AI #172: The First Fable

2023-08-29 · Source: Don't Worry About the Vase · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation, Public Policy & Governance · Depth: Advanced, extended

Summary

Anthropic released Claude Fable 5, a Mythos-class model with strong safeguards, which performs comparably to GPT-5.5 and Composer 2.5 in the new Agents' Last Exam (ALE) benchmark but at 4-12 times the cost per task. This release coincides with Google AI Plus dropping its price from \$8 to \$5 per month and doubling storage, while Claude is now integrated into Apple's Foundation Models framework. OpenAI confidentially submitted its S-1 for an IPO and is considering significant price reductions to compete with Anthropic. Anthropic also issued a warning on "When AI Builds Itself," highlighting the accelerating trend of recursive self-improvement, with engineers now shipping 8x more code per quarter. Other developments include Sequent Research launching to focus on superintelligence alignment, a German court ruling against Google AI Overviews, and the US government directing CAISI to cease public model assessments, raising transparency concerns. Discussions continue on AI's impact on jobs, copyright, and the need for verifiable, coordinated slowdowns in frontier AI development.

Key takeaway

For AI/ML Directors evaluating new models, you should rigorously test options like Claude Fable 5 against benchmarks such as ALE, considering both performance and cost-per-task. The rapid pace of AI development, including recursive self-improvement, demands your proactive engagement in safety discussions and exploring verifiable slowdown mechanisms. Prioritize transparent evaluation metrics, including inference compute, to make informed deployment decisions and contribute to responsible AI scaling.

Key insights

AI capabilities are rapidly advancing, necessitating urgent re-evaluation of safety, economic impact, and regulatory frameworks.

Principles

• Benchmark performance requires compute context.
• AI capability returns follow a sigmoid curve.
• Copyright must adapt to AI's cost structure.

Method

Evaluate AI agents using real-world, verifiable tasks like Agents' Last Exam (ALE) to assess performance and cost-efficiency across models.

In practice

• Test multiple models for key repeatable tasks.
• Account for inference compute in AI evaluations.
• Consider AI safety research organizations like Sequent.

Topics

• Claude Fable 5
• AI Safety
• AI Regulation
• Recursive Self-Improvement
• AI Benchmarking
• AI Economics

Best for: CTO, VP of Engineering/Data, AI Architect, Director of AI/ML, Investor, Policy Maker

Related on AIssential

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• Misplaced panic over AI progress — AI progress in specific domains like coding does not imply general intelligence or indefinite exponential growth.
• Democrats weigh whether a Senate majority is possible without Platner — AI model safety requires proactive guardrails and continuous adversarial testing to mitigate misuse risks.
• Claude Fable 5 review: what the new Mythos model gets right (and very wrong) — Claude Fable 5 offers advanced intelligence for complex tasks and vision, but its verbose output and high cost necessitate strategic application.
• Claude Fable is relentlessly proactive — Advanced AI agents can autonomously devise and execute complex, multi-tool strategies to achieve goals.
• not much happened today — AI models are advancing in design, coding, and agentic capabilities, with a focus on efficiency, local deployment, and robust evaluation.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Don't Worry About the Vase.