Future of Life Institute Podcast

On this episode, Jeffrey Ding joins me to discuss diffusion of AI versus AI innovation, how US-China dynamics shape AI’s global trajectory, and whether there is an AI arms race between the two powers. We explore Chinese attitudes toward AI safety, the level of concentration of AI development, and lessons from historical technology diffusion. Jeffrey also shares insights from translating Chinese AI writings and the potential of automating translations to bridge knowledge gaps.   You can learn more about Jeffrey’s work at: https://jeffreyjding.github.io   Timestamps:   00:00:00 Preview and introduction   00:01:36 A US-China AI arms race?   00:10:58 Attitudes to AI safety in China   00:17:53 Diffusion of AI   00:25:13 Innovation without diffusion   00:34:29 AI development concentration   00:41:40 Learning from the history of technology   00:47:48 Translating Chinese AI writings   00:55:36 Automating translation of AI writings

On this episode, Allison Duettmann joins me to discuss centralized versus decentralized AI, how international governance could shape AI’s trajectory, how we might cooperate with future AIs, and the role of AI in improving human decision-making. We also explore which lessons from history apply to AI, the future of space law and property rights, whether technology is invented or discovered, and how AI will impact children.  You can learn more about Allison's work at: https://foresight.org   Timestamps:   00:00:00 Preview  00:01:07 Centralized AI versus decentralized AI   00:13:02 Risks from decentralized AI   00:25:39 International AI governance   00:39:52 Cooperation with future AIs   00:53:51 AI for decision-making   01:05:58 Capital intensity of AI  01:09:11 Lessons from history   01:15:50 Future space law and property rights   01:27:28 Is technology invented or discovered?   01:32:34 Children in the age of AI

On this episode, Steven Byrnes joins me to discuss brain-like AGI safety. We discuss learning versus steering systems in the brain, the distinction between controlled AGI and social-instinct AGI, why brain-inspired approaches might be our most plausible route to AGI, and honesty in AI models. We also talk about how people can contribute to brain-like AGI safety and compare various AI safety strategies.   You can learn more about Steven's work at: https://sjbyrnes.com/agi.html   Timestamps:   00:00 Preview   00:54 Brain-like AGI Safety  13:16 Controlled AGI versus Social-instinct AGI   19:12 Learning from the brain   28:36 Why is brain-like AI the most likely path to AGI?   39:23 Honesty in AI models   44:02 How to help with brain-like AGI safety   53:36 AI traits with both positive and negative effects   01:02:44 Different AI safety strategies

On this episode, Ege Erdil from Epoch AI joins me to discuss their new GATE model of AI development, what evolution and brain efficiency tell us about AGI requirements, how AI might impact wages and labor markets, and what it takes to train models with long-term planning. Toward the end, we dig into Moravec’s Paradox, which jobs are most at risk of automation, and what could change Ege's current AI timelines.   You can learn more about Ege's work at https://epoch.ai   Timestamps:  00:00:00 – Preview and introduction  00:02:59 – Compute scaling and automation - GATE model  00:13:12 – Evolution, Brain Efficiency, and AGI Compute Requirements  00:29:49 – Broad Automation vs. R&D-Focused AI Deployment  00:47:19 – AI, Wages, and Labor Market Transitions  00:59:54 – Training Agentic Models and Long-Term Planning Capabilities  01:06:56 – Moravec’s Paradox and Automation of Human Skills  01:13:59 – Which Jobs Are Most Vulnerable to AI?  01:33:00 – Timeline Extremes: What Could Change AI Forecasts?

In this special episode, we feature Nathan Labenz interviewing Nicholas Carlini on the Cognitive Revolution podcast. Nicholas Carlini works as a security researcher at Google DeepMind, and has published extensively on adversarial machine learning and cybersecurity. Carlini discusses his pioneering work on adversarial attacks against image classifiers, and the challenges of ensuring neural network robustness. He examines the difficulties of defending against such attacks, the role of human intuition in his approach, open-source AI, and the potential for scaling AI security research.   00:00 Nicholas Carlini's contributions to cybersecurity 08:19 Understanding attack strategies  29:39 High-dimensional spaces and attack intuitions  51:00 Challenges in open-source model safety  01:00:11 Unlearning and fact editing in models  01:10:55 Adversarial examples and human robustness  01:37:03 Cryptography and AI robustness  01:55:51 Scaling AI security research