Lee Gomes

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Brad Templeton is a consultant for Google’s driverless-cars division, so he has a dog–a car–in this race, but I think his response to Lee Gomes’ recent Slate piece about the disappointment of autonomous cars, the idea that they may never be viable, is pretty reasonable. Vehicles will be incrementally more robotic and independent in the near-term, offering automated parking and autopilot on highways, and in the longer term, when the final 5% of the process is figured and infrastructure is retrofitted, their function will be even greater. From Templeton:

“Fully functional robocars that can drive almost everywhere are not coming this decade, but nor are they many decades away. But more to the point, less-functional robocars are probably coming this decade — much sooner than these articles expect, and these vehicles are much more useful and commercially viable than people may expect.

There are many challenges facing developers, and those challenges will keep them busy refining products for a long time to come. Most of those challenges either already have a path to solution, or constrain a future vehicle only in modest ways that still allow it to be viable. Some of the problems are in the ‘unsolved’ class. It is harder to predict when those solutions will come, of course, but at the same time one should remember that many of the systems in today’s research vehicles where in this class just a few years ago. Tackling hard problems is just what these teams are good at doing. This doesn’t guarantee success, but neither does it require you bet against it.

And very few of the problems seem to be in the ‘unsolvable without human-smart AI’ class, at least none that bar highly useful operation.”

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An Economist article responds to Lee Gomes’ Slate piece about the difficulty of making driverless vehicles truly autonomous, suggesting that the real impediment to such machines might be the large stock of cheap labor created by the disruptive qualities of other technologies. An excerpt:

“Writing at Slate, for instance, Lee Gomes frets that driverless vehicles struggle in unfamiliar territory when they lack good maps, can make errors when sun blinds their cameras, and are occasionally caught out by the unexpected appearance of new traffic signals. Human drivers, of course, share these weaknesses, and others: like difficulty operating in adverse weather conditions. The big difference between driverless vehicles and humans, in these cases, is that the computer can be programmed to behave cautiously when stumped, while humans often plough ahead heedlessly. When critiquing driverless cars it is often useful to recall that human drivers kill and maim millions of people each year.

Ironically, the biggest obstacle to widespread use of driverless vehicles, over the next decade or two at any rate, may be the effects of rapid technological progress in other parts of the economy. As a recent special report explains, technological change over the last generation has wiped out many middle-skill jobs, pushing millions of workers into competition for low-wage work. That glut has contributed to stagnant wages for most workers, and low pay has in turn reduced the incentive to firms to deploy labour-saving technology. Why automate, when there is an enormous stock of cheap labour available? At the same time firms like Uber are making the use of hired cars cheaper and more convenient, reducing the attraction to many households of owning and driving their own personal vehicles.

The combination of Uber and cheap labour could pose a formidable threat to the driverless car. The cost of the sensors and processors needed to pilot an autonomous vehicle is falling and is likely to fall much more as production ramps up. Yet the technology is still pricey, especially compared with a human, which, after all, is a rather efficient package of sensory and information-processing equipment. At low wages, a smartphone-enabled human driver is formidable competition for a driverless vehicle.

It would be a remarkable irony if the driverless car—in many ways the symbol of the technological revolution that is now reshaping modern economies—fails to materialise as an economic reality thanks to the disemploying power of other technologies.”


In a Slate piece, Lee Gomes wonders whether the Google driverless car will ever be a reality, one impediment being the need for real-time maps able to read constantly shifting infrastructure on a national level. His comparison of the search-giant’s autonomous-vehicle plans to the Apple Newton seems a self-defeating argument, however, since all the elements of that ill-fated invention were realized soon thereafter in other tools. The opening:

“A good technology demonstration so wows you with what the product can do that you might forget to ask about what it can’t. Case in point: Google’s self-driving car. There is a surprisingly long list of the things the car can’t do, like avoid potholes or operate in heavy rain or snow. Yet a consensus has emerged among many technologists, policymakers, and journalists that Google has essentially solved—or is on the verge of solving—all of the major issues involved with robotic driving. The Economist believes that ‘the technology seems likely to be ready before all the questions of regulation and liability have been sorted out.’ The New York Times declared that ‘autonomous vehicles like the one Google is building will be able to pack roads more efficiently’—up to eight times so. Google co-founder Sergey Brin forecast in 2012 that self-driving cars would be ready in five years, and in May, said he still hoped that his original prediction would come true.

But what Google is working on may instead result in the automotive equivalent of the Apple Newton, what one Web commenter called a ‘timid, skittish robot car whose inferior level of intelligence becomes a daily annoyance.’ To be able to handle the everyday stresses and strains of the real driving world, the Google car will require a computer with a level of intelligence that machines won’t have for many years, if ever.”


Autonomous vehicles are probably not as close as we’d like nor as far in the distance as we might believe, but they’re not road-ready yet and hurdling those last few obstacles may be more difficult than all the ones that came before them. From Lee Gomes at Technology Review:

“Would you buy a self-driving car that couldn’t drive itself in 99 percent of the country? Or that knew nearly nothing about parking, couldn’t be taken out in snow or heavy rain, and would drive straight over a gaping pothole?

If your answer is yes, then check out the Google Self-Driving Car, model year 2014.

Of course, Google isn’t yet selling its now-famous robotic vehicle and has said that its technology will be thoroughly tested before it ever does. But the car clearly isn’t ready yet, as evidenced by the list of things it can’t currently do—volunteered by Chris Urmson, director of the Google car team.

Google’s cars have safely driven more than 700,000 miles. As a result, ‘the public seems to think that all of the technology issues are solved,’ says Steven Shladover, a researcher at the University of California, Berkeley’s Institute of Transportation Studies. ‘But that is simply not the case.’

No one knows that better than Urmson. But he says he is optimistic about tackling outstanding challenges and that it’s ‘going to happen more quickly than many people think.'”

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