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5.0 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.7 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.5 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src2.2 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.8 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.4 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.4 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.4 VoiceCodeBench arXiv paper proposes benchmark for exact structured-token recovery in speech recognition — 1 src1.4 Arabic-Russian Parallel Corpus and LLM Benchmark for Scientific Text — 1 src1.4 Study Analyzes Self-Reported Limitations in NLP Research — 1 src5.0 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.7 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.5 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src2.2 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.8 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.4 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.4 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.4 VoiceCodeBench arXiv paper proposes benchmark for exact structured-token recovery in speech recognition — 1 src1.4 Arabic-Russian Parallel Corpus and LLM Benchmark for Scientific Text — 1 src1.4 Study Analyzes Self-Reported Limitations in NLP Research — 1 src

#scalability

2 curated events

papersTODAY 04:00 UTC

arXiv paper proposes scalable data attribution via influence matrix estimation

A new arXiv preprint addresses the computational cost of data attribution, which measures how individual training samples affect a model's behavior. The authors frame the problem around estimating the influence matrix at scale, with applications in data valuation, machine unlearning, and interpretability. The abstract highlights that scaling such methods has remained a longstanding obstacle.

data-attribution/research/interpretability/scalability

papersSEP 11 04:00 UTC

Sublinear Variational Optimization Scales Gaussian Mixture Models to Billions of Parameters

A revised arXiv preprint introduces a variational optimization method for training Gaussian mixture models that keeps computational cost below the usual scaling limits. The approach aims to make large, general GMMs trainable even when datasets contain many high-dimensional data points. This addresses a long-standing bottleneck that has made such models impractical at scale.

gaussian-mixture-models/optimization/scalability/arxiv