jasondclinton

We did exactly that and published it last month right here: https://www.anthropic.com/research/emotion-concepts-function

If you use context cacheing, it saves quite a lot on the costs/budgets. You can cache 900k tokens if you want.

This is false.

Thanks for the report! We're addressing it urgently.

Starlink has been deployed on JSX for almost a year now and I've taken quite a few flights on their Bay Area to LA and Vegas routes. Despite 20 people on the planes, no one has ever been on a video conference, though I could see it becoming an issue with a broader consumer base.

Hi, CISO at Anthropic here. Sorry that we didn't respond to your BAA request. I am accountable for our response to BAA requests and I'd like to dig into what happened here. If you are comfortable, would you please reach out to me at j@anthropic.com to let me know how you sent your request in?

Got it, thanks for the feedback!

It's available now! Sorry for the delay.

Have you tried our prompt generator? https://docs.anthropic.com/en/docs/build-with-claude/prompt-... . We've seen it improve performance.

It's live now! Sorry for the delay.

That’s what the Long Term Benefit Trust solves: https://www.anthropic.com/news/the-long-term-benefit-trust No one on that board is financially interested in Anthropic.

Hi, Anthropic is a 3 year old company that, until the release of GPT-4o last week from a company that is almost 10 years old, had the most capable model in the world, Opus, for a period of two months. With regard to availability, we had a huge amount of inbound interest on our 1P API but our model was consistently available on Amazon Bedrock throughout the last year. The 1P API has been available for the last few months to all.

No open weights model is currently within the performance class of the frontier models: GPT-4*, Opus, and Gemini Pro 1.5, though it’s possible that could change.

We are structured as a public benefit corporation formed to ensure that the benefits of AI are shared by everyone; safety is our mission and we have a board structure that puts the Response Scaling Policy and our policy mission at the fore. We have consistently communicated publicly about safety since our inception.

We have shared all of our safety research openly and consistently. Dictionary learning, in particular, is a cornerstone of this sharing.

The ASL-3 benchmark discussed in the blog post is about upcoming harms including bioweapons and cybersecurity offensive capabilities. We agree that information on web searches is not a harm increased by LLMs and state that explicitly in the RSP.

I’d encourage you to read the blog post and the RSP.

This is called a “compute multiplier” and, yes, we have a protocol for that. All AI labs do, as far as I am aware; standard industry practice.

Our consistent position has been that testing and evaluations would best govern actual risks. No measured risk: no restrictions. The White House Executive Order put the models of concern at those which have 10^26 FLOPs of training compute. There are no open weights models at this threshold to consider. We support open weights models as we've outlined here: https://www.anthropic.com/news/third-party-testing . We also talk specifically about how to avoid regulatory capture and to have open, third-party evaluators. One thing that we've been advocating for, in particular, is the National Research Cloud and the US has one such effort in National AI Research Resource that needs more investment and fair, open accessibility so that all of society has inputs into the discussion.

You're the first person who I've run into who heard the podcast, thank you for listening! Glad that it was informative.

Hi, I'm the CISO from Anthropic. Thank you for the criticism, any feedback is a gift.

We have laid out in our RSP what we consider the next milestone of significant harms that we're are testing for (what we call ASL-3): https://anthropic.com/responsible-scaling-policy (PDF); this includes bioweapons assessment and cybersecurity.

As someone thinking night and day about security, I think the next major area of concern is going to be offensive (and defensive!) exploitation. It seems to me that within 6-18 months, LLMs will be able to iteratively walk through most open source code and identify vulnerabilities. It will be computationally expensive, though: that level of reasoning requires a large amount of scratch space and attention heads. But it seems very likely, based on everything that I'm seeing. Maybe 85% odds.

There's already the first sparks of this happening published publicly here: https://security.googleblog.com/2023/08/ai-powered-fuzzing-b... just using traditional LLM-augmented fuzzers. (They've since published an update on this work in December.) I know of a few other groups doing significant amounts of investment in this specific area, to try to run faster on the defensive side than any malign nation state might be.

Please check out the RSP, we are very explicit about what harms we consider ASL-3. Drug making and "stuff on the internet" is not at all in our threat model. ASL-3 seems somewhat likely within the next 6-9 months. Maybe 50% odds, by my guess.

None of the "washlet" toilet attachments need a hot water line. They have built-in heaters and heat the water coming in from the cold water line.

Whole-house battery backup is a modern marvel, too. (We have 80KWH of stored battery capacity and it shifts grid load to non-peak hours to save huge amounts of money.)

please contact support: https://support.anthropic.com/en/ . we'll get it fixed. sorry!

Thanks for the vote of confidence. I led the Chrome Infrastructure Security Team hardening for insider risk and generally defending against APTs for the last 3 years at Google. Before that, I was on the Payments Security Team defending PII and SPII data up and down the stack. Indeed, I and the company take this very seriously. We're racing as fast as we can to defend against the run-of-the-mill opportunistic attackers but also APTs. We've ramped the securtiy team over the last year from 4 to 35 people. I'm still hiring, though!

Amanda is one the most talented researchers at Anthropic. Truly an honor to work with her.

Thank you! Might also be seeing performance improved by by our system prompt on claude.ai.

Seems stochastic? This is what I see from Opus which is correct: https://claude.ai/share/f5dcbf13-237f-4110-bb39-bccb8d396c2b

Did you perhaps run this on Sonnet?

We are tracking LMSys, too. There are strange safety incentives on this benchmark: you can “win” points by never blocking adult content for example.

LLMs are building blocks and I’m excited about folks building with a concert of models working together with subagents.

Thank you!

Hi, CISO of Anthropic here. Thank you for the feedback! If you can share any details about the image, please share in a private message.

No LLM has had an emergent calculator yet.

This is such a bad headline. The story is mostly about an author posting fraudulent reviews to promote her own work.

Howdy! Please give 2.1 a try and let me know what you think. You can see the benchmark data in the appendix of our updated 2.1 model card here: https://www-files.anthropic.com/production/images/ModelCardC...

Heya, as with all language models, if you open the conversation with antagonistic questions, the rest of the conversation thread becomes tainted. If you ask most of your questions in a new thread, almost everything you ask here will be answered. See our model card for more prompting guidance.

Howdy, CISO of Anthropic here. I'm not sure what happened in your case but please reach out to support@ and mention my name; we'll respond ASAP.