Field
Applied AI
What AI is actually used for — as opposed to how it works. The economically enormous, professionally unglamorous end of the field.
Most of this site explains mechanisms. This field is about jobs: the tasks that AI does in the world, generating most of the money and almost none of the discussion.
Recommender systems are the clearest case. They decide what you watch, buy and read, they are plausibly the highest-revenue machine learning in existence, and they get a fraction of the attention that chatbots do — because they're invisible when they work. They also have the field's most interesting failure: the system trains on data it generated, so it doesn't just predict preference, it shapes it.
The pattern repeats. Time series forecasting spent forty years watching sophisticated methods lose to simple ones in public competitions — machine learning only convincingly won in 2020, and with gradient boosting rather than neural networks. Anomaly detection is defeated by arithmetic before any model is chosen: at a 1-in-10,000 base rate, a very good detector still drowns its users in false alarms.
Named entity recognition reports 93% and disappoints, because the benchmark is 1990s newswire. Sentiment analysis is a well-solved version of a possibly badly-posed task. Machine translation invented the attention mechanism that everything else is now built from, and remains fluent when it's wrong.
The through-line is worth stating: in applied work, the model is rarely the bottleneck. The objective, the labels, the base rate and the evaluation are.
Start with Recommender System — the most consequential AI most people never think about.
7 concepts in this field
Recommender System
The AI that decides what you see next — probably the most economically significant machine learning on earth, and the least discussed.
Time Series Forecasting
Predicting what comes next in a sequence over time — where simple methods beat sophisticated ones for forty years, and only recently stopped.
Anomaly Detection
Finding the unusual thing — where the base rate makes precision nearly impossible and almost every deployment drowns in false alarms.
Named Entity Recognition
Pulling the names, dates and places out of text — reported as solved, and reliably disappointing on anything that isn't news.
Sentiment Analysis
Deciding whether text is positive or negative — the most deployed NLP task, and the one whose target may not exist.
Machine Translation
Translating between languages automatically — the task that invented modern NLP, where fluency arrived long before reliability.
A/B Testing
Showing two versions to two random groups and measuring — the only method that tells you whether your model actually helped anyone.