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I'm a science fiction writer, so I quite enjoy thinking about self-driving cars. They make for really interesting analogies about data, liability, self-determination, information security and openness.
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Sometimes I write fiction about this! Deakin University commissioned "Car Wars," a short story about the sociotechnological issues raised by autonomous vehicle thought-experiments.
https://this.deakin.edu.au/self-improvement/car-wars
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https://this.deakin.edu.au/self-improvement/car-wars
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(they also got a faculty member to write a quiz for it whose correct answers take a 180' different view to my own!)
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And without any spoilers here, I'll say that subverted, lethal autonomous vehicles are a key plot point in Attack Surface, the third Little Brother book, coming out in Oct.
https://read.macmillan.com/promo/attacksurfacepreordercampaign/
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https://read.macmillan.com/promo/attacksurfacepreordercampaign/
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But I'm a science fiction writer and that means I can tell the difference between "thought experiments" and "real things." Alas, the same cannot be said of corporate America.
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For example, according to its own IPO filings, Uber can only be profitable if it invents fully autonomous vehicles and replaces every public transit ride in the world with them.
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Elon Musk - a man whose "green electric car company" is only profitable thanks to the carbon credits it sells to manufacturers of the dirtiest SUVs in America, without which those planet-killing SUVs would not exist - makes the same mistake.
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Musk wants to abolish public transit and replace it with EVs (he says that public transit makes you sit next to strangers who might be serial killers, which tells you a lot about his view of humanity).
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Now, both Uber and Musk are both wrong as a matter of simple geometry. Multiply the space occupied by all those AVs by the journeys people in cities need to make by the additional distances of those journeys if we need road for all those cars, and you run out of space.
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It's a trivially modelable Red Queen's Race, in which the more cars you add, the more road you need, the more spaced out everything gets, the more cars you need, the more road you need, the more spaced out everything gets, the more cars you need...
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Indeed, these fairy tales require so much credulity to be taken seriously that they strain even the car-addled imaginations of American automotive culture, and also rely on the irrational exuberance inspired by imaginary self-driving cars to propagate and persist.
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But that exuberance is sorely misplaced. Machine learning systems have brittle and unpredictable failure modes that can be triggered by accident or deliberately. The unconstrained problem of navigating busy cities with unquantifiable human activities is insoluble with ML.
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Or, at least, it's insoluble if you care about whether cars kill even more people in even less predictable ways than they do now.
This is a lesson that Ford CEO Jim Hackett just learned the hard way.
https://www.engadget.com/2019-04-10-ford-ceo-says-the-company-overestimated-self-driving-cars.html
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This is a lesson that Ford CEO Jim Hackett just learned the hard way.
https://www.engadget.com/2019-04-10-ford-ceo-says-the-company-overestimated-self-driving-cars.html
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After investing heavily in the technology and boasting about how Ford's future was a bet on the imminent arrival of AVs, Hackett has had to admit that "We overestimated the arrival of autonomous vehicles."
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The company has sunk $4b into the technology. But there's gotta be a pony under there somewhere:
"When we bring this thing to market, it's going to be really powerful." -Hackett
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"When we bring this thing to market, it's going to be really powerful." -Hackett
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Image:
Cryteria (modified)
https://commons.wikimedia.org/wiki/File:HAL9000.svg
CC BY:
https://creativecommons.org/licenses/by/3.0/deed.en
Gartner (modified):
https://www.gartner.com/en/research/methodologies/gartner-hype-cycle
eof/
Cryteria (modified)
https://commons.wikimedia.org/wiki/File:HAL9000.svg
CC BY:
https://creativecommons.org/licenses/by/3.0/deed.en
Gartner (modified):
https://www.gartner.com/en/research/methodologies/gartner-hype-cycle
eof/
