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The family includes three state-of-the-art models in ascending order of capability: Claude 3 Haiku, Claude 3 Sonnet, and Claude 3 Opus. Each successive model offers increasingly powerful performance, allowing users to select the optimal balance of intelligence, speed, and \",\"_key\":\"6c7c68493bd10\",\"_type\":\"span\"},{\"_type\":\"span\",\"marks\":[\"e690c5685704\"],\"text\":\"cost\",\"_key\":\"1309100e4264\"},{\"marks\":[],\"text\":\" for their specific application.\",\"_key\":\"443337375366\",\"_type\":\"span\"}],\"_type\":\"block\",\"style\":\"normal\"},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"496fb9c9f406\",\"markDefs\":[{\"_type\":\"link\",\"href\":\"https://www.anthropic.com/supported-countries\",\"_key\":\"b2d9865719c6\"}],\"children\":[{\"marks\":[],\"text\":\"Opus and Sonnet are now available to use in claude.ai and the Claude API which is now generally available in \",\"_key\":\"b0e67afac1800\",\"_type\":\"span\"},{\"_type\":\"span\",\"marks\":[\"b2d9865719c6\"],\"text\":\"159 countries\",\"_key\":\"cfd556a17bfc\"},{\"marks\":[],\"text\":\". 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It exhibits near-human levels of comprehension and fluency on complex tasks, leading the frontier of general intelligence.\",\"_key\":\"b2c1b6abdeda0\"}]},{\"markDefs\":[{\"_type\":\"link\",\"href\":\"https://www.anthropic.com/claude-3-model-card\",\"_key\":\"aca6e529fda4\"}],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"All \",\"_key\":\"ea2185156be50\"},{\"_type\":\"span\",\"marks\":[\"aca6e529fda4\"],\"text\":\"Claude 3\",\"_key\":\"f41e11b72a5a\"},{\"text\":\" models show increased capabilities in analysis and forecasting, nuanced content creation, code generation, and conversing in non-English languages like Spanish, Japanese, and French.\",\"_key\":\"ce9cf674fb4f\",\"_type\":\"span\",\"marks\":[]}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"791ebb9539f9\"},{\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Below is a comparison of the Claude 3 models to those of our peers on multiple benchmarks [1] of capability:\",\"_key\":\"e66d47cf953f0\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"b77f3b42f058\",\"markDefs\":[]},{\"_type\":\"image\",\"style\":\"inline\",\"_key\":\"96b31e47a780\",\"asset\":{\"_ref\":\"image-9ad98d612086fe52b3042f9183414669b4d2a3da-2200x1954-png\",\"_type\":\"reference\"}},{\"style\":\"h3\",\"_key\":\"122d99955c94\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Near-instant results\",\"_key\":\"94ebd918295e0\"}],\"_type\":\"block\"},{\"markDefs\":[],\"children\":[{\"text\":\"The Claude 3 models can power live customer chats, auto-completions, and data extraction tasks where responses must be immediate and in real-time.\",\"_key\":\"d636443bda240\",\"_type\":\"span\",\"marks\":[]}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"a716760d26f9\"},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"ef96ccac31b1\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Haiku is the fastest and most cost-effective model on the market for its intelligence category. It can read an information and data dense research paper on arXiv (~10k tokens) with charts and graphs in less than three seconds. Following launch, we expect to improve performance even further.\",\"_key\":\"c30fe08114e00\"}]},{\"markDefs\":[],\"children\":[{\"marks\":[],\"text\":\"For the vast majority of workloads, Sonnet is 2x faster than Claude 2 and Claude 2.1 with higher levels of intelligence. It excels at tasks demanding rapid responses, like knowledge retrieval or sales automation. Opus delivers similar speeds to Claude 2 and 2.1, but with much higher levels of intelligence.\",\"_key\":\"5d256ef7b4150\",\"_type\":\"span\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"2c59a54bb9cf\"},{\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Strong vision capabilities\",\"_key\":\"e67b4b4e5440\"}],\"_type\":\"block\",\"style\":\"h3\",\"_key\":\"118680357dd1\"},{\"style\":\"normal\",\"_key\":\"4c7c669b3031\",\"markDefs\":[],\"children\":[{\"_key\":\"69e310a95d7b0\",\"_type\":\"span\",\"marks\":[],\"text\":\"The Claude 3 models have sophisticated vision capabilities on par with other leading models. They can process a wide range of visual formats, including photos, charts, graphs and technical diagrams. We鈥檙e particularly excited to provide this new modality to our enterprise customers, some of whom have up to 50% of their knowledge bases encoded in various formats such as PDFs, flowcharts, or presentation slides.\"}],\"_type\":\"block\"},{\"_type\":\"image\",\"style\":\"inline\",\"_key\":\"5b0b439445a2\",\"asset\":{\"_ref\":\"image-6b66d86ff0c180e95bc6ad2e6e4a1843aa74c80f-2200x960-png\",\"_type\":\"reference\"}},{\"_key\":\"e1adb462b46f\",\"markDefs\":[],\"children\":[{\"marks\":[],\"text\":\"Fewer refusals\",\"_key\":\"102fdd5307960\",\"_type\":\"span\"}],\"_type\":\"block\",\"style\":\"h3\"},{\"_key\":\"19ea17e89d91\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Previous Claude models often made unnecessary refusals that suggested a lack of contextual understanding. We鈥檝e made meaningful progress in this area: Opus, Sonnet, and Haiku are significantly less likely to refuse to answer prompts that border on the system鈥檚 guardrails than previous generations of models. As shown below, the Claude 3 models show a more nuanced understanding of requests, recognize real harm, and refuse to answer harmless prompts much less often.\",\"_key\":\"bf8b70935bb90\"}],\"_type\":\"block\",\"style\":\"normal\"},{\"asset\":{\"_ref\":\"image-d1fbcf3d58ebc2dcd2e98aac995d70bf50cb2e9c-2188x918-png\",\"_type\":\"reference\"},\"_type\":\"image\",\"style\":\"inline\",\"_key\":\"a78c6685664b\"},{\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Improved accuracy\",\"_key\":\"7f14def3c85c\"}],\"_type\":\"block\",\"style\":\"h3\",\"_key\":\"2e52cdedf221\",\"markDefs\":[]},{\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Businesses of all sizes rely on our models to serve their customers, making it imperative for our model outputs to maintain high accuracy at scale. To assess this, we use a large set of complex, factual questions that target known weaknesses in current models. We categorize the responses into correct answers, incorrect answers (or hallucinations), and admissions of uncertainty, where the model says it doesn鈥檛 know the answer instead of providing incorrect information. 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We're also excited to release a series of features to enhance our models' capabilities, particularly for enterprise use cases and large-scale deployments. These new features will include Tool Use (aka function calling), interactive coding (aka REPL), and more advanced agentic capabilities.\",\"_key\":\"36d68827df070\"}],\"_type\":\"block\",\"style\":\"normal\"},{\"_key\":\"0d8c2d562fb2\",\"markDefs\":[],\"children\":[{\"marks\":[],\"text\":\"As we push the boundaries of AI capabilities, we鈥檙e equally committed to ensuring that our safety guardrails keep apace with these leaps in performance. Our hypothesis is that being at the frontier of AI development is the most effective way to steer its trajectory towards positive societal outcomes.