Jeremy Howard — The Simple but Profound Insight Behind Diffusion

Jeremy Howard — The Simple but Profound Insight Behind Diffusion

Author: Lukas Biewald January 5, 2023 Duration: 1:12:57

Jeremy Howard is a co-founder of fast.ai, the non-profit research group behind the popular massive open online course "Practical Deep Learning for Coders", and the open source deep learning library "fastai".

Jeremy is also a co-founder of #Masks4All, a global volunteer organization founded in March 2020 that advocated for the public adoption of homemade face masks in order to help slow the spread of COVID-19. His Washington Post article "Simple DIY masks could help flatten the curve." went viral in late March/early April 2020, and is associated with the U.S CDC's change in guidance a few days later to recommend wearing masks in public.

In this episode, Jeremy explains how diffusion works and how individuals with limited compute budgets can engage meaningfully with large, state-of-the-art models. Then, as our first-ever repeat guest on Gradient Dissent, Jeremy revisits a previous conversation with Lukas on Python vs. Julia for machine learning.

Finally, Jeremy shares his perspective on the early days of COVID-19, and what his experience as one of the earliest and most high-profile advocates for widespread mask-wearing was like.

Show notes (transcript and links): http://wandb.me/gd-jeremy-howard-2

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⏳ Timestamps:

0:00 Intro

1:06 Diffusion and generative models

14:40 Engaging with large models meaningfully

20:30 Jeremy's thoughts on Stable Diffusion and OpenAI

26:38 Prompt engineering and large language models

32:00 Revisiting Julia vs. Python

40:22 Jeremy's science advocacy during early COVID days

1:01:03 Researching how to improve children's education

1:07:43 The importance of executive buy-in

1:11:34 Outro

1:12:02 Bonus: Weights & Biases

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📝 Links

📍 Jeremy's previous Gradient Dissent episode (8/25/2022): http://wandb.me/gd-jeremy-howard

📍 "Simple DIY masks could help flatten the curve. We should all wear them in public.", Jeremy's viral Washington Post article: https://www.washingtonpost.com/outlook/2020/03/28/masks-all-coronavirus/

📍 "An evidence review of face masks against COVID-19" (Howard et al., 2021), one of the first peer-reviewed papers on the effectiveness of wearing masks: https://www.pnas.org/doi/10.1073/pnas.2014564118

📍 Jeremy's Twitter thread summary of "An evidence review of face masks against COVID-19": https://twitter.com/jeremyphoward/status/1348771993949151232

📍 Read more about Jeremy's mask-wearing advocacy: https://www.smh.com.au/world/north-america/australian-expat-s-push-for-universal-mask-wearing-catches-fire-in-the-us-20200401-p54fu2.html

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Connect with Jeremy and fast.ai:

📍 Jeremy on Twitter: https://twitter.com/jeremyphoward

📍 fast.ai on Twitter: https://twitter.com/FastDotAI

📍 Jeremy on LinkedIn: https://www.linkedin.com/in/howardjeremy/

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💬 Host: Lukas Biewald

📹 Producers: Riley Fields, Angelica Pan


Lukas Biewald hosts Gradient Dissent: Conversations on AI, a series that moves beyond theoretical discussions to examine how artificial intelligence is actually built and deployed. Each episode features a direct, unscripted talk with a leading practitioner-you’ll hear from engineers and researchers at places like NVIDIA, Meta, Google, Lyft, and OpenAI. The focus is on the tangible challenges and breakthroughs they encounter, from initial research to the complex reality of putting models into production. This isn't about abstract futures; it's a grounded look at the decisions shaping the field right now. Biewald, bringing his perspective from Weights & Biases, steers conversations toward the practical trade-offs and collaborative efforts that define modern AI work. For anyone in technology or business who wants to understand the mechanics behind the headlines, this podcast offers a rare, candid window into the process. You’ll come away with a clearer sense of how ideas become functional systems and what it really takes to operate at the cutting edge.
Author: Language: English Episodes: 100

Gradient Dissent: Conversations on AI
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