82. Jakob Foerster - The high cost of automated weapons
Towards Data Science
05/05/21
•54m
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Automated weapons mean fewer casualties, faster reaction times, and more precise strikes. They’re a clear win for any country that deploys them. You can see the appeal.
But they’re also a classic prisoner’s dilemma. Once many nations have deployed them, humans no longer have to be persuaded to march into combat, and the barrier to starting a conflict drops significantly.
The real risks that come from automated weapons systems like drones aren’t always the obvious ones. Many of them take the form of second-order effects — the knock-on consequences that come from setting up a world where multiple countries have large automated forces. But what can we do about them? That’s the question we’ll be taking on during this episode of the podcast with Jakob Foerster, an early pioneer in multi-agent reinforcement learning, and incoming faculty member at the University of Toronto. Jakob has been involved in the debate over weaponized drone automation for some time, and recently wrote an open letter to German politicians urging them to consider the risks associated with the deployment of this technology.
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81. Nicolas Miailhe - AI risk is a global problem
April 28, 2021
•56m
In December 1938, a frustrated nuclear physicist named Leo Szilard wrote a letter to the British Admiralty telling them that he had given up on his greatest invention — the nuclear chain reaction.
"The idea of a nuclear chain reaction won’t work. There’s no need to keep this patent secret, and indeed there’s no need to keep this patent too. It won’t work." — Leo SzilardWhat Szilard didn’t know when he licked the envelope was that, on that very same day, a research team in Berlin had just split the uranium atom for the very first time. Within a year, the Manhatta Project would begin, and by 1945, the first atomic bomb was dropped on the Japanese city of Hiroshima. It was only four years later — barely a decade after Szilard had written off the idea as impossible — that Russia successfully tested its first atomic weapon, kicking off a global nuclear arms race that continues in various forms to this day.
It’s a surprisingly short jump from cutting edge technology to global-scale risk. But although the nuclear story is a high-profile example of this kind of leap, it’s far from the only one. Today, many see artificial intelligence as a class of technology whose development will lead to global risks — and as a result, as a technology that needs to be managed globally. In much the same way that international treaties have allowed us to reduce the risk of nuclear war, we may need global coordination around AI to mitigate its potential negative impacts.
One of the world’s leading experts on AI’s global coordination problem is Nicolas Miailhe. Nicolas is the co-founder of The Future Society, a global nonprofit whose primary focus is encouraging responsible adoption of AI, and ensuring that countries around the world come to a common understanding of the risks associated with it. Nicolas is a veteran of the prestigious Harvard Kennedy School of Government, an appointed expert to the Global Partnership on AI, and advises cities, governments, international organizations about AI policy.
Next Episode
83. Rosie Campbell - Should all AI research be published?
May 12, 2021
•52m
When OpenAI developed its GPT-2 language model in early 2019, they initially chose not to publish the algorithm, owing to concerns over its potential for malicious use, as well as the need for the AI industry to experiment with new, more responsible publication practices that reflect the increasing power of modern AI systems.
This decision was controversial, and remains that way to some extent even today: AI researchers have historically enjoyed a culture of open publication and have defaulted to sharing their results and algorithms. But whatever your position may be on algorithms like GPT-2, it’s clear that at some point, if AI becomes arbitrarily flexible and powerful, there will be contexts in which limits on publication will be important for public safety.
The issue of publication norms in AI is complex, which is why it’s a topic worth exploring with people who have experience both as researchers, and as policy specialists — people like today’s Towards Data Science podcast guest, Rosie Campbell. Rosie is the Head of Safety Critical AI at Partnership on AI (PAI), a nonprofit that brings together startups, governments, and big tech companies like Google, Facebook, Microsoft and Amazon, to shape best practices, research, and public dialogue about AI’s benefits for people and society. Along with colleagues at PAI, Rosie recently finished putting together a white paper exploring the current hot debate over publication norms in AI research, and making recommendations for researchers, journals and institutions involved in AI research.
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