\",\"_key\":\"568f855ea89d0\",\"_type\":\"span\"}],\"_type\":\"block\",\"style\":\"normal\"},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"334da1e777ff\",\"markDefs\":[{\"_type\":\"link\",\"href\":\"https://www.anthropic.com/claude\",\"_key\":\"0e64a357cbbf\"}],\"children\":[{\"_key\":\"6b06e5bc26df0\",\"_type\":\"span\",\"marks\":[],\"text\":\"We鈥檙e excited to see what you create with Claude 3 and hope you will give us feedback to make Claude an even more useful assistant and creative companion. To start building with Claude, visit \"},{\"_key\":\"1b11e7efec5a\",\"_type\":\"span\",\"marks\":[\"0e64a357cbbf\"],\"text\":\"anthropic.com/claude\"},{\"_type\":\"span\",\"marks\":[],\"text\":\". \",\"_key\":\"a3578cd2523e\"}]}],\"cta\":{\"url\":\"https://anthropic.com/claude\",\"_type\":\"link\",\"text\":\"Try Claude 3\"},\"fileAsset\":null},\"text\":\"Claude 3 Opus\",\"_key\":\"818055e9fe1f\",\"_type\":\"link\"}],\"_key\":\"6efa8c7f7494\",\"title\":\"Claude model family\",\"ctas\":null}]},{\"category\":\"Commitments\",\"card\":{\"title\":\"ISO 42001 certification\",\"url\":\"/news/anthropic-achieves-iso-42001-certification-for-responsible-ai\",\"backgroundColor\":\"fig\",\"illustration\":{\"description\":null,\"height\":202,\"url\":\"https://cdn.sanity.io/images/4zrzovbb/website/9caa14b490b0f02f7b3e5a37e6ea171f182a2544-202x202.svg\",\"width\":202},\"category\":\"Announcements\"},\"sections\":[{\"links\":[{\"text\":\"Transparency\",\"_key\":\"f1bf2286ea51\",\"url\":\"/transparency\",\"page\":null,\"_type\":\"link\"},{\"_type\":\"link\",\"page\":{\"subjects\":[{\"_type\":\"tag\",\"label\":\"Announcements\",\"_key\":\"announcements\",\"value\":\"announcements\"}],\"cta\":{\"_type\":\"link\",\"text\":\"Read the Responsible Scaling Policy \",\"url\":\"http://anthropic.com/rsp \"},\"slug\":{\"current\":\"announcing-our-updated-responsible-scaling-policy\",\"_type\":\"slug\"},\"fileAsset\":null,\"body\":[{\"_key\":\"177824ba8e58\",\"markDefs\":[{\"_type\":\"link\",\"href\":\"https://arxiv.org/abs/2403.10462\",\"_key\":\"a8d027e86aae\"}],\"children\":[{\"text\":\"Today we are publishing a significant update to our Responsible Scaling Policy (RSP), the risk governance framework we use to mitigate potential catastrophic risks from frontier AI systems. \",\"_key\":\"b66b73206ce30\",\"_type\":\"span\",\"marks\":[\"strong\"]},{\"_type\":\"span\",\"marks\":[],\"text\":\"This update introduces a more flexible and nuanced approach to assessing and managing AI risks while maintaining our commitment not to train or deploy models unless we have implemented adequate safeguards. Key improvements include new capability thresholds to indicate when we will upgrade our safeguards, refined processes for evaluating model capabilities and the adequacy of our safeguards (inspired by \",\"_key\":\"b66b73206ce31\"},{\"_type\":\"span\",\"marks\":[\"a8d027e86aae\"],\"text\":\"safety case methodologies\",\"_key\":\"b66b73206ce32\"},{\"_type\":\"span\",\"marks\":[],\"text\":\"), and new measures for internal governance and external input. By learning from our implementation experiences and drawing on risk management practices used in other high-consequence industries, we aim to better prepare for the rapid pace of AI advancement.\",\"_key\":\"b66b73206ce33\"}],\"_type\":\"block\",\"style\":\"normal\"},{\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"The promise and challenge of advanced AI\",\"_key\":\"9405114da2700\"}],\"_type\":\"block\",\"style\":\"h3\",\"_key\":\"1c3077afa1f5\"},{\"style\":\"normal\",\"_key\":\"750223c4acfb\",\"markDefs\":[],\"children\":[{\"text\":\"As frontier AI models advance, they have the potential to bring about transformative benefits for our society and economy. AI could accelerate scientific discoveries, revolutionize healthcare, enhance our education system, and create entirely new domains for human creativity and innovation. However, frontier AI systems also present new challenges and risks that warrant careful study and effective safeguards.\",\"_key\":\"a1dcb0091c340\",\"_type\":\"span\",\"marks\":[]}],\"_type\":\"block\"},{\"children\":[{\"marks\":[],\"text\":\"In September 2023, we \",\"_key\":\"b75aa93fa2c80\",\"_type\":\"span\"},{\"_key\":\"b75aa93fa2c81\",\"_type\":\"span\",\"marks\":[\"4791430b5ec2\"],\"text\":\"released\"},{\"text\":\" our Responsible Scaling Policy, a framework for managing risks from increasingly capable AI systems. After a year of implementation and learning, we are now sharing a significantly updated version that reflects practical insights and accounts for advancing technological capabilities.\",\"_key\":\"b75aa93fa2c82\",\"_type\":\"span\",\"marks\":[]}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"c8b393e2960c\",\"markDefs\":[{\"_type\":\"link\",\"href\":\"https://www.anthropic.com/news/anthropics-responsible-scaling-policy\",\"_key\":\"4791430b5ec2\"}]},{\"_key\":\"a1948e4cc873\",\"markDefs\":[{\"_type\":\"link\",\"href\":\"https://www.anthropic.com/legal/aup\",\"_key\":\"ba04e1121891\"},{\"_key\":\"32798753cd99\",\"_type\":\"link\",\"href\":\"https://www.anthropic.com/research#societal-impacts\"}],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Although this policy focuses on catastrophic risks like the categories listed below, they are not the only risks that we monitor and prepare for. Our \",\"_key\":\"5d488fbe87cd0\"},{\"_type\":\"span\",\"marks\":[\"ba04e1121891\"],\"text\":\"Usage Policy\",\"_key\":\"5d488fbe87cd1\"},{\"_key\":\"5d488fbe87cd2\",\"_type\":\"span\",\"marks\":[],\"text\":\" sets forth our standards for the use of our products, including rules that prohibit using our models to spread misinformation, incite violence or hateful behavior, or engage in fraudulent or abusive practices. We continually refine our technical measures for enforcing our trust and safety standards at scale. Further, we conduct research to understand the broader \"},{\"_type\":\"span\",\"marks\":[\"32798753cd99\"],\"text\":\"societal impacts\",\"_key\":\"5d488fbe87cd3\"},{\"text\":\" of our models. Our Responsible Scaling Policy complements our work in these areas, contributing to our understanding of current and potential risks.\",\"_key\":\"5d488fbe87cd4\",\"_type\":\"span\",\"marks\":[]}],\"_type\":\"block\",\"style\":\"normal\"},{\"_key\":\"3b55c0bceff9\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"A framework for proportional safeguards\",\"_key\":\"5525570b2a010\"}],\"_type\":\"block\",\"style\":\"h3\"},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"ad1d58dbdd5b\",\"markDefs\":[{\"_type\":\"link\",\"href\":\"https://en.wikipedia.org/wiki/Biosafety_level\",\"_key\":\"f8e6f3368061\"}],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"As before, we maintain our core commitment: we will not train or deploy models unless we have implemented safety and security measures that keep risks below acceptable levels. Our RSP is based on the principle of proportional protection: safeguards that scale with potential risks. To do this, we use \",\"_key\":\"cdd8f6a2997b0\"},{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"AI Safety Level Standards (ASL Standards)\",\"_key\":\"3517fb380020\"},{\"_type\":\"span\",\"marks\":[],\"text\":\", graduated sets of safety and security measures that become more stringent as model capabilities increase. Inspired by \",\"_key\":\"aede15153eee\"},{\"_type\":\"span\",\"marks\":[\"f8e6f3368061\"],\"text\":\"Biosafety Levels,\",\"_key\":\"cdd8f6a2997b1\"},{\"_type\":\"span\",\"marks\":[],\"text\":\" these begin at ASL-1 for models that have very basic capabilities (for example, chess-playing bots) and progress through ASL-2, ASL-3, and so on.