About
About fast.ai
Deep learning is transforming the world. We are making deep learning easier to use and getting more people from all backgrounds involved through our:
About our slogan
The world needs everyone involved with AI, no matter how unlikely your background.
Being cool is about being exclusive, and that’s the opposite of what we want. We want to make deep learning as accessible as possible– including to people using uncool languages like C#, uncool operating systems like Windows (which is used by the majority of the world), uncool datasets (way smaller than anything at Google, and in domain areas you’d consider obscure), and with uncool backgrounds (maybe you didn’t go to Stanford).
fast.ai in the news
- The Economist: New schemes teach the masses to build AI
- MIT Tech Review: The startup diversifying the AI workforce beyond just “techies”
- The New York Times: Finally, a Machine That Can Finish Your Sentence
- The Verge: An AI speed test shows clever coders can still beat tech giants like Google and Intel
- MIT Tech Review: A small team of student AI coders beats Google’s machine-learning code
- Forbes: Artificial Intelligence Education Transforms The Developing World
- ZDNet: fast.ai’s software could radically democratize AI
About the team
Jeremy Howard
Jeremy Howard is a data scientist, researcher, developer, educator, and entrepreneur. He created ULMFiT, the AI system at the heart of all of today’s major language models, including ChatGPT and Google Gemini. Jeremy is the founding CEO of Answer.AI, a new kind of AI R&D lab which creates practical end-user products based on foundational research breakthroughs. He is also the co-founder of fast.ai, a research institute dedicated to making deep learning more accessible, an Honorary Professor at the University of Queensland, and a Digital Fellow at Stanford University. Previously, Jeremy was a Distinguished Research Scientist at the University of San Francisco, where he was the founding chair of the Wicklow Artificial Intelligence in Medical Research Initiative.
Jeremy is a co-founder of the global Masks4All movement, including leading the largest evidence review of masks, published in the Proceedings of the National Academy of Science, and becoming the most read paper of all time on preprints.org. He wrote the first article to push for public mask use in the English-speaking work, in the Washington Post, along with articles in The Guardian, The Atlantic, The Conversation, and the Sydney Morning Herald, and he appeared on most of the major national US TV channels, including Good Morning America and Nightline.
He co-authored the book Deep Learning for Coders with Fastai and PyTorch, which has 5 stars on Amazon. Google’s Director of Research, Peter Norvig, said “This is one of the best sources for a programmer to become proficient in deep learning.” The book is based on Jeremy’s free online course, which is the world’s longest-running course on AI and deep learning. He also created the fastai software library, one of the world’s most popular deep learning frameworks.
Jeremy was the founding CEO of Enlitic, which was the first company to apply deep learning to medicine, and was selected as one of the world’s top 50 smartest companies by MIT Tech Review two years running. He was the President and Chief Scientist of the data science platform Kaggle, where he was the top ranked participant in international machine learning competitions 2 years running. He was the founding CEO of two successful Australian startups (FastMail, and Optimal Decisions Group–purchased by Lexis-Nexis). Before that, he spent 8 years in management consulting, at McKinsey & Co, and AT Kearney. Jeremy has invested in, mentored, and advised many startups, and contributed to many open source projects. His talk on TED.com, “The wonderful and terrifying implications of computers that can learn”, has over 2.5 million views.
Rachel Thomas
Photo by Gabriela Hasbun
Dr Rachel Thomas is a professor of practice at Queensland University of Technology and co-founder of fast.ai, which has been featured in The Economist, MIT Tech Review, and Forbes. She was the founding director of the USF Center for Applied Data Ethics, and was selected by Forbes as one of 20 Incredible Women in AI. Dr Thomas earned her math PhD at Duke, and was an early engineer at Uber.
Her writing has been read by over a million people; has been translated into Chinese, Spanish, Korean, & Portuguese; and has made the front page of Hacker News 9x. Some of her most popular articles include:
- Medicine’s Machine Learning Problem
- The problem with metrics is a big problem for AI
- If you think women in tech is just a pipeline issue, you haven’t been paying attention
- The real reason women quit tech, and how to address it
- Google’s AutoML: Cutting Through the Hype
- An Introduction to Deep Learning for Tabular Data
Rachel’s talks include:
- AI, Medicine, and Bias: Diversifying Your Dataset is Not Enough (Stanford AI in Medicine & Imaging Symposium)
- Getting Specific About Algorithmic Bias (featured talk at PyBay)
- The New Era in NLP (keynote at SciPy)
- The Barriers to AI are Lower than You Think (MIT Technology Review conference)