Politicians need to learn how AI works, fast

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This week, the US senators heard alarming testimony suggesting that uncontrolled AI could steal jobs, spread misinformationand in general “go bad enough”, in the words of OpenAI CEO Sam Altman (whatever that means). He and several lawmakers agreed that the US may now need a new federal agency to oversee the development of the technology. But there was also agreement at the hearing that no one wants to kick around a technology that could potentially increase productivity and give the US a head start in a new technological revolution.

Concerned senators might consider speaking with miss cummingsto former fighter pilot and professor of engineering and robotics at George Mason University. She studies the use of AI and automation in safety-critical systems, including automobiles and aircraft, and earlier this year she returned to academia after a stint with the National Highway Traffic Safety Administration, which oversees automotive technologyincluding Tesla Autopilot and driverless cars. Cummings’ insight could help politicians and lawmakers trying to weigh the promise of much-hyped new algorithms against the risks that lie ahead.

Cummings told me this week that he left NHTSA with a sense of deep concern about the autonomous systems being implemented by many automakers. “We’re in serious trouble as far as the capabilities of these cars,” Cummings says. “They’re not even close to being as capable as people think they are.”

I was struck by the parallels with ChatGPT and similar chatbots that were generating excitement and concern about the power of AI. Automated driving features have been around longer, but like large language models, they are based on machine learning algorithms that are inherently unpredictable, difficult to inspect, and require a different kind of engineering thinking than in the past.

Also like ChatGPT, Tesla’s Autopilot and other self-driving projects have been elevated by absurd amounts of hype. Heady dreams of a transportation revolution led automakers, start-ups, and investors to pour vast sums of money into developing and implementing technology that still have many unresolved issues. There was a permissive regulatory environment around self-driving cars in the mid-2010s, with government officials reluctant to rein in a technology that promised to be worth billions to American companies.

After billions spent on the technology, driverless cars are still to bully by issuesand some car companies have pulled the plug in large projects of autonomy. Meanwhile, as Cummings says, it’s often unclear to the public how capable semi-autonomous technology really is.

In a sense, it’s good to see governments and legislators rushing to suggest regulation of generative AI tools and large language models. The current panic is focused on large language models and tools like ChatGPT What are they remarkably good at answering questions and solving problemseven if they still have significant shortcomings, including confidently fabricating facts.

At this week’s Senate hearing, OpenAI’s Altman, who gave us ChatGPT, went so far as to call for a licensing system to control whether companies like his can work on advanced AI. “My worst fear is that we, the field, the technology, the industry, will cause significant damage to the world,” Altman said during the hearing.