A fraud detection model trained before a global pandemic learns fraud patterns from a world where people commute to offices, shop in stores, and travel on predictable schedules. When those patterns ...

A language model trained only to predict the next word in a sequence is a very different thing from an AI assistant. The raw pre-trained model is impressive in its own way. It generates fluent, ...

There's a counterintuitive idea at the heart of machine learning that trips up a lot of people encountering it for the first time: More learning is not always better. A model that has learned its ...

The history of large language model improvement has largely been a story about training. Bigger models. More data. More compute spent during training. The scaling laws, covered in a separate piece in ...

There's a persistent tension in applied AI between capability and cost. The most capable models are large. Large models are expensive to run. Running them at scale, serving millions of requests per ...

A neural network without activation functions is just a very complicated way of doing linear algebra. Every layer would multiply its inputs by a matrix of weights and add a bias. Stack as many of ...

In the 1970s, British economist Charles Goodhart made an observation about monetary policy that has turned out to apply far beyond economics. When a measure becomes a target, it ceases to be a good ...

The term "hallucination" entered mainstream conversation alongside the rise of large language models, and it spread quickly because it named something people were already experiencing. You ask an AI a ...

There's a point in the growth of any dataset where queries that used to return in seconds start taking minutes, and the instinct is to throw more compute at the problem. Sometimes that helps. Often ...

Under current U.S. copyright law and the Copyright Office's evolving guidance, content generated by AI without meaningful human authorship is not eligible for ...

A neural network is only as useful as the neurons that are actually doing work. That sounds obvious. What's less obvious is that a meaningful fraction of neurons in a trained neural network can end up ...

Training a machine learning model is an optimization process. You define an objective, a loss function that measures how badly the model is performing, and you run an algorithm that adjusts the ...