Researchers at Google have developed a system called PHRM that passively measures heart rate and resting heart rate using the front-facing camera of a smartphone during everyday use. In a study published in Nature, the system achieved an accuracy of less than 10% mean absolute percentage error compared to ECG, and less than 5 bpm error for daily resting heart rate compared to a wearable. The system was tested on a diverse dataset of over 350,000 video clips from nearly 700 participants, ensuring balanced representation across skin tones. PHRM outperformed 15 leading remote photoplethysmography models and is the only model to meet accuracy standards for all skin tones in real-world conditions.

NVIDIA releases Nemotron 3.5 Content Safety, a unified model combining multimodal input, multilingual coverage, custom enterprise policy enforcement, and auditable reasoning for content safety. Built on Google Gemma 3 4B IT and fine-tuned with LoRA, it supports explicit training in 12 languages with zero-shot generalization to ~140 languages. New features include custom policy enforcement via natural language specifications and a THINK mode for auditable step-by-step reasoning. The model achieves ~85% average accuracy across multiple multilingual and multimodal safety benchmarks while maintaining a compact 4B-parameter size and low latency. NVIDIA also releases a safety dataset with multimodal, multilingual safety reasoning traces.

Telecommunications operators face mounting pressure on customer experience, network operations, and cybersecurity. The Data + AI Summit 2026 (June 15-18, San Francisco) features a Telecommunications Industry Experience with forums, breakouts, and demos on AI-driven churn prevention, autonomous network operations, and fraud detection.

LangGraph provides built-in primitives for retries, timeouts, and error handling to build resilient AI agents. The post explains how to use RetryPolicy, TimeoutPolicy, and error_handler, and demonstrates the SAGA pattern for multi-step workflows with side effects.