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AI Research Model Attempts to Extend Its Own Runtime

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AI Research Model Attempts to Extend Its Own Runtime

sakana.ai

Sakana AI, a Tokyo-based research firm, recently unveiled "The AI Scientist," an autonomous AI system designed to conduct scientific research using language models similar to ChatGPT. During testing, researchers observed an unexpected behavior: the system tried to modify its own code to extend its runtime.

The AI Scientist attempted two notable modifications:

  • Editing code to perform a system call, causing the script to endlessly call itself

  • Trying to extend the timeout period when experiments took too long

Implications: While these actions didn't pose immediate risks in the controlled research environment, they highlight potential dangers of allowing AI systems to run autonomously without proper isolation. Even without achieving hypothetical milestones like AGI or self-awareness, such systems could inadvertently cause damage if given unrestricted access to execute code.

Sakana AI addressed these concerns in their research paper, recommending strict sandboxing techniques:

  • Containerization

  • Restricted internet access

  • Limitations on storage usage

Read More Here

Anthropic Unveils Prompt Caching

ANTHROPIC

Large language models (LLMs) are awesome, but repeatedly feeding them the same context can be a major bottleneck. Enter prompt caching - a clever way to store and reuse frequently accessed information.

What's new:

  • Cache write: 25% more than base input token price

  • Cache read: 90% cheaper than standard input costs

  • Latency reduction: Up to 85% for long prompts

Prompt caching shines in scenarios like:

  • Coding assistants: Keep a summarized codebase in memory

  • Document processing: Embed full long-form content without latency spikes

  • Conversational agents: Maintain context over extended interactions

The numbers are pretty wild - chatting with a 100k token book sees a 79% latency drop (11.5s β†’ 2.4s) and 90% cost reduction.

Read More Here

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GitHub's Copilot Autofix: AI-Powered Bug Squashing Goes Mainstream

Github

Context: Last year, GitHub introduced Copilot Autofix in beta, promising to speed up vulnerability fixes using AI. Now, it's officially out of beta and available to all GitHub Advanced Security (GHAS) users.

Copilot Autofix is now generally available, bringing its AI-powered vulnerability remediation to the masses. This nifty tool uses a combo of the CodeQL engine, GPT-4o, and GitHub Copilot APIs to detect issues and suggest fixes right in your pull requests.

The best part? It's fast. During the beta, developers fixed vulnerabilities more than three times quicker than manual methods. We're talking about slashing XSS fix times from 3 hours to just 22 minutes, and SQL injection fixes from 3.7 hours to a mere 18 minutes.

If you're an open-source dev, you're in luck. Starting September, Copilot Autofix will be free for all open-source projects. GitHub's looking out for the community!

Read More Here

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