Latest Interviews
Showing 1–2 of 2 transcripts.
Clear all filters- Lex Fridman1h 28m
Deep Learning State of the Art (2020)
Pamela McCordick, Alan Turing, Frank Rosenblatt, Yann LeCun, Geoffrey Hinton, Yoshua Bengio, Walter Pitts, Warren McCulloch, Alexei Evaknenko, V.G. Lapa, John Hopfield, Juergen Schmidhuber, Rodney Brooks, Sebastian Reuter, Jacob, Noah Brown, Chris Ferguson, Darren Elias, Jeremy Howard, Ian Goodfellow, Aaron Corville, Andrew Trask, Francois Chollet, David Silver, Robbie Allen, Victor Flevin, Ilias Esquiver, Peter Singer, George Washington, Stalin
This presentation traces the evolution of artificial intelligence from Alan Turing's foundational predictions to 2019's deep learning dominance by LeCun, Hinton, and Bengio, while analyzing recent paradigm shifts in reinforcement learning and autonomous vehicle strategies. The speaker highlights 2020's framework convergence between TensorFlow and PyTorch, details the limitations of current transformer-based models regarding common sense reasoning, and outlines critical research priorities in ethics and long-term safety. Ultimately, the discourse frames the greatest existential risk not as rogue AI, but as human utilization of these tools for control and warfare, urging a democratization of the technology to ensure ethical stewardship.
- Lex Fridman10 min
Yann LeCun: Can Neural Networks Reason? | AI Podcast Clips
This presentation critiques discrete logic-based reasoning and rigid knowledge graphs in favor of continuous, gradient-based learning frameworks inspired by Jeff Hinton. It proposes that functional artificial reasoning requires working memory systems capable of episodic storage and energy minimization, citing Léon Boutou's work on learning logic-like operations within continuous spaces. The discussion concludes by highlighting the unresolved theoretical debate regarding the extent of structural bias necessary for reasoning to emerge versus learning it purely from data.