81. Nicolas Miailhe - AI risk is a global problem

81. Nicolas Miailhe - AI risk is a global problem

Author: The TDS team April 28, 2021 Duration: 56:03

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 Szilard

What 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.


While the active production of Towards Data Science has concluded, its archive remains a vital resource. Created by The TDS team, this collection captures a specific moment in the rapid evolution of data science and artificial intelligence. Each conversation pulls you directly into the room with leading researchers and practitioners who were shaping the tools and theories of their time. The discussions are not abstract lectures; they are grounded explorations of real-world problems, ethical dilemmas, and technical challenges that defined the field's trajectory. You'll hear experts dissect the implications of their work, from algorithmic fairness to the practicalities of deploying models at scale. This podcast served as a forum for nuanced debate, where complex ideas were unpacked with clarity and depth. Listening now offers a unique historical perspective, a chance to understand the foundational conversations that continue to influence where technology is headed next. The archive of Towards Data Science stands as a substantive record of insight, preserving the voices and questions from the forefront of a digital revolution.
Author: Language: en-us Episodes: 50

Towards Data Science
Podcast Episodes
90. Jeffrey Ding - China’s AI ambitions and why they matter [not-audio_url] [/not-audio_url]

Duration: 49:07
There are a lot of reasons to pay attention to China’s AI initiatives. Some are purely technological: Chinese companies are producing increasingly high-quality AI research, and they’re poised to become even more importan…
87. Evan Hubinger - The Inner Alignment Problem [not-audio_url] [/not-audio_url]

Duration: 1:09:32
How can you know that a super-intelligent AI is trying to do what you asked it to do? The answer, it turns out, is: not easily. And unfortunately, an increasing number of AI safety researchers are warning that this is a…
86. Andy Jones - AI Safety and the Scaling Hypothesis [not-audio_url] [/not-audio_url]

Duration: 1:25:44
When OpenAI announced the release of their GPT-3 API last year, the tech world was shocked. Here was a language model, trained only to perform a simple autocomplete task, which turned out to be capable of language transl…
85. Brian Christian - The Alignment Problem [not-audio_url] [/not-audio_url]

Duration: 1:06:19
In 2016, OpenAI published a blog describing the results of one of their AI safety experiments. In it, they describe how an AI that was trained to maximize its score in a boat racing game ended up discovering a strange ha…
83. Rosie Campbell - Should all AI research be published? [not-audio_url] [/not-audio_url]

Duration: 52:37
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 experime…
82. Jakob Foerster - The high cost of automated weapons [not-audio_url] [/not-audio_url]

Duration: 54:07
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…