Machines of Loving Grace (8 page)

Today, partially autonomous cars are already appearing on the market, and they offer two paths toward the future of transportation—one with smarter and safer human drivers and one in which humans will become passengers.

G
oogle had not disclosed how it planned to commercialize its research, but by the end of 2013 more than a half-dozen automakers had already publicly stated their intent to offer autonomous vehicles.
Indeed, 2014 was the year that the line was first crossed commercially when a handful of European car manufacturers including BMW, Mercedes, Volvo, and Audi announced an optional feature—traffic jam assist, the first baby step toward autonomous driving.
In Audi’s case, while on the highway, the car will drive autonomously when traffic is moving at less than forty miles per hour, staying in its lane and requiring driver intervention only as dictated by lawyers fearful that passengers might go to sleep or otherwise distract
themselves.
In late 2014 Tesla announced that it would begin to offer an “autopilot” system for its Model S, making the car self-driving in some highway situations.

The autonomous car will sharpen the dilemma raised by the AI versus IA dichotomy.
While there is a growing debate over the liability issue—who will pay when the first human is killed by a robot car—the bar that the cars must pass to improve safety is actually incredibly low.
In 2012 a National Highway Transportation Safety Administration study estimated that the deployment of electronic stability control (ESC) systems in light vehicles alone would save almost ten thousand lives and prevent almost a quarter million injuries.
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Driving, it would seem, might be one area of life where humans should be taken out of the loop to the greatest degree possible.
Even unimpaired humans are not particularly good drivers, and we are worse when distracted by the gadgets that increasingly surround us.
We will be saved from ourselves by a generation of cheap cameras, radars, and lidars that, when coupled with pattern-sensing computers, will wrap an all-seeing eye around our cars, whether we are driving or are being driven.

F
or Amnon Shashua, the aha moment came while seated in a university library as a young computer science undergraduate in Jerusalem.
Reading an article written in Hebrew by Shimon Ullman, who had been the first Ph.D.
student under David Marr, a pioneer in vision research, he was thrilled to discover that the human retina was in many ways a computer.
Ullman was a computer scientist who specialized in studying vision in both humans and machines.
The realization that computing was going on inside the eye fascinated Shashua and he decided to follow in Ullman’s footsteps.

He arrived at MIT in 1996 to study artificial intelligence when the field was still recovering from an earlier cycle of boom-and-bust.
Companies had tried to build commercial
expert systems based on the rules and logic approach of early artificial intelligence pioneers like Ed Feigenbaum and John McCarthy.
In the heady early days of AI it had seemed that it would be straightforward to simply bottle the knowledge of a human expert, but the programs were fragile and failed in the marketplace, leading to the collapse of a number of ambitious start-ups.
Now the AI world was rebounding.
Progress in AI, which had been relatively stagnant for its first three decades, finally took off during the 1990s because statistical techniques made classification and decision-making practical.
AI experiments hadn’t yet seen great results because the computers of the era were still relatively underpowered for the data at hand.
The new ideas, however, were in the air.

As a graduate student Shashua would focus on a promising approach to visually recognizing objects based on imaging them from multiple views to capture their geometry.
The approach was derived from the world of computer graphics, where Martin Newell had pioneered a new modeling approach as a graduate student at the University of Utah—which was where much of computer graphics was invented during the 1970s.
A real Melitta teapot found in his kitchen inspired Newell’s approach.
One day, as he was discussing the challenges of modeling objects with his wife over tea, she suggested that he model that teapot, which thereafter became an iconic image in the early days of computer graphics research.

At MIT, Shashua studied under computer vision scientists Tommy Poggio and Eric Grimson.
Poggio was a scientist who stood between the worlds of computing and neuroscience and Grimson was a computer scientist who would later become MIT’s chancellor.
At the time there seemed to be a straight path from capturing shapes to recognizing them, but programming the recognition software would actually prove daunting.
Even today the holy grail of “scene understanding”—for example, not only identifying a figure as a woman but also identifying what she might be doing—is still largely beyond reach, and significant
progress has been made only in niche industries.
For example, many cars can now identify pedestrians or bicyclists in time to automatically slow before a collision.

Shashua would become one of the masters in pragmatically carving out those niches.
In an academic world where brain scientists debated computational scientists, he would ally himself with a group who took the position that “just because airplanes don’t flap their wings, it doesn’t mean they can’t fly.”
After graduate school he moved back to Israel.
He had already founded a successful company, Cognitens, using vision modeling to create incredibly accurate three-dimensional models of parts for industrial applications.
The images, accurate to hair-thin tolerances, gave manufacturers ranging from automotive to aerospace the ability to create digital models of existing parts, enabling checking their fit and finish.
The company was quickly sold.

Looking around for another project, Shashua heard from a former automotive industry customer about an automaker searching for stereovision technology for computer-assisted driving.
They knew about Shashua’s work in multiple-view geometry and asked if he had ideas for stereovision.
He responded, “Well, that’s fine but you don’t need a stereo system, you can do it with a single camera.”
Humans can tell distances with one eye shut under some circumstances, he pointed out.

The entrepreneurial Shashua persuaded General Motors to invest $200,000 to develop demonstration software.
He immediately called a businessman friend, Ziv Aviram, and proposed that they start a new company.
“There is an opportunity,” he told his friend.
“This is going to be a huge field and everybody is thinking about it in the wrong way and we already have a customer, somebody who is willing to pay money.”
They called the new company Mobileye and Shashua wrote software for the demonstration on a desktop computer, soon showing one-camera machine vision that seemed like science fiction to the automakers at that time.

