The Gradient: Perspectives on AI

Harvey Lederman, a philosophy professor, discusses propositional attitudes and reference in language models. They explore higher-order metaphysics, syntactic types, and ontological categories. They also delve into the challenges of extreme vagueness, perspectivism, and the context-sensitivity of language. The podcast covers topics such as identity statements, propositional attitude psychology, sense and reference, and the attribution of beliefs to language models. Additionally, they discuss n-gram models, novel reference, and the intersection between AI and the philosophy of language.

In this podcast, Eric Jang, an AI researcher and author of 'AI is Good for You', discusses the problem space of humanoid robots, definitions and timelines of AGI, rediscovering old ideas in AI and robotics, the role of human in the loop learning, levels of abstraction for modeling decision-making, embodiment in AI systems, and the potential benefits and drawbacks of AGI.

Nathan Benaich, Founder and General Partner at Air Street Capital, discusses the state of AI, open-source versus closed-source, benchmarking and evaluation of AI models, opportunities in the natural sciences, reinforcement learning from human feedback, generative AI applications, the importance of compute, geopolitical tensions, and predictions for the future of AI.

Dr. Kathleen Fisher, Director of DARPA’s Information Innovation Office, discusses DARPA's history of funding AI research and recent initiatives for national security. They explore the intersection between programming languages and AI, DARPA's investment in AI technology, galvanizing the research community, interactions with technology development and policy formulation, and how to get involved with DARPA's work.

Peter Tse, Professor of Cognitive Neuroscience, discusses the neuroscience of consciousness and free will. Topics explored include determinism, quantum theory, biases in theory selection, neuroscience and subjectivity, introspection in neuroscience, and the physical properties of neurons. Other topics include the evolution of psychology and neuroscience, intrinsic meaning in AI, consciousness, desire systems, and mental operations in cognitive neuroscience.

Vera Liao, Principal Researcher at Microsoft Research, discusses AI explainability and transparency. Topics include UX design and toolkits for AI explainability, interfaces for explanation and model capabilities, online social communities, the difference between explainability and interpretability, the importance of interactivity in explanations, understanding the social context of AI systems, evaluating AI explainability criteria, challenges in AI transparency, and the need for a collaborative community.

Thomas Dietterich, pioneer in machine learning, discusses the early days of AI, philosophy of science in AI, levels of understanding and sentience, error correcting output codes, hierarchical reinforcement learning, computational sustainability, flaws of language models, and more.

Martin Wattenberg, a professor at Harvard and co-founder of Google Research's People + AI Research (PAIR) initiative, discusses his background in ML visualization, skepticism of neural networks in the 1980s, organization of information in graphics, progressive disclosure of complexity in interface design, evolutionary conversation interfaces, developing tools for model understanding, and creating trust in ML systems.

Laurence Liew, Director for AI Innovation at AI Singapore, discusses the adoption of AI in Singapore, the success of the AI Apprenticeship Program, developing generational AI talent, Singapore's place in the global AI ecosystem, and his vision for Singapore's tech future.

Michael Levin & Adam Goldstein discuss diverse intelligence, unconventional embodiments, failure of life-machine distinction, the concept of self, the combination problem in cognitive function, regenerative medicine, and the relationship between computation, intelligence, and moral concerns.