\",\"_key\":\"cdd8f6a2997b2\"}]},{\"_key\":\"18e2a21a8d24\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"In our updated policy, we have refined our methodology for assessing specific capabilities (and their associated risks) and implementing proportional safety and security measures. Our updated framework has two key components:\",\"_key\":\"94961b6308040\"}],\"_type\":\"block\",\"style\":\"normal\"},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"110d48e0214c\",\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Capability Thresholds:\",\"_key\":\"4fa1b51cfc900\"},{\"marks\":[],\"text\":\" Specific AI abilities that, if reached, would require stronger safeguards than our current baseline.\",\"_key\":\"f90398ece8b9\",\"_type\":\"span\"}],\"level\":1},{\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"132d0001c926\",\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"marks\":[\"strong\"],\"text\":\"Required Safeguards: \",\"_key\":\"d0053cd57ea60\",\"_type\":\"span\"},{\"text\":\"The specific ASL Standards needed to mitigate risks once a Capability Threshold has been reached.\",\"_key\":\"663a30c5e5b1\",\"_type\":\"span\",\"marks\":[]}]},{\"style\":\"normal\",\"_key\":\"36479c291519\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"At present, all of our models operate under ASL-2 Standards, which reflect current industry best practices. Our updated policy defines two key Capability Thresholds that would require upgraded safeguards:\",\"_key\":\"a03bfcd2c90a0\"}],\"_type\":\"block\"},{\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Autonomous AI Research and Development:\",\"_key\":\"113a03232f950\"},{\"_type\":\"span\",\"marks\":[],\"text\":\" If a model can independently conduct complex AI research tasks typically requiring human expertise鈥攑otentially significantly accelerating AI development in an unpredictable way鈥攚e require elevated security standards (potentially ASL-4 or higher standards) and additional safety assurances to avoid a situation where development outpaces our ability to address emerging risks.\",\"_key\":\"559bae44e291\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"7960a2e610b8\",\"listItem\":\"bullet\",\"markDefs\":[]},{\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Chemical, Biological, Radiological, and Nuclear (CBRN) weapons:\",\"_key\":\"04f8008234340\"},{\"_type\":\"span\",\"marks\":[],\"text\":\" If a model can meaningfully assist someone with a basic technical background in creating or deploying CBRN weapons, we require enhanced security and deployment safeguards (ASL-3 standards).\",\"_key\":\"e87c145d832e\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"b1e7e0200f28\",\"listItem\":\"bullet\"},{\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"ASL-3 safeguards involve enhanced security measures and deployment controls. On the security side, this will include internal access controls and more robust protection of model weights. For deployment risks, we plan to implement a multi-layered approach to prevent misuse, including real-time and asynchronous monitoring, rapid response protocols, and thorough pre-deployment red teaming.\",\"_key\":\"c21521b5c8660\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"5028d8139378\"},{\"_type\":\"block\",\"style\":\"h3\",\"_key\":\"9d149ea1669f\",\"markDefs\":[],\"children\":[{\"_key\":\"c9476c97d6320\",\"_type\":\"span\",\"marks\":[],\"text\":\"Implementation and oversight\"}]},{\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"To contribute to effective implementation of the policy, we have established:\",\"_key\":\"0898891e3fd00\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"081edde580ae\",\"markDefs\":[]},{\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"1ef822ef2fa2\",\"listItem\":\"bullet\",\"markDefs\":[{\"_type\":\"link\",\"href\":\"http://www.anthropic.com/rsp-updates\",\"_key\":\"e97965203d94\"}],\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Capability assessments\",\"_key\":\"65ad635ebc020\"},{\"_type\":\"span\",\"marks\":[],\"text\":\": Routine model evaluations based on our Capability Thresholds to determine whether our current safeguards are still appropriate. (Summaries of past assessments are available \",\"_key\":\"ee8bb23f5481\"},{\"_type\":\"span\",\"marks\":[\"e97965203d94\"],\"text\":\"here\",\"_key\":\"65ad635ebc021\"},{\"text\":\".)\",\"_key\":\"65ad635ebc022\",\"_type\":\"span\",\"marks\":[]}]},{\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"b5fc19b935bb\",\"listItem\":\"bullet\",\"markDefs\":[{\"_type\":\"link\",\"href\":\"http://www.anthropic.com/rsp-updates\",\"_key\":\"d6b74c71ad6d\"}],\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Safeguard assessments: \",\"_key\":\"fd96692cf5950\"},{\"_type\":\"span\",\"marks\":[],\"text\":\"Routine evaluation of the effectiveness of our security and deployment safety measures to assess whether we have met the Required Safeguards bar. (Summaries of these decisions will be available \",\"_key\":\"bbdf4a0ded13\"},{\"_key\":\"fd96692cf5951\",\"_type\":\"span\",\"marks\":[\"d6b74c71ad6d\"],\"text\":\"here\"},{\"_key\":\"fd96692cf5952\",\"_type\":\"span\",\"marks\":[],\"text\":\".)\"}]},{\"markDefs\":[{\"_type\":\"link\",\"href\":\"https://arxiv.org/abs/2403.10462\",\"_key\":\"dd97b493d101\"}],\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Documentation and decision-making: \",\"_key\":\"ed8d6ec6368a0\"},{\"_type\":\"span\",\"marks\":[],\"text\":\"Processes for documenting the capability and safeguard assessments, inspired by procedures (such as \",\"_key\":\"d451a32ef6cb\"},{\"_key\":\"ed8d6ec6368a1\",\"_type\":\"span\",\"marks\":[\"dd97b493d101\"],\"text\":\"safety case methodologies\"},{\"_type\":\"span\",\"marks\":[],\"text\":\") common in high-reliability industries.\",\"_key\":\"ed8d6ec6368a2\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"8074ca752c13\",\"listItem\":\"bullet\"},{\"_key\":\"60c9b7dabd40\",\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"text\":\"Measures for internal governance and external input: \",\"_key\":\"ca95d473f4a90\",\"_type\":\"span\",\"marks\":[\"strong\"]},{\"_type\":\"span\",\"marks\":[],\"text\":\"Our assessment methodology will be backed up by internal stress-testing in addition to our existing internal reporting process for safety issues. 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These included procedural issues such as completing a set of evaluations three days later than scheduled or a lack of clarity on how and where we should note any changes to our placeholder evaluations. We also flagged some evaluations where we may have been able to elicit slightly better model performance through implementing standard techniques (such as chain-of-thought or best-of-N).\",\"_key\":\"8932eeee77830\",\"_type\":\"span\",\"marks\":[]}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"142037ff4afb\"},{\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"In all cases, we found these instances posed minimal risk to the safety of our models. We used the additional three days to refine and improve our evaluations; the different set of evaluations we used provided a more accurate assessment than the placeholder evaluations; and our evaluation methodology still showed we were sufficiently far from the thresholds. From this, we learned two valuable lessons to incorporate into our updated framework: we needed to incorporate more flexibility into our policies, and we needed to improve our process for tracking compliance with the RSP. You can read more \",\"_key\":\"2c9de8a77cf10\"},{\"_type\":\"span\",\"marks\":[\"724c1bcc37c6\"],\"text\":\"here\",\"_key\":\"2c9de8a77cf11\"},{\"_type\":\"span\",\"marks\":[],\"text\":\".