The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies (5 page)

This situation has come to be known as Moravec’s paradox, nicely summarized by Wikipedia as “the discovery by artificial intelligence and robotics researchers that, contrary to traditional assumptions, high-level reasoning requires very little computation, but low-level sensorimotor skills require enormous computational resources.”
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Moravec’s insight is broadly accurate, and important. As the cognitive scientist Steven Pinker puts it, “The main lesson of thirty-five years of AI research is that the hard problems are easy and the easy problems are hard. . . . As the new generation of intelligent devices appears, it will be the stock analysts and petrochemical engineers and parole board members who are in danger of being replaced by machines. The gardeners, receptionists, and cooks are secure in their jobs for decades to come.”
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Pinker’s point is that robotics experts have found it fiendishly difficult to build machines that match the skills of even the least-trained manual worker. iRobot’s Roomba, for example, can’t do everything a maid does; it just vacuums the floor. More than ten million Roombas have been sold, but none of them is going to straighten the magazines on a coffee table.

When it comes to work in the physical world, humans also have a huge flexibility advantage over machines. Automating a single activity, like soldering a wire onto a circuit board or fastening two parts together with screws, is pretty easy, but that task must remain constant over time and take place in a ‘regular’ environment. For example, the circuit board must show up in exactly the same orientation every time. Companies buy specialized machines for tasks like these, have their engineers program and test them, then add them to their assembly lines. Each time the task changes—each time the location of the screw holes move, for example—production must stop until the machinery is reprogrammed. Today’s factories, especially large ones in high-wage countries, are highly automated, but they’re not full of general-purpose robots. They’re full of dedicated, specialized machinery that’s expensive to buy, configure, and reconfigure.

Rethinking Factory Automation

Rodney Brooks, who co-founded iRobot, noticed something else about modern, highly automated factory floors: people are scarce, but they’re not absent. And a lot of the work they do is repetitive and mindless. On a line that fills up jelly jars, for example, machines squirt a precise amount of jelly into each jar, screw on the top, and stick on the label, but a person places the empty jars on the conveyor belt to start the process. Why hasn’t this step been automated? Because in this case the jars are delivered to the line twelve at a time in cardboard boxes that don’t hold them firmly in place. This imprecision presents no problem to a person (who simply sees the jars in the box, grabs them, and puts them on the conveyor belt), but traditional industrial automation has great difficulty with jelly jars that don’t show up in exactly the same place every time.

In 2008 Brooks founded a new company, Rethink Robotics, to pursue and build
un
traditional industrial automation: robots that can pick and place jelly jars and handle the countless other imprecise tasks currently done by people in today’s factories. His ambition is to make some progress against Moravec’s paradox. What’s more, Brooks envisions creating robots that won’t need to be programmed by high-paid engineers; instead, the machines can be taught to do a task (or retaught to do a new one) by shop floor workers, each of whom need less than an hour of training to learn how to instruct their new mechanical colleagues. Brooks’s machines are cheap, too. At about $20,000, they’re a small fraction of the cost of current industrial robots. We got a sneak peek at these potential paradox-busters shortly before Rethink’s public unveiling of their first line of robots, named Baxter. Brooks invited us to the company’s headquarters in Boston to see these automatons, and to see what they could do.

Baxter is instantly recognizable as a humanoid robot. It has two burly, jointed arms with claw-like grips for hands; a torso; and a head with an LCD face that swivels to ‘look at’ the nearest person. It doesn’t have legs, though; Rethink sidestepped the enormous challenges of automatic locomotion by putting Baxter on wheels and having it rely on people to get from place to place. The company’s analyses suggest that it can still do lots of useful work without the ability to move under his own power.

To train Baxter, you grab it by the wrist and guide the arm through the motions you want it to carry out. As you do this, the arm seems weightless; its motors are working so you don’t have to. The robot also maintains safety; the two arms can’t collide (the motors resist you if you try to make this happen) and they automatically slow down if Baxter senses a person within their range. These and many other design features make working with this automaton a natural, intuitive, and nonthreatening experience. When we first approached it, we were nervous about catching a robot arm to the face, but this apprehension faded quickly, replaced by curiosity.

