🚘 Beyond the hype: Self-driving cars shifting into high(er) gear
Robotaxis can be found in more and more places in more and more cities.
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🚘 Beyond the hype: Self-driving cars shifting into high(er) gear
Item: California regulators voted in favor of robotaxi operators expanding their paid driverless services in the city of San Francisco, a major milestone toward commercializing the technology. The state’s Public Utilities Commission voted 3 to 1 to allow General Motors’ Cruise and Alphabet’s Waymo to increase the areas of the city where they can operate a car without a safety driver, and charge riders a fare for it. … Robotaxis have increasingly become a normal sight on the Northern California city’s roads, with Waymo running a fleet of about 200 cars. Such services are currently limited in where they can drive, and the companies typically can’t charge passengers. Cruise has 300 cars across three cities — San Francisco, Austin and Phoenix — averaging 1,000 trips a day. Both services have thousands of individuals on waiting lists to try them out. - Bloomberg, August 11, 2023
Twenty-first-century fears about technological automation didn’t begin this past November when OpenAI launched ChatGPT, the large language model-based chatbot. In the decade before the COVID-19 pandemic, there was increasing concern about “robots stealing all the jobs” — well, at least once we all stopped worrying about global financial collapse.
Those worries, I would argue, began when Google announced in an Oct. 9, 2010, blog post that the company had “developed technology for cars that can drive themselves. … While this project is very much in the experimental stage, it provides a glimpse of what transportation might look like in the future thanks to advanced computer science. And that future is very exciting.”
But not everyone saw it as “very exciting.” It didn’t take much nudging for a society soaked in techno-dystopianism to begin obsessing about the downsides to autonomous vehicle technology and, more broadly, artificial intelligence and robotics. If a car could accomplish one of the seemingly most difficult tasks that humans do, surely there would be many other human activities — and jobs — these brilliant machines could also do. It wasn’t just the long-haul truckers who had to worry.
What’s more, our imaginative speculations were bolstered by scholarship. In 2013, two Oxford scholars, Carl Benedikt Frey and Michael Osborne, concluded that 47 percent of employment in America is at high risk of being automated away over the next two decades.” It was a forecast that seemed even more reasonable after Elon Musk in 2015 predicted that Tesla cars would drive themselves in two years. (In 2019, Musk suggested that Tesla would have a million robo-taxis operating on public roads by the end of 2020.) Such predictions, and our fear of them, are what helped Andrew Yang gain a bit of presidential buzz in 2019 with his dire warnings about a jobless future and the solution of a universal basic income:
All you need is self-driving cars to destabilize society. ... That one innovation will be enough to create riots in the street. And we’re about to do the same thing to retail workers, call center workers, fast-food workers, insurance companies, accounting firms.
But self-driving technology has proven to be a tougher problem to solve than thought back in 2010. There are not, you may have noticed, a million Tesla robo-taxis cruising along highways and through the downtowns of big U.S. cities. Just a year ago, there were plenty of pessimistic headlines:
“Even After $100 Billion, Self-Driving Cars Are Going Nowhere” - Bloomberg Businessweek
“Slow Self-Driving Car Progress Tests Investors’ Patience” - The Wall Street Journal
“As Driverless Cars Falter, Are ‘Driver Assistance’ Systems in Closer Reach?” - The New York Times
“The problem with self-driving cars? Many don’t drive themselves.” - The Washington Post
There’s a lesson here, one we should remember when thinking about generative AI systems such as ChatGPT. New technology can generate lots of hype followed by lots of disappointment. But that doesn’t mean the technology is junk or will never fulfill anywhere near its original promise. Oftentimes, more tech innovation, along with user learning and investment, are needed. This is exactly the sort of phenomenon we’ve been seeing with self-driving tech. From The Economist in April:
Cruise, a subsidiary of GM, is adding paid driverless rides in Phoenix, Arizona, and Austin, Texas, to the service that it launched for the public in San Francisco in February 2022. Waymo, the self-driving arm of Alphabet, Google’s parent company, operates in Phoenix and San Francisco. Uber’s app with vehicles from Motional, a joint venture between South Korea’s Hyundai and Aptiv, an American supplier, allows the hailing of self-driving rides in Las Vegas. Amazon is running robotaxis between its offices in San Francisco, operated by Zoox, a self-driving startup that it bought for $1.3bn in 2020. In China Baidu, a tech firm, is operating similar services in several cities. Didi, a ride-hailing giant, and WeRide, an av startup that has teamed up with gac, a car firm, are testing out robotaxis in some Chinese cities.