\",\"_key\":\"2c9de8a77cf12\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"fdd39c742e14\",\"markDefs\":[{\"_type\":\"link\",\"href\":\"http://anthropic.com/rsp-updates\",\"_key\":\"724c1bcc37c6\"}]},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"c84e70ee115b\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Since we first released the RSP a year ago, our goal has been to offer an example of a framework that others might draw inspiration from when crafting their own AI risk governance policies. We hope that proactively sharing our experiences implementing our own policy will help other companies in implementing their own risk management frameworks and contribute to the establishment of best practices across the AI ecosystem.\",\"_key\":\"855d60a456920\"}]},{\"children\":[{\"_key\":\"81a081f760a10\",\"_type\":\"span\",\"marks\":[],\"text\":\"Looking ahead\"}],\"_type\":\"block\",\"style\":\"h3\",\"_key\":\"20a9139b3a51\",\"markDefs\":[]},{\"style\":\"normal\",\"_key\":\"78baf5750bd0\",\"markDefs\":[],\"children\":[{\"_key\":\"252e5f0060c50\",\"_type\":\"span\",\"marks\":[],\"text\":\"The frontier of AI is advancing rapidly, making it challenging to anticipate what safety measures will be appropriate for future systems. All aspects of our safety program will continue to evolve: our policies, evaluation methodology, safeguards, and our research into potential risks and mitigations.\"}],\"_type\":\"block\"},{\"style\":\"normal\",\"_key\":\"2afadf824368\",\"markDefs\":[],\"children\":[{\"text\":\"Additionally, Co-Founder and Chief Science Officer Jared Kaplan will serve as Anthropic鈥檚 Responsible Scaling Officer, succeeding Co-Founder and Chief Technology Officer Sam McCandlish who held this role over the last year. Sam oversaw the RSP鈥檚 initial implementation and will continue to focus on his duties as Chief Technology Officer. As we work to scale up our efforts on implementing the RSP, we鈥檙e also opening a position for a Head of Responsible Scaling. 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Many of our teams now contribute to risk management via the RSP, including:\",\"_key\":\"ec2a1674bc0a\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"88e4ecab085f\"},{\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"marks\":[],\"text\":\"Frontier Red Team (responsible for threat modeling and capability assessments)\",\"_key\":\"0a4d9f27a0cd0\",\"_type\":\"span\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"803749c5db22\"},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"fe760029da3a\",\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Trust \u0026 Safety (responsible for developing deployment safeguards)\",\"_key\":\"dc5557c893ca0\"}],\"level\":1},{\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Security and Compliance (responsible for security safeguards and risk 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It exhibits near-human levels of comprehension and fluency on complex tasks, leading the frontier of general intelligence.\",\"_key\":\"b2c1b6abdeda0\"}]},{\"markDefs\":[{\"_type\":\"link\",\"href\":\"https://www.anthropic.com/claude-3-model-card\",\"_key\":\"aca6e529fda4\"}],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"All \",\"_key\":\"ea2185156be50\"},{\"_type\":\"span\",\"marks\":[\"aca6e529fda4\"],\"text\":\"Claude 3\",\"_key\":\"f41e11b72a5a\"},{\"text\":\" models show increased capabilities in analysis and forecasting, nuanced content creation, code generation, and conversing in non-English languages like Spanish, Japanese, and French.\",\"_key\":\"ce9cf674fb4f\",\"_type\":\"span\",\"marks\":[]}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"791ebb9539f9\"},{\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Below is a comparison of the Claude 3 models to those of our peers on multiple benchmarks [1] of capability:\",\"_key\":\"e66d47cf953f0\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"b77f3b42f058\",\"markDefs\":[]},{\"_type\":\"image\",\"style\":\"inline\",\"_key\":\"96b31e47a780\",\"asset\":{\"_ref\":\"image-9ad98d612086fe52b3042f9183414669b4d2a3da-2200x1954-png\",\"_type\":\"reference\"}},{\"style\":\"h3\",\"_key\":\"122d99955c94\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Near-instant results\",\"_key\":\"94ebd918295e0\"}],\"_type\":\"block\"},{\"markDefs\":[],\"children\":[{\"text\":\"The Claude 3 models can power live customer chats, auto-completions, and data extraction tasks where responses must be immediate and in real-time.\",\"_key\":\"d636443bda240\",\"_type\":\"span\",\"marks\":[]}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"a716760d26f9\"},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"ef96ccac31b1\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Haiku is the fastest and most cost-effective model on the market for its intelligence category. It can read an information and data dense research paper on arXiv (~10k tokens) with charts and graphs in less than three seconds. Following launch, we expect to improve performance even further.\",\"_key\":\"c30fe08114e00\"}]},{\"markDefs\":[],\"children\":[{\"marks\":[],\"text\":\"For the vast majority of workloads, Sonnet is 2x faster than Claude 2 and Claude 2.1 with higher levels of intelligence. It excels at tasks demanding rapid responses, like knowledge retrieval or sales automation. Opus delivers similar speeds to Claude 2 and 2.1, but with much higher levels of intelligence.\",\"_key\":\"5d256ef7b4150\",\"_type\":\"span\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"2c59a54bb9cf\"},{\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Strong vision capabilities\",\"_key\":\"e67b4b4e5440\"}],\"_type\":\"block\",\"style\":\"h3\",\"_key\":\"118680357dd1\"},{\"style\":\"normal\",\"_key\":\"4c7c669b3031\",\"markDefs\":[],\"children\":[{\"_key\":\"69e310a95d7b0\",\"_type\":\"span\",\"marks\":[],\"text\":\"The Claude 3 models have sophisticated vision capabilities on par with other leading models. They can process a wide range of visual formats, including photos, charts, graphs and technical diagrams. We鈥檙e particularly excited to provide this new modality to our enterprise customers, some of whom have up to 50% of their knowledge bases encoded in various formats such as PDFs, flowcharts, or presentation slides.\"}],\"_type\":\"block\"},{\"_type\":\"image\",\"style\":\"inline\",\"_key\":\"5b0b439445a2\",\"asset\":{\"_ref\":\"image-6b66d86ff0c180e95bc6ad2e6e4a1843aa74c80f-2200x960-png\",\"_type\":\"reference\"}},{\"_key\":\"e1adb462b46f\",\"markDefs\":[],\"children\":[{\"marks\":[],\"text\":\"Fewer refusals\",\"_key\":\"102fdd5307960\",\"_type\":\"span\"}],\"_type\":\"block\",\"style\":\"h3\"},{\"_key\":\"19ea17e89d91\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Previous Claude models often made unnecessary refusals that suggested a lack of contextual understanding. We鈥檝e made meaningful progress in this area: Opus, Sonnet, and Haiku are significantly less likely to refuse to answer prompts that border on the system鈥檚 guardrails than previous generations of models. As shown below, the Claude 3 models show a more nuanced understanding of requests, recognize real harm, and refuse to answer harmless prompts much less often.