Final Jeopardy (20 page)

Not everyone agrees. Another faction, closely associated with search engines, is approaching machine intelligence from a different angle. They often remove human experts from the training process altogether and let computers, guided by algorithms, study largely on their own. These are the statisticians. They're closer to Watson's camp. For decades, they've been at odds with their rule-writing colleagues. But their approach registered a dramatic breakthrough in 2005, when the U.S. National Institute for Standards and Technologies held one of its periodic competitions on machine translation. The government was ravenous for this translation technology. If machines could automatically monitor and translate Internet traffic, analysts might get a jump on trends in trade and technology and, even more important, terrorism. The competition that year focused on machine translation from Chinese and Arabic into English. And it drew the usual players, including a joint team from IBM and Carnegie Mellon and a handful of competitors from Europe. Many of these teams, with their blend of experts in linguistics, cognitive psychology, and computer science, had decades of experience working on translations.

One new player showed up: Google. The search giant had been hiring experts in machine translation, but its team differed from the others in one aspect: No one was expert in Arabic or Chinese. Forget the nuances of language. They would do it with math. Instead of translating based on semantic and grammatical structure, the interplay of the verbs and objects and prepositional phrases, their computers were focusing purely on statistical relationships. The Google team had fed millions of translated documents, many of them from the United Nations, into their computers and supplemented them with a multitude of natural-language text culled from the Web. This training set dwarfed their competitors'. Without knowing what the words meant, their computers had learned to associate certain strings of words in Arabic and Chinese with their English equivalents. Since they had so very many examples to learn from, these statistical models caught nuances that had long confounded machines. Using statistics, Google's computers won hands down. “Just like that, they bypassed thirty years of work on machine translation,” said Ed Lazowska, the chairman of the computer science department at the University of Washington.

The statisticians trounced the experts. But the statistically trained machines they built, whether they were translating from Chinese or analyzing the ads that a Web surfer clicked, didn't know anything. In that sense, they were like their question-answering cousins, the forerunners of the yet-to-be-conceived
Jeopardy
machine. They had no response to different types of questions, ones they weren't programmed to answer. They were incapable of reasoning, much less coming up with ideas.

Machines were seemingly boxed in. When people taught them about the world, as in the Halo project, the process was too slow and expensive and the machines ended up “overfitted”—locked into single interpretations of facts and relationships. Yet when machines learned for themselves, they turned everything into statistics and remained, in their essence, ignorant.

How could computers get smarter about the world? Tom Mitchell, a computer science professor at Carnegie Mellon, had an idea. He would develop a system that, just like millions of other students, would learn by reading. As it read, it would map all the knowledge it could make sense of. It would learn that Buenos Aires appeared to be a city, and a capital too, and for that matter also a province, that it fit inside Argentina, which was a country, a South American country. The computer would perform the same analysis for billions of other entities. It would read twenty-four hours a day, seven days a week. It would be a perpetual reading machine, and by extracting information, it would slowly cobble together a network of knowledge: every president, continent, baseball team, volcano, endangered species, crime. Its curriculum was the World Wide Web.

Mitchell's goal was not to build a smart computer but to construct a body of knowledge—a corpus—that smart computers everywhere could turn to as a reference. This computer, he hoped, would be doing on a global scale what the human experts in chemistry had done, at considerable cost, for the Halo system. Like Watson, Mitchell's Read-the-Web computer, later called NELL, would feature a broad range of analytical tools, each one making sense of the readings from its own perspective. Some would compare word groups, others would parse the grammar. “Learning method A might decide, with 80 percent probability, that Pittsburgh is a city,” Mitchell said. “Method C believes that Luke Ravenstahl is the mayor of Pittsburgh.” As the system processed these two beliefs, it would find them consistent and mutually reinforcing. If the entity called Pittsburgh had a mayor, there was a good chance it was a city. Confidence in that belief would rise. The computer would learn.

Mitchell's team turned on NELL in January 2010. It worked on a subsection of the Web, a cross section of two hundred million Web pages that had been culled and curated by Mitchell's colleague Jamie Callan. (Operating with a fixed training set made it easier in the early days to diagnose troubles and carry out experiments.) Within six months, the machine had developed some four hundred thousand beliefs—a minute fraction of what it would need for a global knowledge base. But Mitchell saw NELL and other fact-hunting systems growing quickly. “Within ten years,” he predicted, “we'll have computer programs that can read and extract 80 percent of the content of the Web, which itself will be much bigger and richer.” This, he said, would produce
“a huge knowledge base that AI can work from.”

Much like Watson, however, this knowledge base would brim with beliefs, not facts. After all, statistical systems merely develop confidence in facts as a calculation of probability. They believe, to one degree or another, but are certain of nothing. Humans, by contrast, must often work from knowledge. Halo's Friedland (who left Vulcan to set up his own shop in 2005) argues that AI systems informed by machine learning will end up as dilettantes, like Watson (at least in its
Jeopardy
incarnation). A computer, he said, can ill afford to draw conclusions about a jet engine turbine based on “beliefs” about bypass ratios or the metallurgy of titanium alloys. It has to know those things.

So when it came to teaching knowledge machines at the end of the first decade of the twenty-first century, it was a question of picking your poison. Computers that relied on human teachers were slow to learn and frightfully expensive to teach. Those that learned automatically unearthed possible answers with breathtaking speed. But their knowledge was superficial, and they were unable to reason from it. The goal of AI—to marry the speed and range of machines with the depth and subtlety of the human brain—was still awaiting a breakthrough. Some believed it was at hand.

