Researcher Develops Method to Train Robots on Uncertain Tasks

AI is accelerating drug discovery by enabling predictive design of candidates and hit identification at scale, but the technology is exposing critical gaps in data quality and lab infrastructure. Drug companies are hitting a 'data wall' where publicly available datasets lack the structure and diversity needed to train accurate models, while lab teams struggle to validate the growing volume of AI-generated compounds. Success depends on closing the loop between computational prediction and experimental validation through better data collection and integration.

Frontier physical AI models are moving beyond video training data to incorporate multiple camera angles, dense annotation, and brain wave readings as training inputs. The shift reflects growing recognition that traditional video datasets alone are insufficient for training AI systems that interact with the physical world. Brain wave data represents an emerging frontier in multimodal training approaches for robotics and embodied AI.

Bluesky has expanded its AI assistant Attie to function as an open social research tool, allowing users to query news, trends, and conversations across Bluesky and other applications built on the AT Protocol. The move positions Attie as a research instrument for analyzing social media data at scale. This represents a shift from a basic assistant toward a platform for structured data exploration.

Atlassian research finds that 89% of executives report individual workers are speeding up with AI, yet only 6% can identify specific ROI. The disconnect stems from optimizing individual AI use rather than team-level workflows. High-performing teams share three traits: shared context graphs, redesigned end-to-end processes, and cultures that encourage experimentation.