88. Oren Etzioni - The case against (worrying about) existential risk from AI

88. Oren Etzioni - The case against (worrying about) existential risk from AI

Author: The TDS team June 16, 2021 Duration: 53:34

Few would disagree that AI is set to become one of the most important economic and social forces in human history.

But along with its transformative potential has come concern about a strange new risk that AI might pose to human beings. As AI systems become exponentially more capable of achieving their goals, some worry that even a slight misalignment between those goals and our own could be disastrous. These concerns are shared by many of the most knowledgeable and experienced AI specialists, at leading labs like OpenAI, DeepMind, CHAI Berkeley, Oxford and elsewhere.

But they’re not universal: I recently had Melanie Mitchell — computer science professor and author who famously debated Stuart Russell on the topic of AI risk — on the podcast to discuss her objections to the AI catastrophe argument. And on this episode, we’ll continue our exploration of the case for AI catastrophic risk skepticism with an interview with Oren Etzioni, CEO of the Allen Institute for AI, a world-leading AI research lab that’s developed many well-known projects, including the popular AllenNLP library, and Semantic Scholar.

Oren has a unique perspective on AI risk, and the conversation was lots of fun!


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
127. Matthew Stewart - The emerging world of ML sensors [not-audio_url] [/not-audio_url]

Duration: 41:34
Today, we live in the era of AI scaling. It seems like everywhere you look people are pushing to make large language models larger, or more multi-modal and leveraging ungodly amounts of processing power to do it. But alt…
126. JR King - Does the brain run on deep learning? [not-audio_url] [/not-audio_url]

Duration: 55:43
Deep learning models — transformers in particular — are defining the cutting edge of AI today. They’re based on an architecture called an artificial neural network, as you probably already know if you’re a regular Toward…
125. Ryan Fedasiuk - Can the U.S. and China collaborate on AI safety? [not-audio_url] [/not-audio_url]

Duration: 48:19
It’s no secret that the US and China are geopolitical rivals. And it’s also no secret that that rivalry extends into AI — an area both countries consider to be strategically critical. But in a context where potentially t…
124. Alex Watson - Synthetic data could change everything [not-audio_url] [/not-audio_url]

Duration: 51:47
There’s a website called thispersondoesnotexist.com. When you visit it, you’re confronted by a high-resolution, photorealistic AI-generated picture of a human face. As the website’s name suggests, there’s no human being…
122. Sadie St. Lawrence - Trends in data science [not-audio_url] [/not-audio_url]

Duration: 43:02
As you might know if you follow the podcast, we usually talk about the world of cutting-edge AI capabilities, and some of the emerging safety risks and other challenges that the future of AI might bring. But I thought th…
121. Alexei Baevski - data2vec and the future of multimodal learning [not-audio_url] [/not-audio_url]

Duration: 49:31
If the name data2vec sounds familiar, that’s probably because it made quite a splash on social and even traditional media when it came out, about two months ago. It’s an important entry in what is now a growing list of s…