Today, numerous large and small companies around the world are working diligently on perfecting their company’s self-driving software. All the large traditional automobile companies are included as well as large technology firms such as Google, Intel and Microsoft, and even Uber. These companies are working in true twentieth-century capitalist fashion: they’re doing it all independently and secretly. This approach leads to sub-optimal technology and foreseeable tragedies.
Self-Driving Vehicles Use Artificial Intelligence (AI)
Programming a self-driving vehicle (SDV) by traditional software-development methods is so fraught with complexity that no one, to my knowledge, is attempting. So scrap that idea. Instead, developers have flocked to artificial intelligence, a red-hot technology idea built on rather old ideas about neural networks.
There’s a lot to AI technology beyond the scope of this blog. A quick Internet search will get you started on a deep dive. For today, let’s sketch a common approach to AI application development:
- First, an AI rules-based model is fed real-world scenarios, rules, and practical knowledge. For example, “turning left into oncoming traffic (in the USA but not the UK) is illegal and hazardous and will likely result in a crash. Don’t do that.” This first phase is the AI Learning Phase.
- Second, the neural network created in the learning phase is executed in a vehicle, often on a specialized chip, graphics processing unit (GPU) or multi-processor. This is the Execution Phase.
- Third, the execution unit records real-world observations while driving, eventually feeding them back into the learning model.
The Problem of Many
Here’s the rub. Every SDV developer is on its own, creating a proprietary AI model with its own set of learning criteria. Each AI model is only as good as the data fed into its learning engine.
No single company is likely to encounter or imagine all of the third standard-deviation, Black Swan events that can and will lead to vehicle tragedies and loss of life. Why should Tesla and the state of Florida be the only beneficiaries of the lessons from a particular fatal crash? The industry should learn from the experience too. That’s how society progresses.
Cue the class-action trial lawyers.
E Pluribus Unum
E Pluribus Unum is Latin for “out of many, one”. (Yes, it’s the motto of the United States). My proposal is simple:
- The federal government should insist that all self-driving vehicles use an AI execution unit that is trained in its learning phase with an open-source database of events, scenarios, and real-world feedback. Out of many AI training models, one model.
- The Feds preempt state regulation of core AI development and operation
- Vehicles that use the federalized learning database for training receive limited class-action immunity, just like we now do with immunization drugs.
- The Feds charge fees to the auto industry that cover the costs of the program.
From a social standpoint, there’s no good reason for wild-west capitalism over proprietary AI learning engines that lead to avoidable crashes and accidents. With one, common AI learning database, all SDVs will get smarter, faster because they are benefiting from the collective experience of the entire industry. By allowing and encouraging innovation in AI execution engines, the industry will focus on areas that impact better-faster-cheaper-smaller products and not in avoiding human-risk situations. Performance benchmarks are a well-understood concept.
Philosophically, I don’t turn first to government regulation. But air traffic control, railroads, and numerous aspects of medical areas are regulated without controversy. Vehicle AI is ripe for regulation before production vehicles are produced by the millions over the next decade.
I am writing this blog because I don’t see the subject being discussed. It ought to be.
Comments and feedback are welcome. See my feed on Twitter @peterskastner.