Brooks showed us several Baxters at work in the company’s demo area. They were blowing past Moravec’s paradox—sensing and manipulating lots of different objects with ‘hands’ ranging from grips to suction cups. The robots aren’t as fast or fluid as a well-trained human worker at full speed, but they might not need to be. Most conveyor belts and assembly lines do not operate at full human speed; they would tire people out if they did.

Baxter has a few obvious advantages over human workers. It can work all day every day without needing sleep, lunch, or coffee breaks. It also won’t demand healthcare from its employer or add to the payroll tax burden. And it can do two completely unrelated things at once; its two arms are capable of operating independently.

Coming Soon to Assembly Lines, Warehouses, and Hallways Near You

After visiting Rethink and seeing Baxter in action, we understood why Texas Instruments Vice President Remi El-Ouazzane said in early 2012, “We have a firm belief that the robotics market is on the cusp of exploding.” There’s a lot of evidence to support his view. The volume and variety of robots in use at companies is expanding rapidly, and innovators and entrepreneurs have recently made deep inroads against Moravec’s paradox.
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Kiva, another young Boston-area company, has taught its automatons to move around warehouses safely, quickly, and effectively. Kiva robots look like metal ottomans or squashed R2-D2s. They scuttle around buildings at about knee-height, staying out of the way of humans and one another. They’re low to the ground so they can scoot underneath shelving units, lift them up, and bring them to human workers. After these workers grab the products they need, the robot whisks the shelf away and another shelf-bearing robot takes its place. Software tracks where all the products, shelves, robots, and people are in the warehouse, and orchestrates the continuous dance of the Kiva automatons. In March of 2012, Kiva was acquired by Amazon—a leader in advanced warehouse logistics—for more than $750 million in cash.
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Boston Dynamics, yet another New England startup, has tackled Moravec’s paradox head-on. The company builds robots aimed at supporting American troops in the field by, among other things, carrying heavy loads over rough terrain. Its BigDog, which looks like a giant metal mastiff with long skinny legs, can go up steep hills, recover from slips on ice, and do other very dog-like things. Balancing a heavy load on four points while moving over an uneven landscape is a truly nasty engineering problem, but Boston Dynamics has been making good progress.

As a final example of recent robotic progress, consider the Double, which is about as different from the BigDog as possible. Instead of trotting through rough enemy terrain, the Double rolls over cubicle carpets and hospital hallways carrying an iPad. It’s essentially an upside-down pendulum with motorized wheels at the bottom and a tablet at the top of a four- to five-foot stick. The Double provides telepresence—it lets the operator ‘walk around’ a distant building and see and hear what’s going on. The camera, microphone, and screen of the iPad serve as the eyes, ears, and face of the operator, who sees and hears what the iPad sees and hears. The Double itself acts as the legs, transporting the whole assembly around in response to commands from the operator. Double Robotics calls it “the simplest, most elegant way to be somewhere else in the world without flying there.” The first batch of Doubles, priced at $2,499, sold out soon after the technology was announced in the fall of 2012.
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The next round of robotic innovation might put the biggest dent in Moravec’s paradox ever. In 2012 DARPA announced another Grand Challenge; instead of autonomous cars, this one was about automatons. The DARPA Robotics Challenge (DRC) combined tool use, mobility, sensing, telepresence, and many other long-standing challenges in the field. According to the website of the agency’s Tactical Technology Office,

The primary technical goal of the DRC is to develop ground robots capable of executing complex tasks in dangerous, degraded, human-engineered environments. Competitors in the DRC are expected to focus on robots that can use standard tools and equipment commonly available in human environments, ranging from hand tools to vehicles, with an emphasis on adaptability to tools with diverse specifications.
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With the DRC, DARPA is asking the robotics community to build and demonstrate high-functioning humanoid robots by the end of 2014. According to an initial specification supplied by the agency, they will have to be able to drive a utility vehicle, remove debris blocking an entryway, climb a ladder, close a valve, and replace a pump.
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These seem like impossible requirements, but we’ve been assured by highly knowledgeable colleagues—ones competing in the DRC, in fact—that they’ll be met. Many saw the 2004 Grand Challenge as instrumental in accelerating progress with autonomous vehicles. There’s an excellent chance that the DRC will be similarly important at getting us past Moravec’s paradox.