\",\"_key\":\"bf8b70935bb90\"}],\"_type\":\"block\",\"style\":\"normal\"},{\"asset\":{\"_ref\":\"image-d1fbcf3d58ebc2dcd2e98aac995d70bf50cb2e9c-2188x918-png\",\"_type\":\"reference\"},\"_type\":\"image\",\"style\":\"inline\",\"_key\":\"a78c6685664b\"},{\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Improved accuracy\",\"_key\":\"7f14def3c85c\"}],\"_type\":\"block\",\"style\":\"h3\",\"_key\":\"2e52cdedf221\",\"markDefs\":[]},{\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Businesses of all sizes rely on our models to serve their customers, making it imperative for our model outputs to maintain high accuracy at scale. To assess this, we use a large set of complex, factual questions that target known weaknesses in current models. We categorize the responses into correct answers, incorrect answers (or hallucinations), and admissions of uncertainty, where the model says it doesn鈥檛 know the answer instead of providing incorrect information. Compared to Claude 2.1, Opus demonstrates a twofold improvement in accuracy (or correct answers) on these challenging open-ended questions while also exhibiting reduced levels of incorrect answers.\",\"_key\":\"692654be5f0a0\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"17889c1cfe91\"},{\"_key\":\"0d0ef8e29c15\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"In addition to producing more trustworthy responses, we will soon enable citations in our Claude 3 models so they can point to precise sentences in reference material to verify their answers.\\n\",\"_key\":\"dabf8032cfab0\"}],\"_type\":\"block\",\"style\":\"normal\"},{\"_key\":\"06407e249d10\",\"asset\":{\"_ref\":\"image-7cb598c6a9fa58c12b77f67ee2067feaac4a2de0-2200x896-png\",\"_type\":\"reference\"},\"_type\":\"image\",\"style\":\"inline\"},{\"_type\":\"block\",\"style\":\"h3\",\"_key\":\"fcfee2b49205\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Long context and near-perfect recall\",\"_key\":\"5db36703ea090\"}]},{\"_key\":\"48daeea9bc01\",\"markDefs\":[],\"children\":[{\"marks\":[],\"text\":\"The Claude 3 family of models will initially offer a 200K context window upon launch. However, all three models are capable of accepting inputs exceeding 1 million tokens and we may make this available to select customers who need enhanced processing power.\",\"_key\":\"69e1354a626c0\",\"_type\":\"span\"}],\"_type\":\"block\",\"style\":\"normal\"},{\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"To process long context prompts effectively, models require robust recall capabilities. The 'Needle In A Haystack' (NIAH) evaluation measures a model's ability to accurately recall information from a vast corpus of data. We enhanced the robustness of this benchmark by using one of 30 random needle/question pairs per prompt and testing on a diverse crowdsourced corpus of documents. Claude 3 Opus not only achieved near-perfect recall, surpassing 99% accuracy, but in some cases, it even identified the limitations of the evaluation itself by recognizing that the \\\"needle\\\" sentence appeared to be artificially inserted into the original text by a human.\",\"_key\":\"bde3af4ac3670\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"7dc25016e60c\"},{\"children\":[{\"_key\":\"f4679e3b7a80\",\"_type\":\"span\",\"marks\":[],\"text\":\"\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"0015926d72c6\",\"markDefs\":[]},{\"asset\":{\"_ref\":\"image-d2aa12b60e9c57e7057924bd8878d754c7b3d8e7-2200x1088-png\",\"_type\":\"reference\"},\"_type\":\"image\",\"style\":\"inline\",\"_key\":\"91eb42b8ae41\"},{\"markDefs\":[],\"children\":[{\"text\":\"Responsible design\",\"_key\":\"dd43651c5b1f0\",\"_type\":\"span\",\"marks\":[]}],\"_type\":\"block\",\"style\":\"h3\",\"_key\":\"aa397b8752f3\"},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"9c58cc4307c0\",\"markDefs\":[{\"href\":\"https://www.anthropic.com/news/constitutional-ai-harmlessness-from-ai-feedback\",\"_key\":\"b608daa539d5\",\"_type\":\"link\"}],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"We鈥檝e developed the Claude 3 family of models to be as trustworthy as they are capable. We have several dedicated teams that track and mitigate a broad spectrum of risks, ranging from misinformation and CSAM to biological misuse, election interference, and autonomous replication skills. We continue to develop methods such as \",\"_key\":\"aa543e64358e0\"},{\"_type\":\"span\",\"marks\":[\"b608daa539d5\"],\"text\":\"Constitutional AI\",\"_key\":\"a30dab63a714\"},{\"text\":\" that improve the safety and transparency of our models, and have tuned our models to mitigate against privacy issues that could be raised by new modalities.\",\"_key\":\"5b0d68dcb1cf\",\"_type\":\"span\",\"marks\":[]}]},{\"_key\":\"538d63deb7c9\",\"markDefs\":[{\"_type\":\"link\",\"href\":\"https://aclanthology.org/2022.findings-acl.165/\",\"_key\":\"8b8565815b0e\"}],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Addressing biases in increasingly sophisticated models is an ongoing effort and we鈥檝e made strides with this new release. As shown in the model card, Claude 3 shows less biases than our previous models according to the \",\"_key\":\"e6f420b3c2a00\"},{\"_type\":\"span\",\"marks\":[\"8b8565815b0e\"],\"text\":\"Bias Benchmark for Question Answering (BBQ)\",\"_key\":\"9b0d6e529f30\"},{\"_type\":\"span\",\"marks\":[],\"text\":\". We remain committed to advancing techniques that reduce biases and promote greater neutrality in our models, ensuring they are not skewed towards any particular partisan stance.\",\"_key\":\"838b9d7eaaf5\"}],\"_type\":\"block\",\"style\":\"normal\"},{\"style\":\"normal\",\"_key\":\"27e6c2393187\",\"markDefs\":[{\"_type\":\"link\",\"href\":\"https://www.anthropic.com/news/anthropics-responsible-scaling-policy\",\"_key\":\"a6acdb8c6c9a\"},{\"_type\":\"link\",\"href\":\"https://www.anthropic.com/news/red-teaming-language-models-to-reduce-harms-methods-scaling-behaviors-and-lessons-learned\",\"_key\":\"41ce6e74280d\"},{\"_key\":\"bff6073a96fa\",\"_type\":\"link\",\"href\":\"https://www.whitehouse.gov/briefing-room/statements-releases/2023/07/21/fact-sheet-biden-harris-administration-secures-voluntary-commitments-from-leading-artificial-intelligence-companies-to-manage-the-risks-posed-by-ai/\"},{\"_type\":\"link\",\"href\":\"https://www.whitehouse.gov/briefing-room/statements-releases/2023/10/30/fact-sheet-president-biden-issues-executive-order-on-safe-secure-and-trustworthy-artificial-intelligence/\",\"_key\":\"34ede29139ed\"},{\"href\":\"https://www.anthropic.com/claude-3-model-card\",\"_key\":\"149416bc7eda\",\"_type\":\"link\"}],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"While the Claude 3 model family has advanced on key measures of biological knowledge, cyber-related knowledge, and autonomy compared to previous models, it remains at AI Safety Level 2 (ASL-2) per our \",\"_key\":\"758320609d91\"},{\"_type\":\"span\",\"marks\":[\"a6acdb8c6c9a\"],\"text\":\"Responsible Scaling Policy\",\"_key\":\"2c7eb5dc236c\"},{\"marks\":[],\"text\":\". Our \",\"_key\":\"10f95acda51c\",\"_type\":\"span\"},{\"text\":\"red teaming\",\"_key\":\"ba6380a89fd3\",\"_type\":\"span\",\"marks\":[\"41ce6e74280d\"]},{\"text\":\" evaluations (performed in line with our \",\"_key\":\"db279d3786f3\",\"_type\":\"span\",\"marks\":[]},{\"_type\":\"span\",\"marks\":[\"bff6073a96fa\"],\"text\":\"White House commitments\",\"_key\":\"1941ee0371ec\"},{\"text\":\" and the \",\"_key\":\"c1d1c30839be\",\"_type\":\"span\",\"marks\":[]},{\"marks\":[\"34ede29139ed\"],\"text\":\"2023 US Executive Order\",\"_key\":\"bef6d5b627ce\",\"_type\":\"span\"},{\"marks\":[],\"text\":\") have concluded that the models present negligible potential for catastrophic risk at this time. We will continue to carefully monitor future models to assess their proximity to the ASL-3 threshold. Further safety details are available in the \",\"_key\":\"0f45812a053e\",\"_type\":\"span\"},{\"_type\":\"span\",\"marks\":[\"149416bc7eda\"],\"text\":\"Claude 3 model card\",\"_key\":\"cfb31e9a0274\"},{\"_type\":\"span\",\"marks\":[],\"text\":\".