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Rather than demonstrating that machines are getting close to artificial intelligence, Deep Blue and Watson actually indicated the contrary. “These recent achievements have, ironically, underscored the limitations of computer science and artificial intelligence,” argued Professor Tomaso Poggio, director of the Center for Brains, Minds, and Machines at MIT. “We do not yet understand how the brain gives rise to intelligence, nor do we know how to build machines that are as broadly intelligent as we are.”9
Douglas Hofstadter, a professor at Indiana University, combined the arts and sciences in his unexpected 1979 best seller, Gödel, Escher, Bach. He believed that the only way to achieve meaningful artificial intelligence was to understand how human imagination worked. His approach was pretty much abandoned in the 1990s, when researchers found it more cost-effective to tackle complex tasks by throwing massive processing power at huge amounts of data, the way Deep Blue played chess.10
This approach produced a peculiarity: computers can do some of the toughest tasks in the world (assessing billions of possible chess positions, finding correlations in hundreds of Wikipedia-size information repositories), but they ca
At Applied Minds near Los Angeles, you can get an exciting look at how a robot is being programmed to maneuver, but it soon becomes apparent that it still has trouble navigating an unfamiliar room, picking up a crayon, and writing its name. A visit to Nuance Communications near Boston shows the wondrous advances in speech-recognition technologies that underpin Siri and other systems, but it’s also apparent to anyone using Siri that you still can’t have a truly meaningful conversation with a computer, except in a fantasy movie. At the Computer Science and Artificial Intelligence Laboratory of MIT, interesting work is being done on getting computers to perceive objects visually, but even though the machine can discern pictures of a girl with a cup, a boy at a water fountain, and a cat lapping up cream, it ca
All of these tasks have one thing in common: even a four-year-old can do them. “The main lesson of thirty-five years of AI research is that the hard problems are easy and the easy problems are hard,” according to Steven Pinker, the Harvard cognitive scientist.12 As the futurist Hans Moravec and others have noted, this paradox stems from the fact that the computational resources needed to recognize a visual or verbal pattern are huge.
Moravec’s paradox reinforces von Neuma
So why not make a computer that mimics the processes of the human brain? “Eventually we’ll be able to sequence the human genome and replicate how nature did intelligence in a carbon-based system,” Bill Gates speculates. “It’s like reverse-engineering someone else’s product in order to solve a challenge.”14 That won’t be easy. It took scientists forty years to map the neurological activity of the one-millimeter-long roundworm, which has 302 neurons and 8,000 synapses.I The human brain has 86 billion neurons and up to 150 trillion synapses.15
At the end of 2013, the New York Times reported on “a development that is about to turn the digital world on its head” and “make possible a new generation of artificial intelligence systems that will perform some functions that humans do with ease: see, speak, listen, navigate, manipulate and control.” The phrases were reminiscent of those used in its 1958 story on the Perceptron (“will be able to walk, talk, see, write, reproduce itself and be conscious of its existence”). Once again, the strategy was to replicate the way the human brain’s neural networks operate. As the Times explained, “the new computing approach is based on the biological nervous system, specifically on how neurons react to stimuli and co
Perhaps this latest round of reports does in fact mean that, in a few more decades, there will be machines that think like humans. “We are continually looking at the list of things machines ca
These latest advances may even lead to the singularity, a term that von Neuma
On the other hand, these latest stories might turn out to be like the similarly phrased ones from the 1950s, glimpses of a receding mirage. True artificial intelligence may take a few more generations or even a few more centuries. We can leave that debate to the futurists. Indeed, depending on your definition of consciousness, it may never happen. We can leave that debate to the philosophers and theologians. “Human ingenuity,” wrote Leonardo da Vinci, whose Vitruvian Man became the ultimate symbol of the intersection of art and science, “will never devise any inventions more beautiful, nor more simple, nor more to the purpose than Nature does.”
There is, however, yet another possibility, one that Ada Lovelace would like, which is based on the half century of computer development in the tradition of Va
HUMAN-COMPUTER SYMBIOSIS: “WATSON, COME HERE”
“The Analytical Engine has no pretensions whatever to originate anything,” Ada Lovelace declared. “It can do whatever we know how to order it to perform.” In her mind, machines would not replace humans but instead become their partners. What humans would bring to this relationship, she said, was originality and creativity.