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Self-Driving Cars: The Complete Guide

Self-Driving Cars: The Complete Guide

In the past decade, autonomous driving has gone from “maybe possible” to “definitely possible” to “inevitable” to “how did anyone ever think this wasn’t inevitable?” to “now commercially available.” In December 2018, Waymo, the company that emerged from Google’s self-driving-car project, officially started its commercial self-driving-car service in the suburbs of Phoenix. At first, the program was underwhelming: available only to a few hundred vetted riders, and human safety operators remained behind the wheel. But in the past four years, Waymo has slowly opened the program to members of the public and has begun to run robotaxis without drivers inside. The company has since brought its act to San Francisco. People are now paying for robot rides.

And it’s just a start. Waymo says it will expand the service’s capability and availability over time. Meanwhile, its onetime monopoly has evaporated. Every significant automaker is pursuing the tech, eager to rebrand and rebuild itself as a “mobility provider. Amazon bought a self-driving-vehicle developer, Zoox. Autonomous trucking companies are raking in investor money. Tech giants like Apple, IBM, and Intel are looking to carve off their slice of the pie. Countless hungry startups have materialized to fill niches in a burgeoning ecosystem, focusing on laser sensors, compressing mapping data, setting up service centers, and more.

This 21st-century gold rush is motivated by the intertwined forces of opportunity and survival instinct. By one account, driverless tech will add $7 trillion to the global economy and save hundreds of thousands of lives in the next few decades. Simultaneously, it could devastate the auto industry and its associated gas stations, drive-thrus, taxi drivers, and truckers. Some people will prosper. Most will benefit. Some will be left behind.

It’s worth remembering that when automobiles first started rumbling down manure-clogged streets, people called them horseless carriages. The moniker made sense: Here were vehicles that did what carriages did, minus the hooves. By the time “car” caught on as a term, the invention had become something entirely new. Over a century, it reshaped how humanity moves and thus how (and where and with whom) humanity lives. This cycle has restarted, and the term “driverless car” may soon seem as anachronistic as “horseless carriage.” We don’t know how cars that don’t need human chauffeurs will mold society, but we can be sure a similar gear shift is on the way.

SelfDriving Cars The Complete Guide

The First Self-Driving Cars

Just over a decade ago, the idea of being chauffeured around by a string of zeros and ones was ludicrous to pretty much everybody who wasn’t at an abandoned Air Force base outside Los Angeles, watching a dozen driverless cars glide through real traffic. That event was the Urban Challenge, the third and final competition for autonomous vehicles put on by Darpa, the Pentagon’s skunkworks arm.


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At the time, America’s military-industrial complex had already thrown vast sums and years of research trying to make unmanned trucks. It had laid a foundation for this technology, but stalled when it came to making a vehicle that could drive at practical speeds, through all the hazards of the real world. So, Darpa figured, maybe someone else—someone outside the DOD’s standard roster of contractors, someone not tied to a list of detailed requirements but striving for a slightly crazy goal—could put it all together. It invited the whole world to build a vehicle that could drive across California’s Mojave Desert, and whoever’s robot did it the fastest would get a million-dollar prize.

The 2004 Grand Challenge was something of a mess. Each team grabbed some combination of the sensors and computers available at the time, wrote their own code, and welded their own hardware, looking for the right recipe that would take their vehicle across 142 miles of sand and dirt of the Mojave. The most successful vehicle went just seven miles. Most crashed, flipped, or rolled over within sight of the starting gate. But the race created a community of people—geeks, dreamers, and lots of students not yet jaded by commercial enterprise—who believed the robot drivers people had been craving for nearly forever were possible, and who were suddenly driven to make them real.

They came back for a follow-up race in 2005 and proved that making a car drive itself was indeed possible: Five vehicles finished the course. By the 2007 Urban Challenge, the vehicles were not just avoiding obstacles and sticking to trails but following traffic laws, merging, parking, even making safe, legal U-turns.

When Google launched its self-driving car project in 2009, it started by hiring a team of Darpa Challenge veterans. Within 18 months, they had built a system that could handle some of California’s toughest roads (including the famously winding block of San Francisco’s Lombard Street) with minimal human involvement. A few years later, Elon Musk announced Tesla would build a self-driving system into its cars. And the proliferation of ride-hailing services like Uber and Lyft weakened the link between being in a car and owning that car, helping set the stage for a day when actually driving that car falls away too. In 2015, Uber poached dozens of scientists from Carnegie Mellon University—a robotics and artificial intelligence powerhouse—to get its effort going.

Dumbed Down AI Rhetoric Harms Everyone

Dumbed Down AI Rhetoric Harms Everyone

When the European Union Commission released its regulatory proposal on artificial intelligence last month, much of the US policy community celebrated. Their praise was at least partly grounded in truth: The world’s most powerful democratic states haven’t sufficiently regulated AI and other emerging tech, and the document marked something of a step forward. Mostly, though, the proposal and responses to it underscore democracies’ confusing rhetoric on AI.

Over the past decade, high-level stated goals about regulating AI have often conflicted with the specifics of regulatory proposals, and what end-states should look like aren’t well-articulated in either case. Coherent and meaningful progress on developing internationally attractive democratic AI regulation, even as that may vary from country to country, begins with resolving the discourse’s many contradictions and unsubtle characterizations.

The EU Commission has touted its proposal as an AI regulation landmark. Executive vice president Margrethe Vestager said upon its release, “We think that this is urgent. We are the first on this planet to suggest this legal framework.” Thierry Breton, another commissioner, said the proposals “aim to strengthen Europe’s position as a global hub of excellence in AI from the lab to the market, ensure that AI in Europe respects our values and rules, and harness the potential of AI for industrial use.”

