This episode breaks down 'Machine Super Intelligence', a thesis on universal artificial intelligence, a theoretical model of an agent that can learn to perform optimally in a wide range of environments. The thesis explores various definitions and measurements of intelligence, both for humans and for artificial systems. It then introduces the AIXI agent, a theoretical model of a universal artificial intelligence that is based on Solomonoff induction, a method for predicting the future of a sequence of observations. The thesis investigates the limitations of computational agents and discusses the possibility of building super intelligent machines.

Audio : (Spotify) https://open.spotify.com/episode/7LA0N7QfYJJIrtdASPVQN5?si=BopcvraFSzq1QvC7RP6dig

Paper: https://www.vetta.org/documents/Machine_Super_Intelligence.pdf