Bias Is a Product Decision Before It Is a Technical One
Most of the harm attributed to biased models is decided upstream: what the system is asked to predict, and what data was available to predict it from.
Articles
Building AI Products for Users on Bad Connections
Streaming responses, hopeful reconnects and generous payloads all assume a connection that a great many users do not have.
The Data Work Behind the Models, and Who Does It
Annotation, moderation and evaluation are done by people, a great many of them in East and West Africa, and the conditions attached deserve more attention than they get.
Retrieval-Augmented Generation, Explained Properly
The idea is simple and the failure modes are not. Most disappointing implementations fail at retrieval, not at generation.
Evaluating an AI Feature Before You Ship It
You cannot unit-test a language model, which is not the same as being unable to test it. A small hand-built evaluation set is worth more than any public benchmark.
What a Longer Context Window Actually Buys You
Context windows have grown enormously. The ability to use everything in them has not grown at the same rate, and the gap matters.
Why Models Make Things Up, and What Genuinely Reduces It
Confident invention is a consequence of what these systems are trained to do. It can be reduced substantially and it cannot be eliminated by prompting.
The AGI Conversation Is Crowding Out Better Questions
Arguing about a hypothetical future system is more comfortable than arguing about the deployed ones. That is largely why it is so popular.
Benchmarks Are Not Progress
A number that improves every quarter is reassuring and largely decorative. Measuring what a system is good at is much harder than ranking it.