Jeremy Howard
Showing 1–5 of 5 transcripts.
- Jane Street1h 6m
The Uncertain Art of Accelerating ML Models with Sylvain Gugger
Sylvain Gugger, Ron Minsky, Jeremy Howard, Mark Mandelmann, Mark Mirchandani, Francesc Campoy, Gabriel Sanchez
Former fast.ai co-author Jeremy Howard discusses his transition from mathematics education to optimizing machine learning infrastructure at Jane Street, highlighting breakthroughs in learning rate schedules and image resizing that previously secured top benchmark placements. He details the development of the Hugging Face Accelerate library, a lightweight tool designed to abstract complex hardware parallelism and eliminate boilerplate code for training across diverse GPUs and TPUs. The discussion further explores the architectural constraints of financial data, the dominance of PyTorch's iterative execution model, and Jane Street's rigorous approach to reproducibility and custom model development for high-frequency trading.
- 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 Fridman9 min
Jeremy Howard: Deep Learning Frameworks - TensorFlow, PyTorch, fast.ai | AI Podcast Clips
The speaker traces the industry's evolution from static frameworks like Theano to interactive environments such as PyTorch and Fast.ai, highlighting how the latter's multi-layered API reduces boilerplate while preserving low-level control. Despite these advancements, the discussion identifies persistent performance bottlenecks in Python-based systems and criticizes TensorFlow 2.0's sluggishness compared to PyTorch, attributing these issues to legacy technical debt. Looking forward, Swift for TensorFlow is positioned as a future solution for high-performance computing, though widespread adoption remains years away due to current gaps in tooling and Apple's limited support for numeric programming.
- Lex Fridman1h 44m
Jeremy Howard: fast.ai Deep Learning Courses and Research | Lex Fridman Podcast #35
Jeremy Howard outlines the evolution of his programming preferences from historical environments like Microsoft Access to modern array-oriented languages such as J, while critiquing current deep learning frameworks for their inefficiency and lack of accessibility. He details how his organization, Fast AI, addresses critical bottlenecks in medical diagnostics by leveraging transfer learning and single-GPU training to empower domain experts in developing nations without requiring extensive computer science backgrounds. Beyond technical innovation, Howard emphasizes the ethical responsibility of practitioners to ensure explainability and human oversight, while warning against the economic risks of unchecked AI displacement and the regulatory hurdles that currently stifle medical data sharing.
- Milken Institute1h 1m
Technology and Jobs: Should Workers Worry?
Josh Barrow, Brad DeLong, Amy Webb, Gerald Huff, Jeremy Howard, Jonathan Zittrain
Brad DeLong, Amy Webb, Jeremy Howard, and Gerald Huff analyze the impact of automation on the labor market, arguing that current unemployment stems from macroeconomic mismanagement rather than robot displacement, even as AI rapidly outperforms humans in cognitive and manual sectors. While experts identify specific industries like banking, legal services, and manufacturing as vulnerable to disruption, data shows that the vast majority of economic activity remains in traditional service roles that are difficult to automate. The panel concludes that without proactive policy shifts toward solutions like universal basic income or reduced work hours, society risks a dystopian concentration of wealth as the traditional link between labor scarcity and income distribution breaks down.