Jeremy Howard
Jeremy Howard is an Australian data scientist, entrepreneur, and educator, known for his work indeep learning and AI. He is the co-founder of fast.ai, a research institute dedicated to making deep learning more accessible. Alongside Dr Rachel Thomas, Jeremy created the widely-used course,DeepLearning for Coders,and developed the fastai software library, which is one of the most popular deep learning frameworks. His bookDeep Learning for Coders with fastai and PyTorchis highly rated, withGoogle’sDirector of Research,Peter Norvig, praising its depth.
Jeremy's notable contributions to AI include the ULMFiT algorithm, a foundational method for training large language models (LLMs), which has been influential in the development of modern models like GPT andChatGPT. He co-foundedAnswer.AIwith Eric Ries, anAI R&D labfocused on creating practical end-user products based on research breakthroughs.
Jeremy was also the founding CEO ofEnlitic, where he applied deep learning to medical diagnostics. Under his leadership, Enlitic was ranked among the world’s smartest companies by MIT Tech Review. He also served as President and Chief Scientist of Kaggle, a platform for data science competitions, where he ranked first globally in 2010 and 2011.
Jeremy has a diverse entrepreneurial background. He co-foundedFastMail, an email service, and Optimal Decisions Group, a company focused on insurance pricing optimisation. Both ventures were later acquired by larger companies. He has also been actively involved in public advocacy during the COVID-19 pandemic, co-founding the globalMasks4Allmovement and leading a major evidence review on mask efficacy, published in theProceedings of the National Academy of Science.
Academically, Jeremy holds roles as an honorary professor at the University of Queensland and a Digital Fellow at Stanford University. He has been involved with institutions likeCSIRO’sData61, where he advises on AI research and development, and Singularity University, where he taught data science and machine learning.
Jeremy's work has been featured in numerous high-impact publications. He has contributed toThe Economist,The Guardian,The Washington Post, and many others. His TED talk on the implications of computers that learn has garnered over 2.5 million views. Jeremy continues to mentor startups, contribute to open-source projects, and focus on expanding the reach and impact of AI technologies.
