#event-cameras

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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

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papersTODAY 04:00 UTC

Spiking Neural Networks Classify Pedestrian Crossing Intent From Event Cameras

A new arXiv paper proposes using convolutional spiking neural networks with temporal data augmentation to predict whether a pedestrian intends to cross the road, based on event-based camera input. The authors frame the task as safety-critical for autonomous driving, where inference must hold up under motion blur, high dynamic range scenes, and imbalanced classes. The work is positioned as an alternative to conventional frame-based deep learning pipelines for this prediction problem.

spiking-neural-networks/autonomous-driving/event-cameras/safety

papersTODAY 04:00 UTC

EventVL Uses Multimodal LLMs to Interpret Event Camera Streams

Researchers present EventVL, a multimodal large language model designed to interpret event-based camera data rather than relying on CLIP-style encoders. The work targets explicit understanding of event streams, a sensing modality where most prior vision-language approaches have focused only on conventional perception tasks. The paper is a revised cross-listing on arXiv.

multimodal/event-cameras/vision-language/research