Writing

Algal bloom alerts describe the observed present; AI and numerical models estimate change over the next seven days. This article explains how to read the forecast target, site, reference time, horizon, and uncertainty together.

Code that finishes without an error is not verified code. This article explains three recurring hiding places for silent errors in AI-written Python—data-type assumptions, range boundaries, and swallowed exceptions—and a 30-second verification routine.

AI uncertainty does not automatically increase when a model encounters data unlike its training data. This article distinguishes OOD from three kinds of distribution shift and sets out criteria for stress testing and revalidation.

In transfer learning for infectious-disease forecasting, models trained on data from the most dissimilar countries consistently outperformed those trained on the most similar countries. This article explains the 30-country experiment and the limitations that must accompany its results.

MC Dropout runs one model in many forms; deep ensembles train multiple models separately. Twenty accessible points explain how these two approaches calculate what AI does not know, along with their costs and limitations.

Why does a claim with a decimal point feel more objective? Twenty short thoughts on recovering the definitions, denominators, comparisons, uncertainty, and uses hidden behind a number.

In 2027, what may matter more than whether we use AI is the ability to design goals, context, tools, and verification as one system—and to improve the rubrics used to evaluate the results.

In an age when AI can write all the code, the reason to learn Python lies not in writing code but in verifying it. When should we trust or question code that fails silently, or AI that invents missing data to produce an answer?

Just as statistics moved from point estimates to confidence intervals, AI is entering an era in which it must report not just one answer, but how much that answer can be trusted. Part 1 of the Trustworthy AI series.