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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.
Wired observed a robotic arm at Generalist AI learning and improvising during a live demonstration. The robot reportedly used a banana as a tool, highlighting progress toward more adaptable embodied AI systems.
The article examines a new phase of AI for Science in which robots are becoming foundational infrastructure for research. It highlights the growing role of robotic systems in enabling and automating scientific work.
Tactile robotics is expanding from sensor hardware into datasets, foundation models, and evaluation benchmarks. Research shows touch improves physical reasoning and robot success, but inconsistent sensor formats create major cross-device transfer challenges.
Serval has made Catalyst generally available as an administrative super agent that discovers repetitive work and builds governed enterprise automations. It can also create background agents that monitor connected systems, identify emerging issues, and suggest fixes before employees submit tickets.
CISA warns that hackers are actively targeting Siemens equipment used in critical U.S. water infrastructure. Attackers are reportedly using artificial intelligence to identify vulnerabilities as water systems face increasing cyberattacks.
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.
Cerebras launched the CS-4, its first system to combine three wafer-scale processors in a single rack. The inference-focused machine is shipping this quarter, with Cerebras claiming up to 30 times the speed of GPU-based systems for frontier models.
AWS outlines enterprise patterns for operating many agentic AI systems across diverse frameworks, models, and providers. The guidance focuses on preserving flexibility and enabling multi-agent systems to scale together without vendor lock-in.
Generative recommenders are shifting recommendation objectives from embedding similarity toward predicting a user's next action or item. The approach uses user-history sequences and large catalogs to rethink how recommendation systems are trained and served at scale.