercomputer software. Any cool (or scary) ideas of what further supercomputer development could lead to?
BB: Most people misunderstand how supercomputers work and what supercomputers really do. We hit peak CPU speed about 15 years ago. More processing speed equals greater power consumption equals formidable heat dissipation problems, so unless there’s some kind of radical breakthrough in processor technology—quantum processing has been coming “next year” for as long as I’ve been in the industry; I’m not holding my breath—the way we increase computer power now is by building ever more massively parallel processor architectures.
The result is that the majority of the work being done on supercomputer systems now is just plain old computational fluid dynamics. Admittedly, we’re talking here about crunching through data sets measured in petabytes or exabytes, but deep down, it’s still just engineering. You may think it’s a dead language, but most of these programs are written in Fortran. While Fortran 2018 ain’t your grandaddy’s Fortran, it’s still Fortran.
There is interesting work being done in artificial intelligence and machine learning on supercomputers now, but it’s more in line with pattern recognition and data mining. For now, most AI work doesn’t need the kind of brute force a modern supercomputer brings to the table.
Ergo, for me, the most frightening possibilities are those that involve the misuse by unscrupulous politicians or corporations of the kinds of insights and inferences that can be drawn from such extensive data mining. The things that are being done right now, and that will be coming online in the next few years, should scare the Hell out of any civil libertarian.
AIs on their own seem to be best at finding flaws in their developer’s assumptions. I’ve seen AIs tasked with solving problems come up with hilariously unworkable solutions, because their developers made assumptions based on physical realities that did not apply in the virtual world in which the AI worked.
CM: Could you elaborate on your comments about data mining?
BB: Sure. What we’re talking about here is a field generally called “big data”. It’s the science of extracting presumably meaningful information from the enormous amount of data that’s being collected—well, everywhere, all the time. “Big data” tries to take information from disparate sources—structured and unstructured databases, credit bureaus, utility records, “the cloud”, pretty much everything—then mashes it together, finds coherences and correlations, and then tries to turn it into meaningful and actionable intelligence—for who? To do what with it? Those are the questions.
For just a small example: do you really want an AI bot to report to your medical clinic—or worse, to make medical treatment decisions—based on your credit card and cell phone dutifully reporting exactly when and for how long you were in the pub and exactly what you ate and drank? Or how about having it phone the Police, if you pay for a few pints and then get into the driver’s seat of your car?
That’s coming. As a fellow I met from a government agency whose name or even acronym I am forbidden to say out loud said, “Go ahead and imagine that you have privacy, if it makes you feel better.”