Jeremy Howard

Jeremy Howard, co-creator of Fast.ai and a leading voice in machine learning, shares his journey from skepticism to success in AI. He discusses the groundbreaking ULMFiT approach to fine-tuning language models and how it faced initial resistance despite its effectiveness. Howard emphasizes the importance of democratizing AI, creating accessible tools, and fostering community engagement. He also explores the evolution of training dynamics in language models and the power of technology to empower diverse communities, advocating for open-source initiatives.

Jeremy Howard, Founder of Answer.ai and a prominent figure in deep learning and fast.ai, joins the conversation to share innovative insights. He discusses revolutionary AI model training techniques that allow anyone to use minimal resources to achieve maximum output. Howard emphasizes collaboration within diverse teams, steering clear of traditional hierarchies, to foster creativity. They also explore the FastHTML framework, showcasing how it simplifies web development. The podcast dives into the ethics surrounding AI governance and the promise of dialogue engineering in transforming coding environments.

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 Intro1:06 Diffusion and generative models14:40 Engaging with large models meaningfully20:30 Jeremy's thoughts on Stable Diffusion and OpenAI26:38 Prompt engineering and large language models32:00 Revisiting Julia vs. Python40:22 Jeremy's science advocacy during early COVID days1:01:03 Researching how to improve children's education1:07:43 The importance of executive buy-in1:11:34 Outro1:12:02 Bonus: Weights & Biases---📝 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---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/---💬 Host: Lukas Biewald📹 Producers: Riley Fields, Angelica Pan

In this dynamic discussion, Jonathan Frankle, Chief Scientist at MosaicML, shares insights on their $1.3 billion acquisition by Databricks. Lin Qiao, CEO of Fireworks AI, talks about optimizing PyTorch for inference. Aman Sanger from Cursor reveals innovative memory strategies for AI coding. Aravind Srinivas discusses the impressive growth of Perplexity AI, hitting 1 million installs, while Jeremy Howard emphasizes the need for accessible AI. Together, they explore the vibrant AI startup landscape showcased at NeurIPS 2023, reflecting on innovation, collaboration, and the future of technology.

Jeremy Howard, founder of fast.ai and a distinguished AI researcher, discusses making deep learning accessible to all. He explores the advantages of self-funding startups, shares innovative learning strategies, and reveals his journey in programming. The conversation touches on using AI to revolutionize healthcare and the balance between privacy and data use. Howard also highlights hands-on learning in AI, success stories from his courses, and the societal implications of artificial intelligence, advocating for ethical considerations in its development.