Nah, we’re all too busy worrying about whether we’re experts in our field. (Source: xkcd)
When my advisor informed her assembled advisees that I was the group’s “machine learning expert,” I nearly choked. I thought I had a pretty good idea of what expertise looked like. An expert possesses a deep, intuitive understanding of his or her subject. An expert exudes confidence in his or her abilities and reputation. An expert fields detailed questions without batting an eyelid. What an expert most certainly does not look like, I thought, is a clueless amateur of a Ph.D. student.
My lofty image of expertise was not my own invention – our society has an unhealthy tendency to fetishize experts. We see the degree of knowledge possessed by professors and analysts and TED speakers as almost mystical. We speak in awed whispers of their brilliance and intuition. And of course, the praise is often well-deserved; I don’t mean to suggest that there is no such thing as expertise. But the way we idolize experts does great damage to experts and novices alike. Continue reading
In 1984, IBM encountered a mystery: computers in Denver were making ten times more unexplained mistakes than the national average. The operators of the computers kept reporting memory errors, but whenever they sent a memory unit back to IBM, the company could find nothing physically wrong. Why wouldn’t computers work properly in Denver?
For several years, the operators had to work around the fact that their computers would occasionally just forget things. It was almost like the computers were high – which, it turned out, was precisely the problem. At 5,280 feet, computers in Denver are much more susceptible to an unlikely culprit: cosmic rays. Continue reading
Everything in the world works by processing information. Even thermoses.
There’s an old joke about three construction workers arguing about the greatest invention ever. The first worker nominates the telephone: “Now we can hear people from miles away!” The second worker points out that with television we can see them, too. “No,” the third worker insists, “you’re both wrong. The greatest invention is the thermos.”
“Sure. On a cold day, it keeps my soup hot. On a warm day, it keeps my lemonade cold.”
“How does it know??”
Laughable though it is, this question is surprisingly insightful from the perspective of computer science. Computation is all about what a system knows and what it can learn from that: how can your laptop, armed with just a wireless radio, find out what www.google.com looks like? How can a security camera infer what’s in an image from a blurry bunch of pixels? Almost every classic problem in computer science amounts to some form of manipulating information.