Theranos CEO Elizabeth Holmes was a persuasive promoter. She convinced many presumably intelligent people that Theranos had developed a technology which could take a few blood drops from a finger prick to test for myriad diseases. The Theranos hoopla turned out to be just another point on the Silicon Valley “Fake-it-Till-You-Make-it” spectrum of BS. This past January, Holmes was found guilty of wire fraud and conspiracy to commit fraud.
Theranos is hardly unique, though successful criminal prosecutions are rare. As the pitch-person mantra goes, “We aren’t selling products; we’re selling dreams.” Too often, investors are beguiled by products and technologies they don’t understand. Mysterious complexity only adds to the allure: “If we don’t understand them, they must be really smart.” For the past several years, the center of the dream universe has been artificial intelligence, which Sundar Pichai, Alphabet’s
CEO, has compared to mankind’s harnessing of fire and electricity. The Association of National Advertisers selected “AI” as the marketing word of the year in 2017. AI is really good at performing narrowly defined chores that require a prodigious memory and fast calculations, but brittle and unreliable at tasks which require more than the identification of statistical patterns in test data. Thus, machine learning pioneer Andrew Ng cautioned that, “Those of us in machine learning are really good at doing well on a test set but unfortunately deploying a system takes more than doing well on a test set.” The real world is messy and AI algorithms struggle with messy data and complex goals. In the game Go, for example, the rules and the goal are clear and AI algorithms can defeat the best human players. If, however, the rules were changed or the goals could not be quantified …