Anthropic is an AI safety and research company that’s working to build reliable, interpretable, and steerable AI systems. Large, general systems of today can have significant benefits, but can also be unpredictable, unreliable, and opaque: our goal is to make progress on these issues. For now, we’re primarily focused on research towards these goals; down the road, we foresee many opportunities for our work to create value commercially and for public benefit. We are a small, highly collaborative group of researchers, engineers, policy experts, and operational leaders, with experience spanning a variety of disciplines. Our research interests span multiple areas including natural language, human feedback, scaling laws, reinforcement learning, code generation, and interpretability.

Our research interests span multiple areas including natural language, human feedback, scaling laws, reinforcement learning, code generation, and interpretability. The easiest way to understand our research directions is to read some of our papers. Our first AI alignment paper focused on simple baselines and investigations. Our second AI alignment paper explores how to train a general language assistant to be helpful, but without providing harmful advice or exhibiting bad behaviors. Our first interpretability paper explores a mathematical framework for trying to reverse engineer transformer, and language models. Our first societal impacts paper explores the technical traits of large generative models and the motivations and challenges people face in building and deploying them. Our second interpretability paper explores the hypothesis that induction heads (discovered in our first interpretability paper) are the mechanism driving in-context learning.