The NYTimes has been on something of a robot kick lately. Today, robot teachers:
In a handful of laboratories around the world, computer scientists are developing robots like this one: highly programmed machines that can engage people and teach them simple skills, including household tasks, vocabulary or, as in the case of the boy, playing, elementary imitation and taking turns.
So far, the teaching has been very basic, delivered mostly in experimental settings, and the robots are still works in progress, a hackers’ gallery of moving parts that, like mechanical savants, each do some things well at the expense of others.
Yet the most advanced models are fully autonomous, guided by artificial intelligence software like motion tracking and speech recognition, which can make them just engaging enough to rival humans at some teaching tasks.
Researchers say the pace of innovation is such that these machines should begin to learn as they teach, becoming the sort of infinitely patient, highly informed instructors that would be effective in subjects like foreign language or in repetitive therapies used to treat developmental problems like autism.
We learn that, big surprise, learning from robots is better than learning from videotapes. And we do “about as well as learning from a human teacher.” But making the robot look more humanoid creeps us out. We’d be perfectly comfortable with an R2-D2 look alike; far less so a Lt. Commander Data.
Last week the Times’ story was on robot companions:
After years of effort to coax empathy from circuitry, devices designed to soothe, support and keep us company are venturing out of the laboratory. Paro [video], its name derived from the first sounds of the words “personal robot,” is one of a handful that take forms that are often odd, still primitive and yet, for at least some early users, strangely compelling. […]
Their appearances in nursing homes, schools and the occasional living room are adding fuel to science fiction fantasies of machines that people can relate to as well as rely on. And they are adding a personal dimension to a debate over what human responsibilities machines should, and should not, be allowed to undertake.
Ms. Oldaker, a part-time administrative assistant, said she was glad Paro could keep her mother company when she could not. In the months before Mrs. Lesek died in March, the robot became a fixture in the room even during her daughter’s own frequent visits.
Here we learn that “our technology is getting ahead of our psychology” as the story explores the emergence of “robot therapy.” Apparently, it works.
On the language/intelligence front, there’s I.B.M.’s Watson, the Jeopardy champ. If you missed Clive Thompson’s Magazine story on Watson last month, it’s well worth returning to for a summer read:
The producers of “Jeopardy!” have agreed to pit Watson against some of the game’s best former players as early as this fall. To test Watson’s capabilities against actual humans, I.B.M.’s scientists began holding live matches last winter. They mocked up a conference room to resemble the actual “Jeopardy!” set, including buzzers and stations for the human contestants, brought in former contestants from the show and even hired a host for the occasion: Todd Alan Crain, who plays a newscaster on the satirical Onion News Network.
Technically speaking, Watson wasn’t in the room. It was one floor up and consisted of a roomful of servers working at speeds thousands of times faster than most ordinary desktops. Over its three-year life, Watson stored the content of tens of millions of documents, which it now accessed to answer questions about almost anything. (Watson is not connected to the Internet; like all “Jeopardy!” competitors, it knows only what is already in its “brain.”)
How Watson works:
Watson’s speed allows it to try thousands of ways of simultaneously tackling a “Jeopardy!” clue. Most question-answering systems rely on a handful of algorithms, but [I.B.M.’s senior manager for its Semantic Analysis and Integration department David] Ferrucci decided this was why those systems do not work very well: no single algorithm can simulate the human ability to parse language and facts. Instead, Watson uses more than a hundred algorithms at the same time to analyze a question in different ways, generating hundreds of possible solutions. Another set of algorithms ranks these answers according to plausibility; for example, if dozens of algorithms working in different directions all arrive at the same answer, it’s more likely to be the right one. In essence, Watson thinks in probabilities. It produces not one single “right” answer, but an enormous number of possibilities, then ranks them by assessing how likely each one is to answer the question.
That pretty much sounds like Daniel Dennett‘s 1991 thesis in Consciousness Explained for how our own minds work. Wikipedia summarizes:
The book puts forward a “multiple drafts” model of consciousness, suggesting that there is no single central place (a “Cartesian Theater“) where conscious experience occurs; instead there are “various events of content-fixation occurring in various places at various times in the brain”.[1] The brain consists of a “bundle of semi-independent agencies”;[2] when “content-fixation” takes place in one of these, its effects may propagate so that it leads to the utterance of one of the sentences that make up the story in which the central character is one’s “self”. Dennett’s view of consciousness is that it is the apparently serial account for the brain’s underlying parallelism.
If Watson ever achieves consciousness — several contestants who play Watson reference Skynet, the computer system in the “Terminator” movies that achieves consciousness and decides humanity should be destroyed — we’ll have watched it happen:
When Watson is playing a game, Ferrucci lets the audience peek into the computer’s analysis. A monitor shows Watson’s top five answers to a question, with a bar graph beside each indicating its confidence. During one of my visits, the host read the clue “Thousands of prisoners in the Philippines re-enacted the moves of the video of this Michael Jackson hit.” On the monitor, I could see that Watson’s top pick was “Thriller,” with a confidence level of roughly 80 percent. This answer was correct, and Watson buzzed first, so it won $800. Watson’s next four choices — “Music video,” “Billie Jean,” “Smooth Criminal” and “MTV” — had only slivers for their bar graphs. It was a fascinating glimpse into the machine’s workings, because you could spy the connective thread running between the possibilities, even the wrong ones. “Billie Jean” and “Smooth Criminal” were also major hits by Michael Jackson, and “MTV” was the main venue for his videos. But it’s very likely that none of those correlated well with “Philippines.”
If consciousness comes, it’ll cost Watson:
Ultimately, Watson’s greatest edge at “Jeopardy!” probably isn’t its perfect memory or lightning speed. It is the computer’s lack of emotion. “Managing your emotions is an enormous part of doing well” on “Jeopardy!” Bob Harris, a five-time champion, told me. “Every single time I’ve ever missed a Daily Double, I always miss the next clue, because I’m still kicking myself.” Because there is only a short period before the next clue comes along, the stress can carry over. Similarly, humans can become much more intimidated by a $2,000 clue than a $200 one, because the more expensive clues are presumably written to be much harder.
We’ll be able to buy our own Watson — well, that is if we can afford “at least one $1 million I.B.M. server” — within a year or two. Expect to see a Watson helpdesk or a Watson M.D. then. Faster than humans and more likely to have the right answer. Amazing.
You can find me @jwindish, at my Public Notebook, or email me at joe-AT-joewindish-DOT-com.