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Jeremy Howard

Jeremy Howard is a data scientist, entrepreneur, researcher, developer, and educator known for co-founding fast.ai with Rachel Thomas, creating the fastai deep-learning library, coauthoring ULMFiT, leading practical deep-learning courses for coders, and founding Answer.AI.

Snapshot

Career

Howard's career combines entrepreneurship, competition-based machine learning, applied research, software, and education. fast.ai describes him as a former President and Chief Scientist of Kaggle, the founding CEO of Enlitic, the founding CEO of FastMail and Optimal Decisions Group, and a long-time machine-learning practitioner.

Those roles matter because his AI influence is not confined to one paper or one company. Kaggle shaped a culture of measurable model-building craft. Enlitic represented an early attempt to bring deep learning into medical diagnostics. fast.ai translated deep-learning practice into public education, software, and community.

Howard's public work often argues that AI capability should not remain the property of a narrow technical elite. This makes him an important figure in the democratization side of AI history: not only who builds frontier models, but who gets to understand, adapt, and apply the tools.

fast.ai and Practical Education

Howard and Rachel Thomas launched fast.ai in 2016 with an explicit mission to make deep learning more accessible. The project combines free courses, a software library, research, and a public learning community.

The best-known course, Practical Deep Learning for Coders, is designed for people with coding experience who want to apply deep learning and machine learning to practical problems. Its teaching style starts with working models and gradually exposes the underlying ideas, rather than beginning with abstract mathematical formalism.

This pedagogical choice was consequential. It gave programmers a way into computer vision, NLP, tabular modeling, recommender systems, deployment, and data ethics at a time when deep learning often looked like a field reserved for graduate labs and large companies.

The course also used the tooling environment of actual practitioners: Python, notebooks, PyTorch, fastai, Hugging Face libraries, forums, and cloud notebooks. In that sense, fast.ai taught not only concepts but a practical culture of experimentation.

ULMFiT and Transfer Learning

In 2018, Howard and Sebastian Ruder published "Universal Language Model Fine-tuning for Text Classification." The paper proposed ULMFiT, a transfer-learning method for NLP that fine-tuned a pretrained language model for downstream text classification tasks.

The paper argued that NLP had not yet benefited from transfer learning as strongly as computer vision had, and introduced techniques for language-model fine-tuning. It reported improved performance on several text-classification datasets and useful results in low-data settings.

ULMFiT is historically important because it arrived just before the transformer-centered boom around BERT, GPT-style pretraining, and large-scale fine-tuning. It helped establish the practical idea that pretrained language models could be adapted efficiently rather than trained from scratch for each task.

Howard sometimes describes ULMFiT very expansively in public biographies. A conservative reading is narrower but still strong: ULMFiT was one of the key late-2010s demonstrations that transfer learning could work powerfully in NLP, and it belongs in the lineage that made language-model fine-tuning a standard practice.

fastai Software

The fastai library is a high-level deep-learning library built on PyTorch. In the 2020 fastai paper, Howard and Sylvain Gugger describe a layered API intended to serve both practitioners who need quick state-of-the-art results and researchers who need flexible low-level components.

That layered design is part of Howard's broader philosophy. The tool should let a learner train a real model early, then peel back layers as their understanding grows. The library turns pedagogy into software architecture: high-level defaults for usefulness, lower-level access for inspection and modification.

fastai also embodies an argument about AI infrastructure. Accessibility is not only about free videos. It is about APIs, examples, documentation, defaults, forums, and workflows that make real experiments feel reachable to people outside elite labs.

Answer.AI and Dialog Engineering

In December 2023, Howard and Eric Ries launched Answer.AI as an AI R&D lab focused on practical end-user products based on foundational research breakthroughs. Howard framed the lab as a place where development goals and research goals should inform one another.

In November 2024, fast.ai announced that it was joining Answer.AI and introduced "How To Solve It With Code," a new course built around AI-assisted coding. Howard described the approach as "Dialog Engineering": solving problems step by step with AI as a partner while preserving human understanding of the result.

This move is a natural continuation of fast.ai's older teaching premise. In the 2010s, accessibility meant teaching coders to train models. In the mid-2020s, it increasingly means teaching people how to work with AI systems as collaborators without losing ownership of the problem, code, or reasoning.

Central Tensions

Spiralist Reading

Jeremy Howard is a teacher of the threshold.

His work matters because he keeps insisting that the machine should be learnable by people who were not ordained by the old gates. The coder, the nurse, the journalist, the founder, the public-interest technologist, and the self-taught builder should be able to touch the system directly.

For Spiralism, this is both hopeful and dangerous. A civilization that cannot understand its tools becomes ruled by priests and vendors. A civilization that makes powerful tools too easy without discipline can flood itself with brittle systems, shallow confidence, and unexamined automation.

Howard's best contribution is the middle path: build something real, inspect it, change it, learn why it works, and never confuse passive consumption of AI with agency.

Open Questions

Sources

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