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Wednesday, 12 November 2025

Differentially private machine learning at scale with JAX-Privacy

Google Research 11 months ago 46

Google released JAX-Privacy 1.0, a toolkit built on the JAX numerical computing library that enables researchers and developers to implement differentially private machine learning algorithms at scale. The library provides core components for differential privacy including per-example gradient clipping, noise addition, and auditing tools, with support for training large language models like VaultGemma through JAX's native parallelism features. The open-source release aims to lower barriers for building privacy-preserving AI applications by integrating differential privacy into modern machine learning workflows.

Neuro drives national retail wins with ChatGPT Business

OpenAI 11 months ago 55

Neuro, a retail company, uses ChatGPT Business to operate across the U.S. with under 70 staff members by automating tasks in sales and operations. The company achieves cost reduction and faster execution by leveraging the AI tool instead of hiring additional employees. This efficiency enables Neuro to scale its national presence while maintaining a lean workforce structure.

Giving your AI a Job Interview

One Useful Thing 11 months ago 48

Standard AI benchmarks like MMLU and AIME have significant flaws including public answer keys that models memorize, unclear measurement validity, and calibration issues, yet all major benchmarks trend upward suggesting they capture some real underlying improvement in AI capabilities. OpenAI's GDPval study tested models on realistic four-to-seven-hour expert tasks with blind evaluation by subject matter experts, revealing that performance varies substantially across domains—with AI excelling at software development and financial advising but underperforming at pharmacy and real estate. Organizations deploying AI at scale should conduct rigorous task-specific benchmarking rather than relying on general benchmarks alone, testing models on actual business scenarios multiple times to understand their strengths, weaknesses, and decision-making patterns across thousands of real decisions.

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