Three Bits about Artificial Intelligence

Ken Jennings on what it’s like to play Jeopardy against IBM supercomputer Watson:

I expected Watson’s bag of cognitive tricks to be fairly shallow, but I felt an uneasy sense of familiarity as its programmers briefed us before the big match: The computer’s techniques for unraveling Jeopardy! clues sounded just like mine. That machine zeroes in on key words in a clue, then combs its memory (in Watson’s case, a 15-terabyte data bank of human knowledge) for clusters of associations with those words. It rigorously checks the top hits against all the contextual information it can muster: the category name; the kind of answer being sought; the time, place, and gender hinted at in the clue; and so on. And when it feels “sure” enough, it decides to buzz. This is all an instant, intuitive process for a human Jeopardy! player, but I felt convinced that under the hood my brain was doing more or less the same thing.

Steven Levy in The AI Revolution is on:

The lesson is that our computers sometimes have to humor us, or they will freak us out. Eric Horvitz—now a top Microsoft researcher and a former president of the Association for the Advancement of Artificial Intelligence—helped build an AI system in the 1980s to aid pathologists in their studies, analyzing each result and suggesting the next test to perform. There was just one problem—it provided the answers too quickly. “We found that people trusted it more if we added a delay loop with a flashing light, as though it were huffing and puffing to come up with an answer,” Horvitz says.

Matt Webb on Domestic Robots:

When we imagine something is intelligent, we simulate its mind inside our own, in order to anticipate it. We begin to think a bit like it, in some small way. We socialise with it, takes cues from it.

[...] Robots don’t need their own brains: they can parasite on ours. Be intelligent simply by appearing to be intelligent.

Bloom.io

Bloom.io is a new design agency working at the intersection of data visualization and game design. Members include Ben Cerveny of Stamen and Flickr fame and Robert Hodgin, co-founder of The Barbarian Group.

I’m already rather fond of their beautiful social networking visualization Fizz. It doesn’t look like much until you try it, but it has already given me a much better overview of individual activity on Twitter than i had before.

Added later: There’s now also an introductory blog post to Fizz.

Sol Lewitt + Mechanical Turk

Custom software recreates various Sol LeWitt drawings. The software also posts instructions on Amazon.com’s Mechanical Turk. Human workers execute the drawings online based on the instructions from the program. The workers are paid 5¢ for each drawing. The software then assembles the drawings in a grid. The computer generated drawings, and the grids filled in by anonymous workers are displayed side by side.

By Clement Valla. (via)

Time-shifted Reading Habits

The Read It Later blog offers a fascinating look at how reading times shift across different devices. Most iPad reading is done in the evening and iPad readers don’t read on their computers anymore.

I find this observation interesting:

Printed media used to allow us to read in the places we found most comfortable. When you imagine yourself reading the newspaper it’s probably in your favorite chair, at the breakfast table, or at the cafe with an orange mocha frappuccino in your hand.
Unfortunately, as news and media moves online, it moves us away from these places and into our desk chairs.

Maybe that’s why i value my iPad so much, because i’ve always done a disproportionate amount of reading on screens, and the PC screens on our desks are indeed rather terrible for relaxed reading.

I faintly remember reading about similar statistics sometime last year, probably originating from the BBC but mentioned in passing by someone else, where they also observed that people predominantly use their iPads in the primetime evening spot. Unfortunately i can’t find the source for that…