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    News History of X/AnthropicAI

    41.
    R to @AnthropicAI: Working closely with AWS, we're developing future generations of Trainium chips. Designing both hardware and software together lets us optimize every aspect of frontier model training.

    Source: https://nitter.privacydev.net/AnthropicAI/rss

    2024-11-23 03:10:28 AM
    42.
    R to @AnthropicAI: Our engineers work with the AWS chip design team to extract maximum computational efficiency from the hardware. This includes writing low-level kernels that allow us to directly interface with Trainium silicon and contributing to the AWS Neuron software stack.

    Source: https://nitter.privacydev.net/AnthropicAI/rss

    2024-11-23 03:10:28 AM
    43.
    R to @AnthropicAI: Through Amazon Bedrock, Claude has become core infrastructure for tens of thousands of companies seeking reliable and practical AI at scale. Together, we're laying a new technological foundation—from silicon to software—to train and power our most advanced AI models.

    Source: https://nitter.privacydev.net/AnthropicAI/rss

    2024-11-23 03:10:28 AM
    44.
    We’ve added a new prompt improver to the Anthropic Console. Take an existing prompt and Claude will automatically refine it with prompt engineering techniques like chain-of-thought reasoning.

    Source: https://nitter.privacydev.net/AnthropicAI/rss

    2024-11-22 03:01:05 AM
    45.
    R to @AnthropicAI: Rolling out now to all Claude Pro, Team, and Enterprise users.

    Source: https://nitter.privacydev.net/AnthropicAI/rss

    2024-11-22 03:01:05 AM
    46.
    Claude 3.5 Haiku is now available on our API, Amazon Bedrock, and Google Cloud's Vertex AI. Haiku is fast and particularly strong at coding. It outperforms state-of-the-art models—including GPT-4o—on SWE-bench Verified, which measures how models solve real software issues.

    Source: https://nitter.privacydev.net/AnthropicAI/rss

    2024-11-21 03:00:49 AM
    47.
    New research: Jailbreak Rapid Response. Ensuring perfect jailbreak robustness is hard. We propose an alternative: adaptive techniques that rapidly block new classes of jailbreak as they’re detected. Read our paper with >@MATSprogram: https://arxiv.org/abs/2411.07494

    Source: https://nitter.privacydev.net/AnthropicAI/rss

    2024-11-21 03:00:49 AM
    48.
    R to @AnthropicAI: In the paper, we develop a benchmark for these defenses. From observing just one example of a jailbreak class, our best defense—fine-tuning an input classifier—reduces jailbreak success rate by 240× on previously detected attacks, and 15× on diverse variants of those attacks.

    Source: https://nitter.privacydev.net/AnthropicAI/rss

    2024-11-21 03:00:49 AM
    49.
    R to @AnthropicAI: The key is producing new jailbreak-like text from a known jailbreak. We “proliferate” jailbreaks by using an LLM to generate more jailbreak examples. The best defense scales dramatically with better proliferation.

    Source: https://nitter.privacydev.net/AnthropicAI/rss

    2024-11-21 03:00:49 AM
    50.
    R to @AnthropicAI: Our work suggests that jailbreak rapid response, along with other enhancements in jailbreak robustness, offers a promising pathway for making real-world LLMs safer. We expect further gains with improved jailbreak proliferation techniques.

    Source: https://nitter.privacydev.net/AnthropicAI/rss

    2024-11-21 03:00:49 AM
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