A few weeks back, I was at a baseball game with a bunch of my wife’s coworkers when I started talking to a developer named Ian who said he just got done building a Hackintosh and it was amazing. To be honest, I hadn’t thought about the Hackintosh community in years, I actually forgot it was still a thing. Ian said the community was now organized around a website called TonyMacx86.com and it had hardware guides, build tutorials, forums, lots of updates, and had been extremely lively in the past 18 months or so as it’s now easier than ever to build a Hackintosh. When he told me how fast his custom Mac was (faster than any iMac and most Mac Pros), and how little it cost (around $1,200–1,300) it struck me as impossible. I know that Apple hasn’t updated their MacBook Pros or Mac Pros in a long time, and I know there’s an “Apple Tax” you pay when parts like RAM or a processor are included in an Apple-designed computer, but the more we talked about his build the more excited I became. It was as if someone told me, yeah, duh, of course there are flying cars, check out my flying car over in the parking lot. You want a flying car, too?
The current wave of AI excitement started with Hinton et al’s breakthrough success with deep convolutional neural networks on image classification. In a field that typical progresses by single percentage points, their results destroyed the previous state of the art. Hinton’s compatriots such as Yoshua Bengio, Yann LeCun, Andrew Ng and others quickly followed, using related techniques to set new benchmarks in speech recognition, face recognition, and a number of other research problems. The world of machine learning researchers rapidly became first aware of (and then profoundly obsessed by) this new suite of approaches, which was gathered together under the banner of Deep Learning.