ABOUT THIS FEED
AI Hub (aihub.org) is a nonprofit initiative designed to connect the AI research community and make cutting-edge knowledge accessible to wider audiences. Its RSS feed aggregates curated articles, interviews, event summaries, and research updates from across the AI ecosystem. The focus is on community engagement: highlighting work from researchers, promoting open collaboration, and sharing insights into AI’s role in academia, policy, and society. Unlike fast-paced news feeds, AI Hub emphasizes depth, context, and inclusivity, featuring content from global contributors. With several posts each day, it ensures that readers are exposed to diverse voices and emerging trends. This feed is particularly valuable for academics, policymakers, and AI enthusiasts who want a comprehensive, community-driven perspective on artificial intelligence beyond just commercial applications.
- RoboCup Logistics League: an interview with Alexander Ferrein, Till Hofmann and Wataru Uemura
The Industrial League arena at RoboCup2025. RoboCup is an international scientific initiative with the goal of advancing the state of the art of intelligent robots, AI and automation. The annual RoboCup event took place from 15-21 July in Salvador, Brazil. The Logistics League forms part of the Industrial League and is an application-driven league inspired
- Data centers consume massive amounts of water – companies rarely tell the public exactly how much
Lone Thomasky & Bits&Bäume / Digital Society Bell / Licenced by CC-BY 4.0 By Peyton McCauley, University of Wisconsin-Milwaukee and Melissa Scanlan, University of Wisconsin-Milwaukee As demand for artificial intelligence technology boosts construction and proposed construction of data centers around the world, those computers require not just electricity and land, but also a significant amount
- Interview with Luc De Raedt: talking probabilistic logic, neurosymbolic AI, and explainability
Should AI continue to be driven by a single paradigm, or does real progress lie in combining the strengths and weaknesses of many? Professor Luc De Raedt of KU Leuven has spent much of his career persistently addressing this question. Through pioneering work that bridges logic, probability, and machine learning, he has helped shape the
- Call for AAAI educational AI videos
The Association for the Advancement of Artificial Intelligence (AAAI) is calling for submissions to a competition for educational AI videos for general audiences. These videos must be two to three minutes in length and should aim to convey informative, accurate, and timely information about AI research and applications. The video could highlight your own research,
- Self-supervised learning for soccer ball detection and beyond: interview with winners of the RoboCup 2025 best paper award
Presentation of the best paper award at the RoboCup 2025 symposium. An important aspect of autonomous soccer-playing robots concerns accurate detection of the ball. This is the focus of work by Can Lin, Daniele Affinita, Marco Zimmatore, Daniele Nardi, Domenico Bloisi, and Vincenzo Suriani, which won the best paper award at the recent RoboCup symposium.
- How AI is opening the playbook on sports analytics
Professional sports teams pour millions of dollars into data analytics, using advanced tracking systems to study every sprint, pass, and decision on the field. The results of that analysis, however, are industry secrets, making many sports difficult for researchers to study. Now, two University of Waterloo researchers, Dr. David Radke and Kyle Tilbury, are using
- Discrete flow matching framework for graph generation
Figure 1: DeFoG progressively denoises graphs, transforming random structures (at t=0) into realistic ones (at t=1). The process is similar to reassembling scattered puzzle pieces back into their correct places. Designing a new drug often means inventing molecules that have never existed before. Chemists represent molecules as graphs, where atoms are the “nodes” and chemical
- We risk a deluge of AI-written ‘science’ pushing corporate interests – here’s what to do about it
Nadia Piet & Archival Images of AI + AIxDESIGN / AI Am Over It / Licenced by CC-BY 4.0 By David Comerford, University of Stirling Back in the 2000s, the American pharmaceutical firm Wyeth was sued by thousands of women who had developed breast cancer after taking its hormone replacement drugs. Court filings revealed the
- Deploying agentic AI: what worked, what broke, and what we learned
Fanny Maurel & Digit / Ambient Scribes / Licenced by CC-BY 4.0 By Francis Osei 1. We built agentic systems. Here’s what broke When Agentic AI started dominating research papers, demos, and conference talks, I was curious but cautious. The idea of intelligent agents, autonomous systems powered by large language models that can plan, reason,
- Memory traces in reinforcement learning
The T-maze, shown below, is a prototypical example of a task studied in the field of reinforcement learning. An artificial agent enters the maze from the left and immediately receives one of two possible observations: red or green. Red means that the agent will be rewarded for moving to the top at the right end