Jeremy Howard — The Simple but Profound Insight Behind Diffusion

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 --- ⏳ 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 ---