Any sufficiently advanced technology is indistinguishable from magic.
—Arthur C. Clarke, 1962
We used to think that computers can outperform people only in tasks like arithmetic, where straightforward rules are followed. That was what the book The New Division of Labor argued.
The … truck driver is processing a constant stream of [visual, aural, and tactile] information from his environment. … To program this behavior we could begin with a video camera and other sensors to capture the sensory input. But executing a left turn against oncoming traffic involves so many factors that it is hard to imagine discovering the set of rules that can replicate a driver’s behavior. …
Articulating [human] knowledge and embedding it in software for all but highly structured situations are at present enormously difficult tasks. … Computers cannot easily substitute for humans in [jobs like truck driving].
The results of the first DARPA Grand Challenge, held in 2004, supported Levy and Murnane’s conclusion. The challenge was to build a driverless vehicle that could navigate a 150-mile route through the unpopulated Mohave Desert. The “winning” vehicle couldn’t even make it eight miles into the course and took hours to go even that far.
Just six years later, however, real-world driving went from being an example of a task that couldn’t be automated to an example of one that had. In October of 2010, Google announced on its official blog that it had modified a fleet of Toyota Priuses to the point that they were fully autonomous cars, ones that had driven more than 1,000 miles on American roads without any human involvement at all, and more than 140,000 miles with only minor inputs from the person behind the wheel. (To comply with driving laws, Google felt that it had to have a person sitting behind the steering wheel at all times).
Levy and Murnane were correct that automatic driving on populated roads is an enormously difficult task, and it’s not easy to build a computer that can substitute for human perception and pattern matching in this domain. Not easy, but not impossible either—this challenge has largely been met.
The Google technologists succeeded not by taking any shortcuts around the challenges listed by Levy and Murnane, but instead by meeting them head-on. They used the staggering amounts of data collected for Google Maps and Google Street View to provide as much information as possible about the roads their cars were traveling. Their vehicles also collected huge volumes of real-time data using video, radar, and LIDAR (light detection and ranging) gear mounted on the car; these data were fed into software that takes into account the rules of the road, the presence, trajectory, and likely identity of all objects in the vicinity, driving conditions, and so on. This software controls the car and probably provides better awareness, vigilance, and reaction times than any human driver could. The Google vehicles’ only accident came when the driverless car was rear-ended by a car driven by a human driver as it stopped at a traffic light.
None of this is easy. But in a world of plentiful accurate data, powerful sensors, and massive storage capacity and processing power, it is possible. This is the world we live in now. It’s one where computers improve so quickly that their capabilities pass from the realm of science fiction into the everyday world not over the course of a human lifetime, or even within the span of a professional’s career, but instead in just a few years.
Translating from one human language to another, for example, has long been a goal of computer science researchers, but progress has been slow because grammar and vocabulary are so complicated and ambiguous.
In January of 2011, however, the translation services company Lionbridge announced pilot corporate customers for GeoFluent, a technology developed in partnership with IBM. GeoFluent takes words written in one language, such as an online chat message from a customer seeking help with a problem, and translates them accurately and immediately into another language, such as the one spoken by a customer service representative in a different country.
The Google driverless car shows how far and how fast digital pattern recognition abilities have advanced recently. Lionbridge’s GeoFluent shows how much progress has been made in computers’ ability to engage in complex communication. Another technology developed at IBM’s Watson labs, this one actually named Watson, shows how powerful it can be to combine these two abilities and how far the computers have advanced recently into territory thought to be uniquely human.
Where did these overlords come from? How has science fiction become business reality so quickly? Two concepts are essential for understanding this remarkable progress. The first, and better known, is Moore’s Law, which is an expansion of an observation made by Gordon Moore, co-founder of microprocessor maker Intel. In a 1965 article in Electronics Magazine, Moore noted that the number of transistors in a minimum-cost integrated circuit had been doubling every 12 months, and predicted that this same rate of improvement would continue into the future. When this proved to be the case, Moore’s Law was born. Later modifications changed the time required for the doubling to occur; the most widely accepted period at present is 18 months. Variations of Moore’s Law have been applied to improvement over time in disk drive capacity, display resolution, and network bandwidth. In these and many other cases of digital improvement, doubling happens both quickly and reliably.
It also seems that software progresses at least as fast as hardware does, at least in some domains. Computer scientist Martin Grötschel analyzed the speed with which a standard optimization problem could be solved by computers over the period 1988-2003. He documented a 43 millionfold improvement, which he broke down into two factors: faster processors and better algorithms embedded in software. Processor speeds improved by a factor of 1,000, but these gains were dwarfed by the algorithms, which got 43,000 times better over the same period.
The second concept relevant for understanding recent computing advances is closely related to Moore’s Law. It comes from an ancient story about math made relevant to the present age by the innovator and futurist Ray Kurzweil. In one version of the story, the inventor of the game of chess shows his creation to his country’s ruler. The emperor is so delighted by the game that he allows the inventor to name his own reward. The clever man asks for a quantity of rice to be determined as follows: one grain of rice is placed on the first square of the chessboard, two grains on the second, four on the third, and so on, with each square receiving twice as many grains as the previous.
The emperor agrees, thinking that this reward was too small. He eventually sees, however, that the constant doubling results in tremendously large numbers. The inventor winds up with 264-1 grains of rice, or a pile bigger than Mount Everest. In some versions of the story the emperor is so displeased at being outsmarted that he beheads the inventor.
In his 2000 book The Age of Spiritual Machines: When Computers Exceed Human Intelligence, Kurzweil notes that the pile of rice is not that exceptional on the first half of the chessboard:
After thirty-two squares, the emperor had given the inventor about 4 billion grains of rice. That’s a reasonable quantity—about one large field’s worth—and the emperor did start to take notice.
But the emperor could still remain an emperor. And the inventor could still retain his head. It was as they headed into the second half of the chessboard that at least one of them got into trouble.
Kurzweil’s point is that constant doubling, reflecting exponential growth, is deceptive because it is initially unremarkable. Exponential increases initially look a lot like standard linear ones, but they’re not. As time goes by—as we move into the second half of the chessboard—exponential growth confounds our intuition and expectations. It accelerates far past linear growth, yielding Everest-sized piles of rice and computers that can accomplish previously impossible tasks.
During the Great Recession, nearly 1 in 12 people working in sales in America lost their job, accelerating a trend that had begun long before. In 1995, for example, 2.08 people were employed in “sales and related” occupations for every $1 million of real GDP generated that year. By 2002 (the last year for which consistent data are available), that number had fallen to 1.79, a decline of nearly 14 percent.
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