Opening article
Anthropic research examines induction heads, which are mechanisms that complete patterns like A to B followed by A to B. According to the research, these induction heads may serve as the mechanistic source for the majority of in-context learning in transformers.
We gave Claude plain-language specifications for eight quantum circuits, along with an automated evaluator to check and score its designs. Working largely on its own, Claude improved on the best published circuits, in some cases by an order of magnitude or more, which could bring practical quantum computers closer than previously anticipated.
The decades-old mystery of the origins of one of the most popular cryptographic standards has been explained.
How will AI reshape the economy in the years ahead? What is the core source of disagreement about AI's future impact on the economy? This note presents new survey evidence on these questions.
Understanding the mechanistic sources of in-context learning in transformers provides critical insight into how large language models process information and generalize from examples.