This is certainly better than many national governments, especially the US, stagnating on rules of the road for the companies, government agencies, and other institutions. AI is already widely used in the EU despite minimal oversight and accountability, whether for surveillance in Athens or operating buses in Málaga, Spain.

But to cast the EU’s regulation as “leading” simply because it’s first only masks the proposal’s many issues. This kind of rhetorical leap is one of the first challenges at hand with democratic AI strategy.

Of the many “specifics” in the 108-page proposal, its approach to regulating facial recognition is especially consequential. “The use of AI systems for ‘real-time’ remote biometric identification of natural persons in publicly accessible spaces for the purpose of law enforcement,” it reads, “is considered particularly intrusive in the rights and freedoms of the concerned persons,” as it can affect private life, “evoke a feeling of constant surveillance,” and “indirectly dissuade the exercise of the freedom of assembly and other fundamental rights.” At first glance, these words may signal alignment with the concerns of many activists and technology ethicists on the harms facial recognition can inflict on marginalized communities and grave mass-surveillance risks.

The commission then states, “The use of those systems for the purpose of law enforcement should therefore be prohibited.” However, it would allow exceptions in “three exhaustively listed and narrowly defined situations.” This is where the loopholes come into play.

The exceptions include situations that “involve the search for potential victims of crime, including missing children; certain threats to the life or physical safety of natural persons or of a terrorist attack; and the detection, localization, identification or prosecution of perpetrators or suspects of the criminal offenses.” This language, for all that the scenarios are described as “narrowly defined,” offers myriad justifications for law enforcement to deploy facial recognition as it wishes. Permitting its use in the “identification” of “perpetrators or suspects” of criminal offenses, for example, would allow precisely the kind of discriminatory uses of often racist and sexist facial-recognition algorithms that activists have long warned about.

The EU’s privacy watchdog, the European Data Protection Supervisor, quickly pounced on this. “A stricter approach is necessary given that remote biometric identification, where AI may contribute to unprecedented developments, presents extremely high risks of deep and non-democratic intrusion into individuals’ private lives,” the EDPS statement read. Sarah Chander from the nonprofit organization European Digital Rights described the proposal to the Verge as “a veneer of fundamental rights protection.” Others have noted how these exceptions mirror legislation in the US that on the surface appears to restrict facial recognition use but in fact has many broad carve-outs.

Humans Need to Create Interspecies Money to Save the Planet

Humans Need to Create Interspecies Money to Save the Planet

The greatest failure of the digital age is how far removed it is from nature. The microchip has no circadian rhythm, nor has the computer breath. The network is incorporeal. This may represent an existential risk for life on Earth. I believe we have to make a decision: Succumb to pushing more of our brain time and economy into unnatural online constructs, or build the digital anew in a way that is rooted in nature.

Nature is excessive, baroque. Its song is not ours alone. We share this planet with 8 million nonhuman species, yet we scarcely think of how they move through the world. There is no way for wild animals, trees, or other species to make themselves known to us online or to express their preferences to us. The only value most of them have is the sum value of their processed body parts. Those that are not eaten are forgotten, or perhaps never remembered: Only 2 million of them are recorded by science.

This decade will be the most destructive for nonhuman life in recorded history. It could also be the most regenerative. Nonhuman life-forms may soon gain some agency in the world. I propose the invention of an Interspecies Money. I’m not talking about Dogecoin, the meme of a Shiba Inu dog that’s become a $64 billion cryptocurrency (as of today). I’m talking about a digital currency that could allow several hundred billion dollars to be held by other beings simply on account of being themselves and no other and being alive in the world. It is possible they will be able to spend and invest this digital currency to improve their lives. And because the services they ask for—recognition, security, room to grow, nutrition, even veterinary care—will often be provided by poor communities in the tropics, human lives will also be improved.

Money needs to cross the species divide. Whoa, I know. King Julien with a credit card. Flower grenades into the meaning of life. Bear with me. If money, as some economic theorists suggest, is a form of memory, it is obvious that nonhuman species are unseen by the market economy because no money has ever been assigned by them. In order to preserve the survival of some species it is necessary in some situations, usually when they are in direct competition with humans, to give them economic advantage. An orchid, a baobab tree, a dugong, an orangutan, even at some future point the trace lines of a mycelial network—all of these should hold money.

We have the technology to start building Interspecies Money now. Indeed, it sometimes seems to me that the living system (Gaia or otherwise) is in fact producing the tools needed to protect complex life at precisely the moment it is most needed: fintech solutions in mobile money, digital wallets, and cryptocurrencies, which have shown that it is possible to address micropayments accurately and cheaply; cloud computing firms, which have demonstrated that large amounts of data can be stored and processed, even in countries that favor data sovereignty; hardware, which has become smarter and cheaper. Single-board computers (Raspberry Pis), camera traps, microphones, and other cheap sensors, energy solutions in solar arrays and batteries, internet connectivity, flying and ground robots, low-orbit satellite systems, and the pervasiveness of smartphones make it plausible to build a verification system in the wild that is trusted by the markets.

The first requirement of Interspecies Money is to provide a digital identity of an individual animal, or a herd, or a type (depending on size, population dynamics, and other characteristics of the organisms). This can be done through many methods. Birds may be identified by sound, insects by genetics, trees by probability. For most wild animals it will be done by sight. Some may be observed constantly, others only glimpsed. For instance, the digital identity of rare Hirola antelopes in Kenya and Somalia, of which there are only 500 in existence, will be minted from images gathered on mobile phones, camera traps, and drones by community rangers. The identity serves as a digital twin, which in legal and practical terms holds the money and releases it based on the services the life-form requires.