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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 FDA has approved Aletta, a robot that can locate a patient's vein, insert a needle, and draw blood without direct human assistance. The system could help address shortages of trained phlebotomy staff.
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.
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 Association for Advancing Automation says non-automotive industries accounted for 54.8% of North American robot orders in the first half of 2026. The shift is being driven by applications in semiconductors, life sciences, pharmaceuticals, food, and consumer products, while AI, collaborative robots, and mobile robots expand the market.
MORPHI made its first systematic domestic appearance at WRC, showcasing its newly released embodied-intelligence model architecture, MoRA. The demonstration focused on how a robot’s embodied brain supports practical long-horizon tasks.
51World has unveiled a suite of data-collection devices and platforms designed to generate training data for embodied AI systems. The Beijing-based company argues that higher-quality physical-world data is essential for improving intelligent humanoid robots.
AI for Science is moving from computational assistance to physical experimentation as robots enter national-level laboratories. 源络科技 is presented as a company helping bring AI for Science into a more automated laboratory 3.0 stage.
Part 1 shows how to configure an AWS account and Snowflake environment for a no-code machine learning workflow. The setup prepares healthcare, retail, and life sciences teams to build a fraud detection model with Amazon SageMaker Canvas.