\\n\",\"_key\":\"f716b43bd491\"}],\"_type\":\"block\"},{\"_type\":\"block\",\"style\":\"h3\",\"_key\":\"51f637ad41d6\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Easier to use\",\"_key\":\"50d56ac801a9\"}]},{\"markDefs\":[],\"children\":[{\"marks\":[],\"text\":\"The Claude 3 models are better at following complex, multi-step instructions. They are particularly adept at adhering to brand voice and response guidelines, and developing customer-facing experiences our users can trust. In addition, the Claude 3 models are better at producing popular structured output in formats like JSON鈥攎aking it simpler to instruct Claude for use cases like natural language classification and sentiment analysis.\\n\",\"_key\":\"f53f06fc023c0\",\"_type\":\"span\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"9eaf0570bbc3\"},{\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"\",\"_key\":\"6d09ab0857020\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"22de8c15e566\",\"markDefs\":[]},{\"_key\":\"f0ecb4920f1b\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Model details\",\"_key\":\"712d5a95b1a60\"}],\"_type\":\"block\",\"style\":\"h3\"},{\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Claude 3 Opus \",\"_key\":\"fc4095508509\"},{\"_type\":\"span\",\"marks\":[],\"text\":\"is our most intelligent model, with best-in-market performance on highly complex tasks. It can navigate open-ended prompts and sight-unseen scenarios with remarkable fluency and human-like understanding. Opus shows us the outer limits of what鈥檚 possible with generative AI.\",\"_key\":\"6440fb6a2110\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"48366d1ea0ba\",\"markDefs\":[]},{\"caption\":\"data\",\"_key\":\"192931d9bf4b\",\"rows\":[{\"cells\":[{\"_type\":\"tableCell\",\"_key\":\"09f50f07543d\",\"content\":[{\"style\":\"normal\",\"_key\":\"07d7ed9dab53\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Cost\\n\\n\",\"_key\":\"fd6270d108f70\"},{\"_key\":\"40a645503125\",\"_type\":\"span\",\"marks\":[\"em\"],\"text\":\" [Input $/million tokens | Output $/million 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We're also excited to release a series of features to enhance our models' capabilities, particularly for enterprise use cases and large-scale deployments. These new features will include Tool Use (aka function calling), interactive coding (aka REPL), and more advanced agentic capabilities.\",\"_key\":\"36d68827df070\"}],\"_type\":\"block\",\"style\":\"normal\"},{\"_key\":\"0d8c2d562fb2\",\"markDefs\":[],\"children\":[{\"marks\":[],\"text\":\"As we push the boundaries of AI capabilities, we鈥檙e equally committed to ensuring that our safety guardrails keep apace with these leaps in performance. Our hypothesis is that being at the frontier of AI development is the most effective way to steer its trajectory towards positive societal outcomes.\",\"_key\":\"568f855ea89d0\",\"_type\":\"span\"}],\"_type\":\"block\",\"style\":\"normal\"},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"334da1e777ff\",\"markDefs\":[{\"_type\":\"link\",\"href\":\"https://www.anthropic.com/claude\",\"_key\":\"0e64a357cbbf\"}],\"children\":[{\"_key\":\"6b06e5bc26df0\",\"_type\":\"span\",\"marks\":[],\"text\":\"We鈥檙e excited to see what you create with Claude 3 and hope you will give us feedback to make Claude an even more useful assistant and creative companion. To start building with Claude, visit \"},{\"_key\":\"1b11e7efec5a\",\"_type\":\"span\",\"marks\":[\"0e64a357cbbf\"],\"text\":\"anthropic.com/claude\"},{\"_type\":\"span\",\"marks\":[],\"text\":\". \",\"_key\":\"a3578cd2523e\"}]}],\"cta\":{\"url\":\"https://anthropic.com/claude\",\"_type\":\"link\",\"text\":\"Try Claude 3\"},\"fileAsset\":null},\"text\":\"Claude 3 Opus\",\"_key\":\"818055e9fe1f\",\"_type\":\"link\"}],\"_key\":\"6efa8c7f7494\",\"title\":\"Claude model family\",\"ctas\":null}]},{\"category\":\"Commitments\",\"card\":{\"title\":\"ISO 42001 certification\",\"url\":\"/news/anthropic-achieves-iso-42001-certification-for-responsible-ai\",\"backgroundColor\":\"fig\",\"illustration\":{\"description\":null,\"height\":202,\"url\":\"https://cdn.sanity.io/images/4zrzovbb/website/9caa14b490b0f02f7b3e5a37e6ea171f182a2544-202x202.svg\",\"width\":202},\"category\":\"Announcements\"},\"sections\":[{\"links\":[{\"text\":\"Transparency\",\"_key\":\"f1bf2286ea51\",\"url\":\"/transparency\",\"page\":null,\"_type\":\"link\"},{\"_type\":\"link\",\"page\":{\"subjects\":[{\"_type\":\"tag\",\"label\":\"Announcements\",\"_key\":\"announcements\",\"value\":\"announcements\"}],\"cta\":{\"_type\":\"link\",\"text\":\"Read the Responsible Scaling Policy \",\"url\":\"http://anthropic.com/rsp \"},\"slug\":{\"current\":\"announcing-our-updated-responsible-scaling-policy\",\"_type\":\"slug\"},\"fileAsset\":null,\"body\":[{\"_key\":\"177824ba8e58\",\"markDefs\":[{\"_type\":\"link\",\"href\":\"https://arxiv.org/abs/2403.10462\",\"_key\":\"a8d027e86aae\"}],\"children\":[{\"text\":\"Today we are publishing a significant update to our Responsible Scaling Policy (RSP), the risk governance framework we use to mitigate potential catastrophic risks from frontier AI systems. \",\"_key\":\"b66b73206ce30\",\"_type\":\"span\",\"marks\":[\"strong\"]},{\"_type\":\"span\",\"marks\":[],\"text\":\"This update introduces a more flexible and nuanced approach to assessing and managing AI risks while maintaining our commitment not to train or deploy models unless we have implemented adequate safeguards. Key improvements include new capability thresholds to indicate when we will upgrade our safeguards, refined processes for evaluating model capabilities and the adequacy of our safeguards (inspired by \",\"_key\":\"b66b73206ce31\"},{\"_type\":\"span\",\"marks\":[\"a8d027e86aae\"],\"text\":\"safety case methodologies\",\"_key\":\"b66b73206ce32\"},{\"_type\":\"span\",\"marks\":[],\"text\":\"), and new measures for internal governance and external input. By learning from our implementation experiences and drawing on risk management practices used in other high-consequence industries, we aim to better prepare for the rapid pace of AI advancement.\",\"_key\":\"b66b73206ce33\"}],\"_type\":\"block\",\"style\":\"normal\"},{\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"The promise and challenge of advanced AI\",\"_key\":\"9405114da2700\"}],\"_type\":\"block\",\"style\":\"h3\",\"_key\":\"1c3077afa1f5\"},{\"style\":\"normal\",\"_key\":\"750223c4acfb\",\"markDefs\":[],\"children\":[{\"text\":\"As frontier AI models advance, they have the potential to bring about transformative benefits for our society and economy. AI could accelerate scientific discoveries, revolutionize healthcare, enhance our education system, and create entirely new domains for human creativity and innovation. However, frontier AI systems also present new challenges and risks that warrant careful study and effective safeguards.\",\"_key\":\"a1dcb0091c340\",\"_type\":\"span\",\"marks\":[]}],\"_type\":\"block\"},{\"children\":[{\"marks\":[],\"text\":\"In September 2023, we \",\"_key\":\"b75aa93fa2c80\",\"_type\":\"span\"},{\"_key\":\"b75aa93fa2c81\",\"_type\":\"span\",\"marks\":[\"4791430b5ec2\"],\"text\":\"released\"},{\"text\":\" our Responsible Scaling Policy, a framework for managing risks from increasingly capable AI systems. After a year of implementation and learning, we are now sharing a significantly updated version that reflects practical insights and accounts for advancing technological capabilities.