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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
---
โณ 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
---
๐ Links
๐ Jeremy's previous Gradient Dissent episode (8/25/2022): http://wandb.me/gd-jeremy-howard
๐ "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/
๐ "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
๐ Jeremy's Twitter thread summary of "An evidence review of face masks against COVID-19": https://twitter.com/jeremyphoward/status/1348771993949151232
๐ 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
---
Connect with Jeremy and fast.ai:
๐ Jeremy on Twitter: https://twitter.com/jeremyphoward
๐ fast.ai on Twitter: https://twitter.com/FastDotAI
๐ Jeremy on LinkedIn: https://www.linkedin.com/in/howardjeremy/
---
๐ฌ Host: Lukas Biewald
๐น Producers: Riley Fields, Angelica Pan
4.8
6666 ratings
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
---
โณ 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
---
๐ Links
๐ Jeremy's previous Gradient Dissent episode (8/25/2022): http://wandb.me/gd-jeremy-howard
๐ "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/
๐ "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
๐ Jeremy's Twitter thread summary of "An evidence review of face masks against COVID-19": https://twitter.com/jeremyphoward/status/1348771993949151232
๐ 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
---
Connect with Jeremy and fast.ai:
๐ Jeremy on Twitter: https://twitter.com/jeremyphoward
๐ fast.ai on Twitter: https://twitter.com/FastDotAI
๐ Jeremy on LinkedIn: https://www.linkedin.com/in/howardjeremy/
---
๐ฌ Host: Lukas Biewald
๐น Producers: Riley Fields, Angelica Pan
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