@gql

Oxilite Studio is a VS Code-based workbench that brings knowledge-graph editing, querying, reasoning, validation, testing, and debugging into one environment. It supports RDF/OWL, SPARQL, Datalog, Cypher, SHACL, and real SQLite or Cloudflare D1 databases, while also explaining how inferred data was produced and integrating checks into CI.

https://oxilitedb.com/articles/oxilite-studio.html

This Penn Engineering research team draws on combined expertise from physics, mathematics, and computer science to build low-data robot learning systems that eliminate the need for massive training datasets to pick up new skills. The team’s two core innovations include a skill framework that lets robots build complex long-sequence tasks from basic learned movements, and a Gaussian Graph workspace roadmap that stitches a small set of existing motion demonstrations together to help robots navigate untaught paths and adapt in real time if obstacles interrupt their route. The lab is currently pairing these systems with language models and adapting them for dual-arm, humanoid and mobile manipulator robots, with the end goal of creating flexible, adaptive robotic partners that can reliably operate in the messy, unpredictable unstructured spaces of everyday real life, and the work has earned top honors at several major global robotics conferences.

Graph Day: NYC is a full-day event focused on using graph technology to solve complex data and AI challenges, including fraud, identity, and GenAI applications. Attendees can explore Spanner and BigQuery Graph, learn about GraphRAG, GNNs, #GQL, and zero-ETL integration, and get hands-on experience building and querying knowledge graphs.

https://cloud.google.com/events/graph-day-nyc

AI-generated content is becoming harder to distinguish from human-created work, increasing the need for reliable ways to verify where content comes from and whether AI was involved. Watermarking and provenance systems can provide useful evidence, but they are not foolproof, and relying too heavily on AI detectors can lead to false accusations, bias, and misplaced suspicion.

https://news.engineering.asu.edu/2026/09/the-rise-of-ai-suspicion/

Public trust in artificial intelligence has plummeted sharply even as everyday reliance on the technology reaches new highs, with growing public anxiety shifting from near-term disruptions like job displacement to long-term, once-fictional doomsday scenarios that once felt confined to science fiction. Leading experts across top academic institutions have weighed in on these rising fears, pushing back against unsubstantiated extreme doomsday claims while acknowledging that unregulated, breakneck AI development carries real, underaddressed risks to public safety and human control. Rather than pushing for a full, blanket pause on all AI progress, most informed voices are calling for balanced, expert-led guardrails that prioritize safety testing and responsible governance, rejecting both unthinking tech utopianism and ungrounded alarmism to steer the technology toward more sustainable, public-focused advancement.

https://poll.qu.edu/poll-release?releaseid=3969