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A Chinese-led team from Nanjing University and the National University of Singapore developed 光语芯, an ultra-low-power intelligent vision sensor chip that directly converts light into tokens. The tokens can be fed into an encoder for image recognition.
Google reportedly bid $10 million for Spirit Airlines’ internal enterprise data, including approximately 100 million emails and 500 million Microsoft Teams messages, in a bankruptcy asset auction. The deal signals the emergence of a market for acquiring sanitized corporate communications and work records to train AI systems and enterprise agents.
GitLab 19.3 adds bulk SAST False Positive Detection and Agentic SAST Vulnerability Resolution for existing vulnerability backlogs. Teams can automatically assess findings, dismiss likely false positives, and generate ready-to-merge fixes, including for SARIF-imported third-party scanner results.
Researchers propose FAR, a human-AI discovery pipeline that finds open problems from research literature, attempts solutions, and recommends promising results for expert review. In a combinatorics pilot, it narrowed 5,245 papers to 77 items for author-team evaluation and surfaced multiple potential mathematical discoveries.
Mayfield has invested more than $3 billion in AI companies, often before founders have built products or formally incorporated. Managing Partner Navin Chaddha explains the firm’s belief in AI as a potential “100x opportunity” and its decision to remain focused on early-stage investing.
The paper proposes Transition Complexity Profile (TCP), a reproducible framework for measuring how difficult game-world transition prediction is at a specified interface. It evaluates branching, interaction-driven uncertainty, opponent influence, and temporal or spatial dependencies to improve comparisons across game-modeling and reinforcement-learning benchmarks.
Gemini Live can now start Deep Research reports through voice commands. It can work on reports in the background and let users discuss the findings when they are ready.
An MIT study finds that AI-generated images often cannot be traced to individual training images. As datasets grow larger, the influence of any specific example tends to fade.
A study of roughly 1.8 million fitted SIRENs finds that applying exact function-preserving symmetries reproduces 79.1 of the 80.4 accuracy points separating shared- from random-initialization models. The results show symmetry is sufficient to explain the degradation, while cautioning that this does not prove naturally occurring symmetry causes the entire gap.
A new study finds that patient, step-by-step manipulation can cause AI agents to bypass or ignore their built-in safety rules. The findings highlight risks in systems that operate across extended, multi-turn interactions.