What happens to me — the physician, the surgeon, the person who has spent forty years inside the system — while AI compresses a century of medical progress into a decade? A terrain map, in the spirit of Dario Amodei's 'Machines of Loving Grace' and drawing on the DeepMind documentary 'The Thinking Game.
Read articleA Nobel Prize laureate who built the most consequential scientific AI tool of the last decade, a cell biologist who won the Nobel for unpicking the machinery of the cell cycle, and the Oxford engineer who chaired the Royal Society's landmark report on AI in science sit down together at Carlton House Terrace. This is what they said — and what it means.
⏱ 22 min Read articleTwo structural shifts in medtech, examined against the primary sources: AI is moving from standalone pilot tools into the clinical and device fabric itself, and surgical robotics is scaling from a one-company category into a genuinely contested, multi-platform market. What the FDA filings, earnings calls, and funding rounds actually show.
⏱ 20 min Read articleA Microsoft diagnostic AI just scored 85.5% on hard NEJM cases against a 20% average for physicians. Seven takeaways from inside the compressed medical century — what's real, what's overstated, and what it means if you're the one holding the scalpel.
⏱ 10 min Read articleLesson 8 of Learning With Dr Neal — the capstone. Build a working retrieval assistant over a folder of your own documents: local embeddings, the twenty-line retriever, grounded answers with citations, and an honest recall@k evaluation that will teach you more about deployed clinical AI than any product demo ever will.
⏱ 40 min Read articleLesson 7 of Learning With Dr Neal. The assistant from Lesson 6 knows nothing about your hospital's guidelines, your patient's notes, or anything published since its training data was collected. Closing that gap without retraining is the job of embeddings and retrieval — turning documents into geometry, searching by meaning, and grounding answers in sources you control.
⏱ 30 min Read articleLesson 6 of Learning With Dr Neal. The raw next-token predictor you trained in Lesson 5 is the 'before' picture. This is the 'after' — instruction tuning, feedback-based refinement, why the same weights can host such different behaviours, and what the post-training pipeline means for the failure modes you'll meet in deployed clinical tools.
⏱ 30 min Read articleLesson 5 of Learning With Dr Neal. Stop reading and train one: take the TinyGPT from Lesson 4, feed it a corpus small enough for a laptop, write the training loop with your own hands, and watch generated text condense out of randomness — from noise, to word-shaped noise, to sentences.
⏱ 35 min Read articleLesson 4 of Learning With Dr Neal. How a language model actually turns a transcript into the next token: tokens and embeddings, why attention was the breakthrough, what a context window physically is — and a complete tiny GPT in PyTorch, small enough to read in one sitting and train on your laptop.
⏱ 35 min Read article