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The interview process at Anthropic varies based on role and candidate, but our standard process looks like this: Step 1 Resume. Submit your resume via our website. Step 2 Exploratory chat. You’ll have a chat with one of our staff to discuss your career interests and relevant experience, and learn more about Anthropic. Step 3 Skills Assessment
Claude can now use tools. Tool use, which enables Claude to interact with external tools and APIs, is now generally available across the entire Claude 3 model family on the Anthropic Messages API, Amazon Bedrock, and Google Cloud's Vertex AI. With tool use, Claude can perform tasks, manipulate data, and provide more dynamic—and accurate ...
anthropic: [adjective] of or relating to human beings or the period of their existence on earth.
Today we’re releasing Claude 3 Haiku, the fastest and most affordable model in its intelligence class. With state-of-the-art vision capabilities and strong performance on industry benchmarks, Haiku is a versatile solution for a wide range of enterprise applications.
Anthropic is an AI safety and research company based in San Francisco. Our interdisciplinary team has experience across ML, physics, policy, and product. Together, we generate research and create reliable, beneficial AI systems. Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.
Sep 25, 2023 · Anthropic is an AI safety and research company based in San Francisco. Our interdisciplinary team has deep experience across machine learning, physics, policy, and product. Together, we create reliable, interpretable, and steerable AI systems. Anthropic’s flagship product is Claude, an AI assistant focused on being helpful, harmless, and honest.
Anthropic 1 Introduction This report includes the model card [1] for Claude models, focusing on Claude 2, along with the results of a range of safety, alignment, and capabilities evaluations. We have been iterating on the training and evaluation of Claude-type models since our first work on Reinforcement Learning from Human Feedback (RLHF) [2];
