# Jeremy Howard

Jeremy Howard is an entrepreneur and researcher who co-founded fast.ai, pioneered practical transfer learning for NLP with ULMFiT, and helped propose the llms.txt standard for AI-readable websites.

Jeremy Howard is an Australian entrepreneur, researcher and educator known for co-founding fast.ai, a widely used deep learning education platform and open-source library, and for helping create ULMFiT, an early and influential [transfer-learning](https://www.wikiprompt.org/wiki/transfer-learning) method for [natural-language-processing](https://www.wikiprompt.org/wiki/natural-language-processing).

Before moving fully into AI, Howard was president and chief scientist of Kaggle, the machine learning competition platform, and founded Enlitic, one of the first companies to apply [deep-learning](https://www.wikiprompt.org/wiki/deep-learning) to medical diagnostic imaging. He co-founded fast.ai in 2016 with Rachel Thomas, building both a free online course, "Practical Deep Learning for Coders," and an accompanying software library, with the explicit goal of making state-of-the-art deep learning accessible to programmers without requiring an advanced mathematics background.

## ULMFiT and transfer learning for NLP

In 2018, Howard and researcher Sebastian Ruder published ULMFiT (Universal Language Model Fine-tuning), a method for [pretraining](https://www.wikiprompt.org/wiki/pretraining) a language model on a large general text corpus and then fine-tuning it cheaply on a specific downstream task with comparatively little labeled data. ULMFiT demonstrated that the [transfer-learning](https://www.wikiprompt.org/wiki/transfer-learning) approach already standard in [computer-vision](https://www.wikiprompt.org/wiki/computer-vision), where models pretrained on [imagenet](https://www.wikiprompt.org/wiki/imagenet) were fine-tuned for other tasks, could work just as well for text, predating the wave of pretrain-then-fine-tune language models that included [bert](https://www.wikiprompt.org/wiki/bert) and later [GPT-style](https://www.wikiprompt.org/wiki/gpt-3) models. The paper is widely cited as an important precursor to the modern [large-language-model](https://www.wikiprompt.org/wiki/large-language-model) paradigm.

## llms.txt and later work

In 2024, Howard helped propose [llms-txt](https://www.wikiprompt.org/wiki/llms-txt), a simple text-file standard placed at a website's root intended to give AI systems a concise, machine-readable summary of a site's content, drawing an explicit analogy to the long-standing robots.txt convention for search crawlers. The proposal was part of a broader push around making websites more legible to AI agents and chatbots as a growing share of web traffic and citations began coming from AI systems rather than direct human browsing, an area sometimes described as [generative-engine-optimization](https://www.wikiprompt.org/wiki/generative-engine-optimization).

Throughout his career Howard has emphasized practical, code-first teaching and open tooling over theory-first academic approaches, and fast.ai's courses and library have been credited with bringing a large number of practitioners into the deep learning field who might not otherwise have had access to graduate-level machine learning training.

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Source: https://www.wikiprompt.org/wiki/jeremy-howard
License: CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)
Last updated: 2026-09-02T20:32:22.967404+00:00