Six months after starting the project, Shashua heard from a large auto industry supplier that General Motors was about to offer a competitive bid for a way to warn drivers that the vehicle was straying out of its lane.
Until then Mobileye had been focusing on far-out problems like vehicle and pedestrian detection that the industry thought weren’t solvable.
However, the parts supplier advised Shashua, “You should do something now.
It’s important to get some real estate inside the vehicle, then you can build more later.”

The strategy made sense to Shashua, and so he put one of his Hebrew University students on the project for a couple of months.
The lane-keeping software demonstration wasn’t awful, but he realized it probably wasn’t as good as what companies who’d started earlier could show, so there was virtually no way that the fledgling company would win.

Then he had a bright idea.
He added vehicle detection to the software, but he told GM that the capability was a bug and that they shouldn’t pay attention.
“It will be taken out in the next version, so ignore it,” he said.
That was enough.
GM was ecstatic about the safety advance that would be made possible by the ability to detect other vehicles at low cost.
The automaker immediately canceled the bidding and committed to fund the novice firm’s project developments.
Vehicle detection would facilitate a new generation of safety features that didn’t replace drivers, but rather augmented them with an invisible sensor and computer safety net.
Technologies like lane departure warning, adaptive cruise control, forward collision warning, and anticollision braking are now rapidly moving toward becoming standard safety systems on cars.

Mobileye would grow into one of the largest international suppliers of AI vision technology for the automotive industry, but Shashua had bigger ideas.
After creating Cognitens and Mobileye, he took a postdoctoral year at Stanford in 2001 and shared an office with Sebastian Thrun.
Both men would eventually pioneer autonomous driving.
Shashua would pursue the
same technologies as Thrun, but with a more pragmatic, less “moon shot” approach.
He had been deeply influenced by Poggio, who pursued biological approaches to vision, which were alternatives to using the brute force of increasingly powerful computers to recognize objects.

The statistical approach to computing would ultimately work best when both powerful clusters of computers, such as Google’s cloud, and big data sets were available.
But what if you didn’t have those resources?
This is where Shashua would excel.
Mobileye had grown to become a uniquely Israeli technology firm, located in Jerusalem, close to Hebrew University, where Shashua teaches computer science.
A Mobileye-equipped Audi served as a rolling research platform.
Unlike the Google car, festooned with sensors, from the outside the Mobileye Audi looked normal, apart from a single video camera mounted unobtrusively just in front of the rearview mirror in the center of the windshield.
The task at hand—automatic driving—required powerful computers, hidden in the car’s trunk, with some room left over for luggage.

Like Google, Mobileye has significant ambitions that are still only partially realized.
On a spring afternoon in 2013, two Mobileye engineers, Gaby Hayon and Eyal Bagon, drove me several miles east of Jerusalem on Highway 1 until they pulled off at a nondescript turnout where another employee waited in a shiny white Audi A7.
As we got in the A7 and prepared for a test drive, Gaby and Eyal apologized to me.
The car was a work in progress, they explained.
Today Mobileye supplies computer vision technology to automakers like BMW, Volvo, Ford, and GM for safety applications.
The company’s third-generation technology is touted as being able to detect pedestrians and cyclists.
Recently, Nissan gave a hint of things to come, demonstrating a car that automatically swerved to avoid a pedestrian walking out from behind a parked car.

Like Google, the Israelis are intent on going further, developing the technology necessary for autonomous driving.
But
while Google might decide to compete with the automobile industry by partnering with an upstart like Tesla, Shashua is exquisitely sensitive to the industry culture exemplified by its current customers.
That means that his vision system designs must cost no more than several hundred dollars for even a premium vehicle and less than a hundred for a standard Chevy.

Google and Mobileye have taken very different approaches to solving the problem of making a car aware of its surroundings with better-than-human precision at highway speeds.
Google’s system is based on creating a remarkably detailed map of the world around the car using radars, video, and a Velodyne lidar, all at centimeter accuracy, augmenting the data it collects using its Street View vehicles.
The Google car connects to the map database via a wireless connection to the Google cloud.
The network is an electronic crutch for the car’s navigation system, confirming what the local sensors are seeing around the car.

The global map database could make things easier for Google.
One of the company’s engineers confided that when the project got under way the Google team was surprised to find how dynamic the world is.
Not only do freeway lanes frequently come and go for maintenance reasons, but “whole bridges will move,” he said.
Even without the database, the Google car is able to do things that might seem to be the province of humans alone, such as seamlessly merging into highway traffic and handling stop-and-go traffic in a dense urban downtown.

Google has conducted its project with a mix of Thrun’s German precision and the firm’s penchant for secrecy.
The Israelis are more informal.
On that hot spring afternoon in suburban Jerusalem there was little caution on the part of the Mobileye engineers.
“Why don’t you drive?”
Eyal suggested to me, as he slid into the passenger seat behind a large display and keyboard.
The engineers proceeded to give a rapid-fire minute-long lesson on driving a robot car: You simply turn on cruise control and then add the lane-keeping feature—steering—by
pulling the cruise control stick on the steering wheel toward you.
A heads-up display projected on the windshield showed the driver the car’s speed and an icon indicated that the autonomous driving feature was on.