Ford’s Future Is Driving Itself


Kyle Harris | Flickr

Look, ma, no hands! What Ford’s driverless plan means. Tuesday, Ford announced it’s hitting the accelerator on a self-driving car program and aims to roll out a fleet of autonomous vehicles by 2021. (CEO Mark Fields details the plan in NewCo Shift.) Ford’s news connects three big trends in the NewCo world. First trend: Driverless cars are coming, faster than many thought, and they’re going to uproot lots of assumptions about how our businesses, cities, and lives run. Ford aims to leap straight to self-driving cars — no steering wheels, no pedals — rather than incrementally refine driver-assistance systems. The first vehicles Ford envisions will be costly, so it plans to sell to ride-hailing and sharing services initially, individuals later. (GM is a partner/investor in Lyft, but Ford has no such alliance — yet.) Second trend: Big industrial transitions like this are making BigCos like Ford return to first principles and think the way they did when they were smaller and younger. Ford CEO Mark Fields says its autonomous vehicle will have “as big an impact on society as Ford’s moving assembly line did a hundred years ago.” He frames Ford’s new plan as a refresh of the company’s populist, autos-for-everyman heritage. Ford is also increasing its Silicon Valley presence and investing in tech firms (like Velodyne, which makes distance sensors that use “lidar,” or light radar) to accelerate its self-driving program. Third trend: Ford’s move, like so much else that’s happening in business today, will speed up the handoff of decisions from people to algorithms. At the end of this road, the code that runs your car won’t only be picking routes — it will be making life-or-death choices. For a preview of that world, read up on MIT’s “moral machine” (Quartz) — a thought-experiment project that asks people how driverless cars should prioritize human lives when the cars’ brakes fail.

War is hell, and climate is war. World War III is here, and it’s not a shooting war with a foreign enemy — it’s humanity’s fight against climate change. That military language isn’t just a metaphor, writes activist-author Bill McKibben (The New Republic): The planet’s carbon-driven warming is seizing territory and causing casualties as swiftly and mercilessly as a hostile army would, and if we’re going to have any hope of stopping it, we need to launch an effort as vast, and as unified, as the one that, last century, saved the world from Hitler. What would a climate-focused version of the Second World War’s mobilization and Manhattan Project look like? Stanford researchers offer one vision (pdf). We have the technology; we need the will. The good news is, we’ve mounted this kind of all-out effort before — and it works.

A bot that battles homelessness. The 19-year-old British Stanford student who built a bot that helps drivers contest parking tickets is now aiming to help people fight evictions (Ars Technica). Joshua Browder’s DoNotPay started out offering legal advice for challenging unfair parking fines, and he says it has overturned more than 170,000 tickets since launch, with a 64 percent success rate. But what started as a convenience gradually evolved into a social-change project: When users started asking him for other kinds of legal help, Browder began to think more ambitiously about how a chatbot could offer DIY legal advice and write form letters that help tenants keep their homes. Right now, the eviction-protection service operates only in the U.K.

Big data is all around you. Machine learning — whether that means chess-champion computers, autonomous cars, or face-recognition programs — is only as good as the data it studies (Recode). The new generation of artificial intelligences learn by training on data sets that are carefully curated by — wait for it — human beings. Today’s companies and organizations already have the information we need. But if machine learning is going to drive the next breakthroughs in a field like cancer research, it will have to unlock the wealth of data that’s currently tucked away in handwritten documents and paper reports. Paradoxically, the road to AI may lead through the dust of the file room.

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