10 results on this page
AWS demonstrates how Amazon Bedrock can add context-aware security monitoring to healthcare FHIR APIs. The approach detects anomalous access patterns, classifies data sensitivity, and generates natural-language compliance reports without adding latency to clinical workflows.
The US Trade Representative’s reference to “digital trade alignment” in talks with Prime Minister Mark Carney’s government is raising concerns in Canada. Critics worry the agreement could limit Canada’s ability to independently regulate technology companies and AI.
Chinese AI companies are optimising software to handle growing inference demand while access to high-end Nvidia processors remains limited. Domestic chips can support some inference workloads, but complex coding tasks still depend partly on scarce Nvidia capacity.
Ant Group’s quarterly profit barely grew after the company increased spending on artificial intelligence. The investments target healthcare, large language models, and payment services, while expansion costs also pressured results.
Humanoid robot traffic officers in China can direct vehicles, reprimand jaywalkers, and guide tourists to nearby metro stations. In Hangzhou, at least 15 robots have been deployed, but they are deliberately unable to physically stop people or vehicles.
A position paper analyzing 500 Hugging Face model cards argues that model cards alone do not provide enough information for governing open-weight foundation models. It proposes combining model cards with acceptable use policies and licenses to address safety, provenance, behavior, and enforcement gaps.
This systematic review examines how large language models support mental-health applications, including social-media analysis, clinical conversational agents, therapy support, and psychoeducation. It also covers multimodal diagnosis, prompt engineering, interpretability, and the ethical and regulatory safeguards needed for responsible deployment.
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.
Apple Machine Learning presents a lexical-intervention approach for improving cross-lingual knowledge transfer when target-language data is scarce. The work targets downstream capabilities such as scientific reasoning, commonsense inference, and world knowledge without relying heavily on parallel data, translation systems, or auxiliary models.
Apple Machine Learning studies how to mix scarce target-domain data with abundant generic data during language-model pretraining. Across more than 2,000 training runs, the research examines the trade-off between insufficient target exposure, repeated examples, diminishing returns, and overfitting.