\",\"_key\":\"b75aa93fa2c82\",\"_type\":\"span\",\"marks\":[]}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"c8b393e2960c\",\"markDefs\":[{\"_type\":\"link\",\"href\":\"https://www.anthropic.com/news/anthropics-responsible-scaling-policy\",\"_key\":\"4791430b5ec2\"}]},{\"_key\":\"a1948e4cc873\",\"markDefs\":[{\"_type\":\"link\",\"href\":\"https://www.anthropic.com/legal/aup\",\"_key\":\"ba04e1121891\"},{\"_key\":\"32798753cd99\",\"_type\":\"link\",\"href\":\"https://www.anthropic.com/research#societal-impacts\"}],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Although this policy focuses on catastrophic risks like the categories listed below, they are not the only risks that we monitor and prepare for. Our \",\"_key\":\"5d488fbe87cd0\"},{\"_type\":\"span\",\"marks\":[\"ba04e1121891\"],\"text\":\"Usage Policy\",\"_key\":\"5d488fbe87cd1\"},{\"_key\":\"5d488fbe87cd2\",\"_type\":\"span\",\"marks\":[],\"text\":\" sets forth our standards for the use of our products, including rules that prohibit using our models to spread misinformation, incite violence or hateful behavior, or engage in fraudulent or abusive practices. We continually refine our technical measures for enforcing our trust and safety standards at scale. Further, we conduct research to understand the broader \"},{\"_type\":\"span\",\"marks\":[\"32798753cd99\"],\"text\":\"societal impacts\",\"_key\":\"5d488fbe87cd3\"},{\"text\":\" of our models. Our Responsible Scaling Policy complements our work in these areas, contributing to our understanding of current and potential risks.\",\"_key\":\"5d488fbe87cd4\",\"_type\":\"span\",\"marks\":[]}],\"_type\":\"block\",\"style\":\"normal\"},{\"_key\":\"3b55c0bceff9\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"A framework for proportional safeguards\",\"_key\":\"5525570b2a010\"}],\"_type\":\"block\",\"style\":\"h3\"},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"ad1d58dbdd5b\",\"markDefs\":[{\"_type\":\"link\",\"href\":\"https://en.wikipedia.org/wiki/Biosafety_level\",\"_key\":\"f8e6f3368061\"}],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"As before, we maintain our core commitment: we will not train or deploy models unless we have implemented safety and security measures that keep risks below acceptable levels. Our RSP is based on the principle of proportional protection: safeguards that scale with potential risks. To do this, we use \",\"_key\":\"cdd8f6a2997b0\"},{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"AI Safety Level Standards (ASL Standards)\",\"_key\":\"3517fb380020\"},{\"_type\":\"span\",\"marks\":[],\"text\":\", graduated sets of safety and security measures that become more stringent as model capabilities increase. Inspired by \",\"_key\":\"aede15153eee\"},{\"_type\":\"span\",\"marks\":[\"f8e6f3368061\"],\"text\":\"Biosafety Levels,\",\"_key\":\"cdd8f6a2997b1\"},{\"_type\":\"span\",\"marks\":[],\"text\":\" these begin at ASL-1 for models that have very basic capabilities (for example, chess-playing bots) and progress through ASL-2, ASL-3, and so on.\",\"_key\":\"cdd8f6a2997b2\"}]},{\"_key\":\"18e2a21a8d24\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"In our updated policy, we have refined our methodology for assessing specific capabilities (and their associated risks) and implementing proportional safety and security measures. Our updated framework has two key components:\",\"_key\":\"94961b6308040\"}],\"_type\":\"block\",\"style\":\"normal\"},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"110d48e0214c\",\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Capability Thresholds:\",\"_key\":\"4fa1b51cfc900\"},{\"marks\":[],\"text\":\" Specific AI abilities that, if reached, would require stronger safeguards than our current baseline.\",\"_key\":\"f90398ece8b9\",\"_type\":\"span\"}],\"level\":1},{\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"132d0001c926\",\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"marks\":[\"strong\"],\"text\":\"Required Safeguards: \",\"_key\":\"d0053cd57ea60\",\"_type\":\"span\"},{\"text\":\"The specific ASL Standards needed to mitigate risks once a Capability Threshold has been reached.\",\"_key\":\"663a30c5e5b1\",\"_type\":\"span\",\"marks\":[]}]},{\"style\":\"normal\",\"_key\":\"36479c291519\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"At present, all of our models operate under ASL-2 Standards, which reflect current industry best practices. Our updated policy defines two key Capability Thresholds that would require upgraded safeguards:\",\"_key\":\"a03bfcd2c90a0\"}],\"_type\":\"block\"},{\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Autonomous AI Research and Development:\",\"_key\":\"113a03232f950\"},{\"_type\":\"span\",\"marks\":[],\"text\":\" If a model can independently conduct complex AI research tasks typically requiring human expertise鈥攑otentially significantly accelerating AI development in an unpredictable way鈥攚e require elevated security standards (potentially ASL-4 or higher standards) and additional safety assurances to avoid a situation where development outpaces our ability to address emerging risks.\",\"_key\":\"559bae44e291\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"7960a2e610b8\",\"listItem\":\"bullet\",\"markDefs\":[]},{\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Chemical, Biological, Radiological, and Nuclear (CBRN) weapons:\",\"_key\":\"04f8008234340\"},{\"_type\":\"span\",\"marks\":[],\"text\":\" If a model can meaningfully assist someone with a basic technical background in creating or deploying CBRN weapons, we require enhanced security and deployment safeguards (ASL-3 standards).\",\"_key\":\"e87c145d832e\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"b1e7e0200f28\",\"listItem\":\"bullet\"},{\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"ASL-3 safeguards involve enhanced security measures and deployment controls. On the security side, this will include internal access controls and more robust protection of model weights. For deployment risks, we plan to implement a multi-layered approach to prevent misuse, including real-time and asynchronous monitoring, rapid response protocols, and thorough pre-deployment red teaming.\",\"_key\":\"c21521b5c8660\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"5028d8139378\"},{\"_type\":\"block\",\"style\":\"h3\",\"_key\":\"9d149ea1669f\",\"markDefs\":[],\"children\":[{\"_key\":\"c9476c97d6320\",\"_type\":\"span\",\"marks\":[],\"text\":\"Implementation and oversight\"}]},{\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"To contribute to effective implementation of the policy, we have established:\",\"_key\":\"0898891e3fd00\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"081edde580ae\",\"markDefs\":[]},{\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"1ef822ef2fa2\",\"listItem\":\"bullet\",\"markDefs\":[{\"_type\":\"link\",\"href\":\"http://www.anthropic.com/rsp-updates\",\"_key\":\"e97965203d94\"}],\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Capability assessments\",\"_key\":\"65ad635ebc020\"},{\"_type\":\"span\",\"marks\":[],\"text\":\": Routine model evaluations based on our Capability Thresholds to determine whether our current safeguards are still appropriate. (Summaries of past assessments are available \",\"_key\":\"ee8bb23f5481\"},{\"_type\":\"span\",\"marks\":[\"e97965203d94\"],\"text\":\"here\",\"_key\":\"65ad635ebc021\"},{\"text\":\".)