The Moving Frontier of AI: Climbing the Competence Hierarchy
A long tiresome speech delivered by a frothy pie topping.
A garment worn by a child, perhaps aboard an operatic ship.
Wanted for a twelve-year crime spree of eating King Hrothgar’s warriors; officer Beowulf has been assigned the case.
It can mean to develop gradually in the mind or to carry during pregnancy.
National Teacher Day and Kentucky Derby Day.
Wordsworth said they soar but never roam.
Four-letter word for the iron fitting on the hoof of a horse or a card-dealing box in a casino.
In act three of an 1846 Verdi opera, this Scourge of God is stabbed to death by his lover, Odabella.
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 these 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.
I, for one, welcome our new robot overlords.
Oh my god. [Watson] is more intelligent than the average Jeopardy! player in answering Jeopardy! questions. That’s impressively intelligent.
Watson understands nothing. It’s a bigger steamroller.
Artificial intelligence is all around us—we no longer have our hand on the plug. The simple act of connecting with someone via a text message, e-mail, or cell phone call uses intelligent algorithms to route the information. Almost every product we touch is originally designed in a collaboration between human and artificial intelligence and then built in automated factories. If all the AI systems decided to go on strike tomorrow, our civilization would be crippled: We couldn’t get money from our bank, and indeed, our money would disappear; communication, transportation, and manufacturing would all grind to a halt. Fortunately, our intelligent machines are not yet intelligent enough to organize such a conspiracy.
What is new in AI today is the viscerally impressive nature of publicly available examples. For example, consider Google’s self-driving cars (which as of this writing have gone over 200,000 miles in cities and towns), a technology that will lead to significantly fewer crashes, increased capacity of roads, alleviating the requirement of humans to perform the chore of driving, and many other benefits. Driverless cars are actually already legal to operate on public roads in Nevada with some restrictions, although widespread usage by the public throughout the world is not expected until late in this decade. Technology that intelligently watches the road and warns the driver of impending dangers is already being installed in cars. One such technology is based in part on the successful model of visual processing in the brain created by MIT’s Tomaso Poggio. Called MobilEye, it was developed by Amnon Shashua, a former postdoctoral student of Poggio’s. It is capable of alerting the driver to such dangers as an impending collision or a child running in front of the car and has recently been installed in cars by such manufacturers as Volvo and BMW.
I will focus in this section of the book on language technologies for several reasons. Not surprisingly, the hierarchical nature of language closely mirrors the hierarchical nature of our thinking. Spoken language was our first technology, with written language as the second. My own work in artificial intelligence, as this chapter has demonstrated, has been heavily focused on language. Finally, mastering language is a powerfully leveraged capability. Watson has already read hundreds of millions of pages on the Web and mastered the knowledge contained in these documents. Ultimately machines will be able to master all of the knowledge on the Web—which is essentially all of the knowledge of our human-machine civilization.
English mathematician Alan Turing (1912–1954) based his eponymous test on the ability of a computer to converse in natural language using text messages.13 Turing felt that all of human intelligence was embodied and represented in language, and that no machine could pass a Turing test through simple language tricks. Although the Turing test is a game involving written language, Turing believed that the only way that a computer could pass it would be for it to actually possess the equivalent of human-level intelligence. Critics have proposed that a true test of human-level intelligence should include mastery of visual and auditory information as well.14 Since many of my own AI projects involve teaching computers to master such sensory information as human speech, letter shapes, and musical sounds, I would be expected to advocate the inclusion of these forms of information in a true test of intelligence. Yet I agree with Turing’s original insight that the text-only version of the Turing test is sufficient. Adding visual or auditory input or output to the test would not actually make it more difficult to pass.