Since that piece came out, there’s been continued incremental expansion. Waymo has expanded its autonomous taxi service in San Francisco and the greater Phoenix area, while Cruise has expanded its robotaxi service to Houston and Dallas. Waymo also recently announced it will expand its autonomous rideshares to Austin, Texas, its fourth city. And now the big news about full expansion in San Francisco.
As tech journalist Timothy Lee told me last year about the future of self-driving vehicles:
Pethokoukis: Autonomous cars may not have lived up to expectations, but there are actual self-driving cars on the road in America today. If our previous expectations about this rapid deployment were wrong, what do you think are reasonable expectations for the next five to 10 years? Will it move beyond being a limited area, almost experimental thing to something widely used?
Lee: I think it will. I think there's a lot of uncertainty about the timeline because as we've seen, it's hard to predict. The two leading companies are Waymo and Cruise, and both of them are planning to grow by a factor of like 10x to 100x over the next two or three years. I think that's probably too optimistic. Currently they're in a couple of cities. I would expect to see that number grow significantly, maybe a dozen cities in two or three years and dozens or hundreds of cities in five to 10 years, something like that. There's a lot of uncertainty about the exact timeline, but if you go to Phoenix there are real driverless cars that can take you around. It's a 180 square mile area of Phoenix that includes downtown Phoenix and gets you to the Sky Train that gets you to the Phoenix airport. So that's like a genuinely useful taxi service. I don't think it's profitable yet. I think that most of the technical problems have been solved, and a lot of it is just kind of making the economics work, getting the costs down, and then doing the logistics of rolling out to new cities.
So not a “gradually then suddenly” sort of thing as with going bankrupt, but gradual steps that add up to something significant over time — although not that much time, really, given the Google blog post was just a decade ago.
Incremental progress but progress nonetheless. And let’s remember what we are progressing toward. As Manhattan Institute Jordan McGillis explains in a new report, “Autonomous Now: Why We Need Self-Driving Technology and How We Can Get It Faster,” the potential for AVs to improve road safety and reduce transportation costs remains pretty compelling. AVs equipped with autonomous driving systems are already safer than human drivers and have the potential to significantly reduce the more than 40,000 annual deaths caused by motor-vehicle collisions in the ... The adoption of AVs also promises cost savings due to reduced labor needs, resulting in lower ride-hailing and trucking expenses. The report emphasizes that AVs with Level 4 automation, capable of autonomous driving within specific environments, hold immense potential for safety enhancement and economic benefits. Among his policy suggestions:
To support successful [autonomous ride-hailing], U.S. cities should: (1) allocate curb space to ride-hailing loading and unloading; (2) allow autonomous ride-hails to use preferred lanes; and (3) implement congestion pricing.
State governments should: (1) devote a state policy office to AVs; (2) license ADS similarly to the licensing of human drivers; and (3) invest in quality infrastructure that facilitates autonomous technology without prematurely committing to vehicle-to-infrastructure (V2I) technology.
The federal government should: (1) raise or eliminate AV manufacturing caps; (2) address problems as they arise, rather than before they arise; and (3) use the power of federal highway funding to induce state receptivity to autonomous trucks.
Give the tremendous benefits from mass AV adoption, this is truly a “Faster, Please!” situation.
▶ Artificial Intelligence and Scientific Discovery: A Model of Prioritized Search - Ajay K. Agrawal, John McHale & Alexander Oettl, NBER | The complexity of the new research frontiers provides one explanation for disappointing recent productivity growth despite exponentially increasing resources invested in R&D. Innovators lack the kinds of science-based predictive theories to search these spaces that underpinned past scientific advances, most notably the predictive tools provided by 19th and 20th century advances in theoretical physics. Many of today’s greatest innovation challenges are in the domain of biology – small molecule-protein binding, gene expression, protein structure prediction, protein design, etc. Scientists hope that AI-based predictive models will provide useful tools to guide search at these challenging frontiers.
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