We founded Anthropic because we believe the impact of AI might be comparable to that of the industrial and scientific revolutions, but we aren’t confident it will go well. And we also believe this level of impact could s
The three main ingredients leading to predictable1 improvements in AI performance are training data, computation, and improved algorithms. In the mid-2010s, some of us noticed that larger AI systems were consistently sma
If you’re willing to entertain the views outlined above, then it’s not very hard to argue that AI could be a risk to our safety and security. There are two common sense reasons to be concerned. First, it may be tricky to
We believe it’s hard to make rapid progress in science and engineering without close contact with our object of study. Constantly iterating against a source of “ground truth” is usually crucial for scientific progress. I
A major reason Anthropic exists as an organization is that we believe it's necessary to do safety research on "frontier" AI systems. This requires an institution which can both work with large models and prioritize safet
Some researchers who care about safety are motivated by a strong opinion on the nature of AI risks. Our experience is that even predicting the behavior and properties of AI systems in the near future is very difficult. M
We categorize research projects at Anthropic into three areas: - Capabilities: AI research aimed at making AI systems generally better at any sort of task, including writing, image processing or generation, game playing,
We’re currently working in a variety of different directions to discover how to train safe AI systems, with some projects addressing distinct threat models and capability levels. Some key ideas include: - Mechanistic int
Claude discovers a novel enzyme system with CRISPR-like repeats We’re announcing a new life sciences research group and laboratory at Anthropic. This post introduces the team behind this work and shares early results in