More Evidence That We’re at an Inflection Point

Self-driving cars,
Jeopardy!
champion supercomputers, and a variety of useful robots have all appeared just in the past few years. And these innovations are not just lab demos; they’re showing off their skills and abilities in the messy real world. They contribute to the impression that we’re at an inflection point—a bend in the curve where many technologies that used to be found only in science fiction are becoming everyday reality. As many other examples show, this is an accurate impression.

On the
Star Trek
television series, devices called tricorders were used to scan and record three kinds of data: geological, meteorological, and medical. Today’s consumer smartphones serve all these purposes; they can be put to work as seismographs, real-time weather radar maps, and heart- and breathing-rate monitors.
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And, of course, they’re not limited to these domains. They also work as media players, game platforms, reference works, cameras, and GPS devices. On
Star Trek
, tricorders and person-to-person communicators were separate devices, but in the real world the two have merged in the smartphone. They enable their users to simultaneously access and generate huge amounts of information as they move around. This opens up the opportunity for innovations that venture capitalist John Doerr calls “SoLoMo”—social, local, and mobile.
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Computers historically have been very bad at writing real prose. In recent times they have been able to generate grammatically correct but meaningless sentences, a state of affairs that’s been mercilessly exploited by pranksters. In 2008, for example, the International Conference on Computer Science and Software Engineering accepted the paper “Towards the Simulation of E-commerce” and invited its author to chair a session. This paper was ‘written’ by SCIgen, a program from the MIT Computer Science and Artificial Intelligence Lab that “generates random Computer Science research papers.” SCIgen’s authors wrote that, “Our aim here is to maximize amusement, rather than coherence,” and after reading the abstract of “Towards the Simulation of E-commerce” it’s hard to argue with them:
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Recent advances in cooperative technology and classical communication are based entirely on the assumption that the Internet and active networks are not in conflict with object-oriented languages. In fact, few information theorists would disagree with the visualization of DHTs that made refining and possibly simulating 8 bitarchitectures a reality, which embodies the compelling principles of electrical engineering.
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Recent developments make clear, though, that not all computer-generated prose is nonsensical. Forbes.com has contracted with the company Narrative Science to write the corporate earnings previews that appear on the website. These stories are all generated by algorithms without human involvement. And they’re indistinguishable from what a human would write:

Forbes Earning Preview: H.J. Heinz

A quality first quarter earnings announcement could push shares of H.J. Heinz (HNZ) to a new 52-week high as the price is just 49 cents off the milestone heading into the company’s earnings release on Wednesday, August 29, 2012.

The Wall Street consensus is 80 cents per share, up 2.6 percent from a year ago when H.J reported earnings of 78 cents per share.

The consensus estimate remains unchanged over the past month, but it has decreased from three months ago when it was 82 cents. Analysts are expecting earnings of $3.52 per share for the fiscal year. Analysts project revenue to fall 0.3 percent year-over-year to $2.84 billion for the quarter, after being $2.85 billion a year ago. For the year, revenue is projected to roll in at $11.82 billion.
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Even computer peripherals like printers are getting in on the act, demonstrating useful capabilities that seem straight out of science fiction. Instead of just putting ink on paper, they are making complicated three-dimensional parts out of plastic, metal, and other materials. 3D printing, also sometimes called “additive manufacturing,” takes advantage of the way computer printers work: they deposit a very thin layer of material (ink, traditionally) on a base (paper) in a pattern determined by the computer.