\",\"_key\":\"65ad635ebc022\",\"_type\":\"span\",\"marks\":[]}]},{\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"b5fc19b935bb\",\"listItem\":\"bullet\",\"markDefs\":[{\"_type\":\"link\",\"href\":\"http://www.anthropic.com/rsp-updates\",\"_key\":\"d6b74c71ad6d\"}],\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Safeguard assessments: \",\"_key\":\"fd96692cf5950\"},{\"_type\":\"span\",\"marks\":[],\"text\":\"Routine evaluation of the effectiveness of our security and deployment safety measures to assess whether we have met the Required Safeguards bar. (Summaries of these decisions will be available \",\"_key\":\"bbdf4a0ded13\"},{\"_key\":\"fd96692cf5951\",\"_type\":\"span\",\"marks\":[\"d6b74c71ad6d\"],\"text\":\"here\"},{\"_key\":\"fd96692cf5952\",\"_type\":\"span\",\"marks\":[],\"text\":\".)\"}]},{\"markDefs\":[{\"_type\":\"link\",\"href\":\"https://arxiv.org/abs/2403.10462\",\"_key\":\"dd97b493d101\"}],\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Documentation and decision-making: \",\"_key\":\"ed8d6ec6368a0\"},{\"_type\":\"span\",\"marks\":[],\"text\":\"Processes for documenting the capability and safeguard assessments, inspired by procedures (such as \",\"_key\":\"d451a32ef6cb\"},{\"_key\":\"ed8d6ec6368a1\",\"_type\":\"span\",\"marks\":[\"dd97b493d101\"],\"text\":\"safety case methodologies\"},{\"_type\":\"span\",\"marks\":[],\"text\":\") common in high-reliability industries.\",\"_key\":\"ed8d6ec6368a2\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"8074ca752c13\",\"listItem\":\"bullet\"},{\"_key\":\"60c9b7dabd40\",\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"text\":\"Measures for internal governance and external input: \",\"_key\":\"ca95d473f4a90\",\"_type\":\"span\",\"marks\":[\"strong\"]},{\"_type\":\"span\",\"marks\":[],\"text\":\"Our assessment methodology will be backed up by internal stress-testing in addition to our existing internal reporting process for safety issues. We are also soliciting external expert feedback on our methodologies.\",\"_key\":\"fb68635efd29\"},{\"_type\":\"span\",\"marks\":[\"sup\"],\"text\":\"1\",\"_key\":\"7ddd60e07d17\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\"},{\"style\":\"h3\",\"_key\":\"6e5effe0bac6\",\"markDefs\":[],\"children\":[{\"_key\":\"ecdac5e44ed40\",\"_type\":\"span\",\"marks\":[],\"text\":\"Learning from experience\"}],\"_type\":\"block\"},{\"markDefs\":[],\"children\":[{\"text\":\"We have learned a lot in our first year with the previous RSP in effect, and are using this update as an opportunity to reflect on what has worked well and what makes sense to update in the policy. As part of this, we conducted our first review of how well we adhered to the framework and identified a small number of instances where we fell short of meeting the full letter of its requirements. These included procedural issues such as completing a set of evaluations three days later than scheduled or a lack of clarity on how and where we should note any changes to our placeholder evaluations. We also flagged some evaluations where we may have been able to elicit slightly better model performance through implementing standard techniques (such as chain-of-thought or best-of-N).\",\"_key\":\"8932eeee77830\",\"_type\":\"span\",\"marks\":[]}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"142037ff4afb\"},{\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"In all cases, we found these instances posed minimal risk to the safety of our models. We used the additional three days to refine and improve our evaluations; the different set of evaluations we used provided a more accurate assessment than the placeholder evaluations; and our evaluation methodology still showed we were sufficiently far from the thresholds. From this, we learned two valuable lessons to incorporate into our updated framework: we needed to incorporate more flexibility into our policies, and we needed to improve our process for tracking compliance with the RSP. You can read more \",\"_key\":\"2c9de8a77cf10\"},{\"_type\":\"span\",\"marks\":[\"724c1bcc37c6\"],\"text\":\"here\",\"_key\":\"2c9de8a77cf11\"},{\"_type\":\"span\",\"marks\":[],\"text\":\".\",\"_key\":\"2c9de8a77cf12\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"fdd39c742e14\",\"markDefs\":[{\"_type\":\"link\",\"href\":\"http://anthropic.com/rsp-updates\",\"_key\":\"724c1bcc37c6\"}]},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"c84e70ee115b\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Since we first released the RSP a year ago, our goal has been to offer an example of a framework that others might draw inspiration from when crafting their own AI risk governance policies. We hope that proactively sharing our experiences implementing our own policy will help other companies in implementing their own risk management frameworks and contribute to the establishment of best practices across the AI ecosystem.\",\"_key\":\"855d60a456920\"}]},{\"children\":[{\"_key\":\"81a081f760a10\",\"_type\":\"span\",\"marks\":[],\"text\":\"Looking ahead\"}],\"_type\":\"block\",\"style\":\"h3\",\"_key\":\"20a9139b3a51\",\"markDefs\":[]},{\"style\":\"normal\",\"_key\":\"78baf5750bd0\",\"markDefs\":[],\"children\":[{\"_key\":\"252e5f0060c50\",\"_type\":\"span\",\"marks\":[],\"text\":\"The frontier of AI is advancing rapidly, making it challenging to anticipate what safety measures will be appropriate for future systems. All aspects of our safety program will continue to evolve: our policies, evaluation methodology, safeguards, and our research into potential risks and mitigations.\"}],\"_type\":\"block\"},{\"style\":\"normal\",\"_key\":\"2afadf824368\",\"markDefs\":[],\"children\":[{\"text\":\"Additionally, Co-Founder and Chief Science Officer Jared Kaplan will serve as Anthropic鈥檚 Responsible Scaling Officer, succeeding Co-Founder and Chief Technology Officer Sam McCandlish who held this role over the last year. Sam oversaw the RSP鈥檚 initial implementation and will continue to focus on his duties as Chief Technology Officer. As we work to scale up our efforts on implementing the RSP, we鈥檙e also opening a position for a Head of Responsible Scaling. This role will be responsible for coordinating the many teams needed to iterate on and successfully comply with the RSP.\",\"_key\":\"744b6d45ddff0\",\"_type\":\"span\",\"marks\":[]}],\"_type\":\"block\"},{\"markDefs\":[{\"_type\":\"link\",\"href\":\"https://www.anthropic.com/jobs\",\"_key\":\"6210958b799c\"}],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"If you would like to contribute to AI risk management at Anthropic, \",\"_key\":\"d603f352b2580\"},{\"text\":\"we are hiring\",\"_key\":\"d0297560aab0\",\"_type\":\"span\",\"marks\":[\"6210958b799c\"]},{\"_type\":\"span\",\"marks\":[],\"text\":\"! Many of our teams now contribute to risk management via the RSP, including:\",\"_key\":\"ec2a1674bc0a\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"88e4ecab085f\"},{\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"marks\":[],\"text\":\"Frontier Red Team (responsible for threat modeling and capability assessments)\",\"_key\":\"0a4d9f27a0cd0\",\"_type\":\"span\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"803749c5db22\"},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"fe760029da3a\",\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Trust \u0026 Safety (responsible for developing deployment safeguards)\",\"_key\":\"dc5557c893ca0\"}],\"level\":1},{\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Security and Compliance (responsible for security safeguards and risk 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