What Happens After Superintelligence? (with Anders Sandberg)
Anders Sandberg joins me to discuss superintelligence and its profound implications for human psychology, markets, and governance. We talk about physical bottlenecks, tensions between the technosphere and the biosphere, and the long-term cultural and physical forces shaping civilization. We conclude with Sandberg explaining the difficulties of designing reliable AI systems amidst rapid change and coordination risks. Learn more about Anders's work here: https://mimircenter.org/anders-sandberg Timestamps: 00:00:00 Preview and intro 00:04:20 2030 superintelligence scenario 00:11:55 Status, post-scarcity, and reshaping human psychology 00:16:00 Physical limits: energy, datacenter, and waste-heat bottlenecks 00:23:48 Technosphere vs biosphere 00:28:42 Culture and physics as long-run drivers of civilization 00:40:38 How superintelligence could upend markets and governments 00:50:01 State inertia: why governments lag behind companies 00:59:06 Value lock-in, censorship, and model alignment 01:08:32 Emergent AI ecosystems and coordination-failure risks 01:19:34 Predictability vs reliability: designing safe systems 01:30:32 Crossing the reliability threshold 01:38:25 Personal reflections on accelerating change
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1:44:54
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1:44:54
Why the AI Race Ends in Disaster (with Daniel Kokotajlo)
On this episode, Daniel Kokotajlo joins me to discuss why artificial intelligence may surpass the transformative power of the Industrial Revolution, and just how much AI could accelerate AI research. We explore the implications of automated coding, the critical need for transparency in AI development, the prospect of AI-to-AI communication, and whether AI is an inherently risky technology. We end by discussing iterative forecasting and its role in anticipating AI's future trajectory. You can learn more about Daniel's work at: https://ai-2027.com and https://ai-futures.org Timestamps: 00:00:00 Preview and intro 00:00:50 Why AI will eclipse the Industrial Revolution 00:09:48 How much can AI speed up AI research? 00:16:13 Automated coding and diffusion 00:27:37 Transparency in AI development 00:34:52 Deploying AI internally 00:40:24 Communication between AIs 00:49:23 Is AI inherently risky? 00:59:54 Iterative forecasting
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1:10:26
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1:10:26
Preparing for an AI Economy (with Daniel Susskind)
On this episode, Daniel Susskind joins me to discuss disagreements between AI researchers and economists, how we can best measure AI’s economic impact, how human values can influence economic outcomes, what meaningful work will remain for humans in the future, the role of commercial incentives in AI development, and the future of education. You can learn more about Daniel's work here: https://www.danielsusskind.com Timestamps: 00:00:00 Preview and intro 00:03:19 AI researchers versus economists 00:10:39 Measuring AI's economic effects 00:16:19 Can AI be steered in positive directions? 00:22:10 Human values and economic outcomes 00:28:21 What will remain for people to do? 00:44:58 Commercial incentives in AI 00:50:38 Will education move towards general skills? 00:58:46 Lessons for parents
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1:03:37
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1:03:37
Will AI Companies Respect Creators' Rights? (with Ed Newton-Rex)
Ed Newton-Rex joins me to discuss the issue of AI models trained on copyrighted data, and how we might develop fairer approaches that respect human creators. We talk about AI-generated music, Ed’s decision to resign from Stability AI, the industry’s attitude towards rights, authenticity in AI-generated art, and what the future holds for creators, society, and living standards in an increasingly AI-driven world. Learn more about Ed's work here: https://ed.newtonrex.com Timestamps: 00:00:00 Preview and intro 00:04:18 AI-generated music 00:12:15 Resigning from Stability AI 00:16:20 AI industry attitudes towards rights 00:26:22 Fairly Trained 00:37:16 Special kinds of training data 00:50:42 The longer-term future of AI 00:56:09 Will AI improve living standards? 01:03:10 AI versions of artists 01:13:28 Authenticity and art 01:18:45 Competitive pressures in AI 01:24:06 Priorities going forward
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1:27:14
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1:27:14
AI Timelines and Human Psychology (with Sarah Hastings-Woodhouse)
On this episode, Sarah Hastings-Woodhouse joins me to discuss what benchmarks actually measure, AI’s development trajectory in comparison to other technologies, tasks that AI systems can and cannot handle, capability profiles of present and future AIs, the notion of alignment by default, and the leading AI companies’ vague AGI plans. We also discuss the human psychology of AI, including the feelings of living in the "fast world" versus the "slow world", and navigating long-term projects given short timelines. Timestamps: 00:00:00 Preview and intro00:00:46 What do benchmarks measure? 00:08:08 Will AI develop like other tech? 00:14:13 Which tasks can AIs do? 00:23:00 Capability profiles of AIs 00:34:04 Timelines and social effects 00:42:01 Alignment by default? 00:50:36 Can vague AGI plans be useful? 00:54:36 The fast world and the slow world 01:08:02 Long-term projects and short timelines
The Future of Life Institute (FLI) is a nonprofit working to reduce global catastrophic and existential risk from powerful technologies. In particular, FLI focuses on risks from artificial intelligence (AI), biotechnology, nuclear weapons and climate change.
The Institute's work is made up of three main strands: grantmaking for risk reduction, educational outreach, and advocacy within the United Nations, US government and European Union institutions.
FLI has become one of the world's leading voices on the governance of AI having created one of the earliest and most influential sets of governance principles: the Asilomar AI Principles.
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