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What's coming up at #RoboCup2026?

AIHub

This year, RoboCup will be held in Incheon, South Korea, from 2-6 July. The event will see teams take part in competitions, training sessions, and a symposium. It's an exciting time for RoboCup, as there have been some updates to the leagues and competition format . Most prominently, the soccer leagues will have a primary focus on humanoid robots. A workshop focused on sharing projects, experiences, and innovations in educational robotics.


AI model used to generate complete models of proteins in motion

AIHub

Many drug and antibody discovery pathways focus on intricately folded cell membrane proteins. When molecules of a drug candidate bind to these proteins, like a key going into a lock, they trigger chemical cascades that alter cellular behavior. Understanding how proteins fold and move is therefore essential for developing drugs that interact well with their targets. Artificial intelligence (AI) is a very useful tool to generate novel protein structures, but most systems - including Google DeepMind's AlphaFold - focus on producing static'snapshots' of proteins. Subtle rearrangements of atoms in structures called side chains, which influence a protein's interactions with other molecules, are not captured.


Three ways to avoid being fooled by AI slop

AIHub

Global society makes billions of images and uploads hundreds of thousands of hours of video on the internet every day. The problem is, some of this content is misleading or downright wrong. And when it's in visual form, it can be particularly convincing . Take the Met Gala that happened earlier this month in New York. While photographers snapped photos of Rhianna, Beyoncรฉ and Nicole Kidman as they strutted their stuff, others saw "photos" of celebrities, such as Rosalรญa, Lady Gaga and Jacob Elordi, who were actually elsewhere (the images in the below Instagram carousel are AI generated).


Engineering Out Loud: S13E1 โ€“ How many robots can a single human supervise?

AIHub

Engineering Out Loud: S13E1 - How many robots can a single human supervise? Will swarms of autonomous aerial vehicles be able to aid humans in wildland firefighting or package delivery? Research summarized in a new paper in Field Robotics represents a big step towards realizing such a future. In this interview, Professor Julie A Adams describes the research showing that one person can supervise more than 100 autonomous ground and aerial robots. "Engineering Out Loud" is a podcast from the College of Engineering at Oregon State University.


AIhub monthly digest: June 2026 โ€“ biodiversity, resource allocation, and color metaphors

AIHub

Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we found out how foundation models are being used for conservation efforts, how AI can help with scarce resource allocation, and how color metaphors and LLMs can teach us about human cognition. We also went to ICRA and captured some footage of cutting-edge robots. In this latest interview in our AAAI Fellow series, we found out about Tanya Berger-Wolf's research developing a foundation model for biology, the insights this model can provide for conservation and protecting ecosystems, interesting collaborations over the years, and what the future has in store. In this interview, we chat to Sanmay Das, who was elected as a Fellow "for development of multiagent interaction mechanisms and learning techniques in the public interest, and for leadership service to the profession".


AAAI presidential panel โ€“ AI agents

AIHub

The Future of AI Research report, published in March 2025, aims to clearly identify the trajectory of AI research in a structured way. The report was led by outgoing AAAI President Francesca Rossi and covers 17 different AI topics . Members of the report team, and other selected AI practitioners, are taking part in a series of video panel discussions covering selected chapters from the report. In the fifth discussion in the collection, the three panellists tackle the topic of AI agents. How multi-agent systems evolved from rule-based systems to complex cooperative frameworks built on generative AI, and what is really different in the modern notion of an agentic AI system.


4 lawn options for people who hate mowing

Popular Science

Grass alternatives can bring beauty (and bees) to your yard. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. If you dislike mowing the lawn, you have other options. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .


Arboretum: ALarge Multimodal Dataset Enabling AI for Biodiversity (Supplemental Material)

Neural Information Processing Systems

Arboretum is a 134.6M sample dataset designed to advance AI for biodiversity applications by providing a large-scale, accurately annotated multimodal dataset that includes images and corresponding textual descriptions for a diverse set of species. Arboretum aims to facilitate the development of AI models for species identification, ecological monitoring, and agricultural research. Additionally, we introduce three new benchmark datasets: Arboretum-Unseen, Arboretum-LifeStages, and Arboretum-Balanced. As the authors of this submission, we affirm that we bear all responsibility in case of any rights violations or ethical issues associated with this work. We confirm that the submitted work is original, and if it includes third-party content, it is used with proper permissions and attributions.


Image Enabling AI for Biodiversity

Neural Information Processing Systems

We introduce BioTrove, the largest publicly accessible dataset designed to advance AI applications in biodiversity. Curated from the iNaturalist platform and vetted to include only research-grade data, BioTrove contains 161.9 million images, offering unprecedented scale and diversity from three primary kingdoms: Animalia ("animals"), Fungi ("fungi"), and Plantae ("plants"), spanning approximately 366.6K species. Each image is annotated with scientific names, taxonomic hierarchies, and common names, providing rich metadata to support accurate AI model development across diverse species and ecosystems. We demonstrate the value of BioTrove by releasing a suite of CLIP models trained using a subset of 40 million captioned images, known as BioTrove-Train. This subset focuses on seven categories within the dataset that are underrepresented in standard image recognition models, selected for their critical role in biodiversity and agriculture: Aves ("birds"), Arachnida ("spiders/ticks/mites"), Insecta ("insects"), Plantae ("plants"), Fungi ("fungi"), Mollusca ("snails"), and Reptilia ("snakes/lizards"). To support rigorous assessment, we introduce several new benchmarks and report model accuracy for zero-shot learning across life stages, rare species, confounding species, and multiple taxonomic levels. We anticipate that BioTrove will spur the development of AI models capable of supporting digital tools for pest control, crop monitoring, biodiversity assessment, and environmental conservation. These advancements are crucial for ensuring food security, preserving ecosystems, and mitigating the impacts of climate change. BioTrove is publicly available, easily accessible, and ready for immediate use.