Neurotechnology and the Recovering Brain
1:14:50Why Your Brain Hates Losing
1:27:55The Beautiful Engineering of Us: From Neurons to Collective Minds
1:06:40Crossroads Democracy — A Panel Discussion
1:34:49Transmission Versus Truth: What Will It Take to Make an Al as Smart as a 4-Year-Old?
1:27:20Machining the Ghost: Ideating in the Age of AI
1:29:15Whom Do We Trust? How AI Is (Re)Shaping Our Interactions Today
1:28:51Climate Change and Contagion: Complex Crises Past and Present
1:33:25Computing, Life, and Intelligence
1:23:05Making Sense of Chaos: A Better Economics for a Better World
1:38:21What is Lyfe? Towards a Biology of Context & Complexity
1:11:46Just Machine Learning
1:13:25Animals in Translation: A Conversation
1:28:56Comrades, Let's Optimize! The Surprising Rebirth of the Planned Economy
1:01:26The Future of Artificial Intelligence
1:27:49Taming the Swarm: Towards Collective Artificial Intelligence
1:22:48How the Brain Makes You: Collective Intelligence and Computation by Neural Circuits
1:34:37The Future of Physics
1:22:21AI: Oppression or Innovation?
1:28:36Harnessing Social Capital to Enhance Collective Welfare
1:39:02The Story of Sync
1:35:51Transmission Versus Truth: What Will It Take to Make an Al as Smart as a 4-Year-Old?
Alison Gopnik examines the concept of general intelligence, proposing that human cognitive development, encompassing exploration in childhood, exploitation in adulthood, and cultural transmission in elderhood, offers a crucial framework for designing diverse AI capacities. The discussion highlights the trade-offs inherent in different developmental stages and their implications for AI architectures.
Alison Gopnik (University of California, Berkeley, SFI) There is no such thing as general intelligence — artificial or natural — argues Alison Gopnik. Instead, there are multiple intelligences, each with its own trade-offs. Three different types of cognitive capacities mark human development, and our life histories accommodate intrinsic trade-offs between the three. The extended human childhood allows a period of protected broad-ranging exploration and truth-seeking, adulthood allows exploitation and resource gathering, and post-menopausal elderhood allows caregiving and cultural transmission. Each period involves motivations and computational capacities that are in tension with those of other periods, but the full suite of development allows for maximal adaptation to changing and variable environments. The human life history offers lessons for AI, suggests Gopnik. Different types of AI enable different capacities: intrinsically motivated reinforcement learning can help enable exploration and truth-finding, while Large Language Models are means of cultural transmission. Learn more, follow us on social media and check out our podcasts:
Crossroads Democracy — A Panel Discussion
Next · Lecture 6Machining the Ghost: Ideating in the Age of AI
