Daniela Amodei
@DanielaAmodei
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President @AnthropicAI. Formerly @OpenAI, @Stripe, congressional staffer, global development
San Francisco, CA
Joined September 2011
RT @AnthropicAI: Introducing Claude 2! Our latest model has improved performance in coding, math and reasoning. It can produce longer respo….
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RT @AnthropicAI: Neural networks often pack many unrelated concepts into a single neuron – a puzzling phenomenon known as 'polysemanticity'….
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RT @AnthropicAI: In "Language Models (Mostly) Know What They Know", we show that language models can evaluate whether what they say is true….
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RT @AnthropicAI: Transformer MLP neurons are challenging to understand. We find that using a different activation function (Softmax Linear….
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RT @AnthropicAI: In a new paper, we show that repeating only a small fraction of the data used to train a language model (albeit many times….
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I’m looking forward to what’s to come. And we’re hiring!
anthropic.com
Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.
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As well as steerability and robustness -- reinforcement learning - societal impacts - and more!.
arxiv.org
Large-scale pre-training has recently emerged as a technique for creating capable, general purpose, generative models such as GPT-3, Megatron-Turing NLG, Gopher, and many others. In this paper, we...
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This includes work on interpretability - and - and some interpretability resources like Garcon and Pysvelte
github.com
A library for bridging Python and HTML/Javascript (via Svelte) for creating interactive visualizations - anthropics/PySvelte
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I’m so proud of the amazing team we’ve assembled at Anthropic and the research we’ve done to date.
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Excited to announce our latest fundraising round! We’re genuinely honored to be entrusted with the resources to continue our work in frontier AI safety and research.
We’ve raised $580 million in a Series B. This will help us further develop our research to build usable, reliable AI systems. Find out more:
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RT @AnthropicAI: Glad @QuantaMagazine highlights progress on induction heads/rigorous interpretability by @ch402, @catherineols, @nelhage a….
quantamagazine.org
Language processing programs are notoriously hard to interpret, but smaller versions can provide important insights into how they work.
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RT @AnthropicAI: We've trained a natural language assistant to be more helpful and harmless by using reinforcement learning with human feed….
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RT @AnthropicAI: On the @FLIxrisk podcast, we discuss AI research, AI safety, and what it was like starting Anthropic during COVID. https:/….
futureoflife.org
Daniela and Dario Amodei join the FLI Podcast to discuss Anthropic, a new company building safe, reliable, interpretable, and steerable AI systems.
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RT @AnthropicAI: In our second interpretability paper, we revisit “induction heads”. In 2+ layer transformers these pattern-completion hea….
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RT @AnthropicAI: Our first societal impacts paper explores the technical traits of large generative models and the motivations and challeng….
arxiv.org
Large-scale pre-training has recently emerged as a technique for creating capable, general purpose, generative models such as GPT-3, Megatron-Turing NLG, Gopher, and many others. In this paper, we...
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RT @AnthropicAI: Our first interpretability paper explores a mathematical framework for trying to reverse engineer transformer language mod….
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RT @AnthropicAI: Our first AI alignment paper, focused on simple baselines and investigations: A General Language Assistant as a Laboratory….
arxiv.org
Given the broad capabilities of large language models, it should be possible to work towards a general-purpose, text-based assistant that is aligned with human values, meaning that it is helpful,...
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We’re going to be focused on pushing forward our research for the next few months and are hoping to have more to share later this year. Thrilled to be working with so many talented colleagues!.
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Excited to announce what we’ve been working on this year - @AnthropicAI, an AI safety and research company. If you’d like to help us combine safety research with scaling ML models while thinking about societal impacts, check out our careers page
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