research

The most prominent study in this area found a language model predicting stock reactions from headlines, and it is usually reported as proving something it explicitly does not claim. This lesson reads it precisely, follows its qualifiers to their consequences, and shows why a real statistical result and a tradable strategy are different things.

Verification is now the expensive step, which changes what a sensible checking strategy looks like. This lesson covers the asymmetry between producing and refuting, deciding what to check before you read it, the questions that actually discriminate, and what a citation is worth.

Consulting bills for a bundle of research, analysis, synthesis and judgement, and AI compresses those unevenly. This lesson separates them, examines which parts clients were actually paying for, and confronts the pricing problem that follows when the visible artefact becomes cheap to produce.

AI touches nearly every stage of research, from reading the literature to analyzing data to writing the paper. Learn the categories of AI tools scientists use, the crucial difference between general assistants and specialized scientific AI like protein-structure predictors, and the caveats, hallucinated citations and reproducibility, that make rigor essential.

A practical guide to the research workflows where AI helps most. Learn how to use AI to search and synthesize the literature (and verify it), write and debug analysis code even without being a programmer, run and interpret statistics, and draft scientific writing, all with the verification and reproducibility discipline that keeps the work rigorous.

How two landmark papers — Kaplan et al. 2020 and DeepMind's Chinchilla 2022 — rewrote our understanding of compute-optimal training, why the industry now deliberately overtrains models, and how inference costs flip the math entirely.