In 1859, the British writer Samuel Butler sailed from England, the most industrial country on earth, to the wilds of New Zealand. There, for a few years, he raised sheep. He was as far away as could be, on the antipodes, but he had the latest books shipped to him. One package included the new work by Charles Darwin,
On the Origin of Species.
Reading it led Butler to contemplate humanity in an evolutionary context. Presumably, humans had developed through millions of years, and their rhythms, from the perspective of his New Zealand farm, appeared almost timeless. Like sheep, people were born, grew up, worked, procreated, died, and didn't change much. If the species evolved from one century to the next, it was imperceptible. But across the seas, in London, the face of the earth was changing. High-pressure steam engines, which didn't exist when his parents were born, were powering trains across the countryside. Information was speeding across Europe through telegraph wires. And this was just the beginning. “In these last few ages,” he wrote, referring to machines, “an entirely new kingdom has sprung up, of which we as yet have only seen what will one day be considered the antediluvian prototypes of the race.” The next step of human evolution, he wrote in an 1863 letter to the editor of a local newspaper, would be led by the progeny of steam engines, electric turbines, and telegraphs. Human beings would eventually cede planetary leadership to machines. (Not to fear, he predicted: The machines would care for us, much the way humans tended to lesser beings.)

What sort of creature [is] man's next successor in the supremacy of the earth likely to be? We have often heard this debated; but it appears to us that we are ourselves creating our own successors; we are daily adding to the beauty and delicacy of their physical organisation; we are daily giving them greater power and supplying by all sorts of ingenious contrivances that self-regulating, self-acting power which will be to them what intellect has been to the human race. In the course of ages we shall find ourselves the inferior race.

Butler's vision, and others like it, nourished science fiction for more than a century. But in the waning years of the twentieth century, as the Internet grew to resemble a global intelligence and computers continued to gain in power, legions of technogeeks and philosophers started predicting that the age of machines was almost upon us. They called it the Singularity, a hypothetical time in which progress in technology would feed upon itself feverishly, leading to transformational change.

In August 2010, hundreds of computer scientists, cognitive psychologists, futurists, and curious technophiles descended on San Francisco's Hyatt hotel, on the Embarcadero, for the two-day Singularity Summit. For most of these people, programming machines to catalogue knowledge and answer questions, whether manually or by machine, was a bit pedestrian. They weren't looking for advances in technology that already existed. Instead, they were focused on a bolder challenge, the development of deep and broad machine intelligence known as Artificial General Intelligence. This, they believed, would lead to the next step of human evolution.

The heart of the Singularity argument, as explained by the technologists Vernor Vinge and Ray Kurzweil, the leading evangelists of the concept, lies in the power of exponential growth. As Samuel Butler noted, machines evolve far faster than humans. But information technology, which Butler only glimpsed, races ahead at an even faster rate. Digital tools double in power or capacity every year or two, whether they are storing data, transmitting it, or performing calculations. A single transistor cost $1 in 1968; by 2010 that same buck could buy a billion of them. This process, extended into the future, signaled that sometime in the third decade of this century, computers would rival or surpass the power and complexity of the human brain. At that point, conceivably, machines would organize our affairs, come up with groundbreaking ideas, and establish themselves as the cognitive leaders of the planet.

Many believed these machines were yet to be invented. They would come along in a decade or two, powered by new generations of spectacularly powerful semiconductors, perhaps fashioned from exotic nanomaterials, the heirs to silicon. And they would feature programs to organize knowledge and generate language and ideas. Maybe the hardware would replicate the structure of the human brain. Maybe the software would simulate its patterns. Who knew? Whatever its configuration, perhaps a few of Watson's algorithms or an insight from Josh Tenenbaum's research would find their way into this machinery.

But others argued that the Singularity was already well under way. In this view, computers across the Internet were already busy recording our movements and shopping preferences, suggesting music and diets, and replacing traditional brain functions such as information recall and memory storage. Gregory Stock, a biophysicist, echoed Butler as he placed technology in an evolutionary context. “Lines are blurring between the living and the not-living, between the born and the made,” he said. The way he described it, life leapt every billion years or so into greater levels of complexity. It started with simple algaelike cells, advanced to complex cells and later to multicellular organisms, and then to an explosion of life during the Cambrian period, some five hundred fifty million years ago. This engendered new materials within earth's life forms, including bone. Stock argued that humans, using information technology, were continuing this process, creating a “planetary superorganism”—a joint venture between our cerebral cortex and silicon. He said that this global intelligence was already transforming and subjugating us, much the way our ancestors tamed the gray wolf to create dogs. He predicted that this next step of evolution would lead to the demise of “free-range humans,” and that those free of the support and control of the planetary superorganism would retreat to back eddies. “I hate to see them disappear,” he said.

The crowd at the Singularity Summit was by no means united in these visions. A biologist from Cambridge University, Dennis Bray, described the daunting complexity of a single cell and cautioned that the work of modeling the circuitry and behavior of even the most basic units of life remained formidable. “The number of distinguishable proteins that a human makes is essentially uncountable,” he said. So what chance was there to model the human brain, with its hundred billion neurons and quadrillions of connections?

In the near term, it was academic. No one was close to replicating the brain in form or function. Still, the scientists at the conference were busy studying it, hoping to glean from its workings single applications that could be taught to computers. The brain, they held, would deliver its treasures bit by bit. Tenenbaum was of this school.

And so was Demis Hassabis. A diminutive thirty-four-year-old British neuroscientist, Hassabis told the crowd that technology wasn't the only thing growing exponentially. Research papers on the brain were also doubling every year. Some fifty thousand academic papers on neuroscience had been published in 2008 alone. “If you looked at neuroscience in 2005, or before that, you're way out of date now,” he said. But which areas of brain research would lead to the development of Artificial General Intelligence?