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".

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.

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 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.

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.

ROV pilots filmed this glass squid while exploring the Colorado-Rawson submarine canyon off the coast of Argentina. Breakthroughs, discoveries, and DIY tips sent six days a week. The marine biologists of the Schmidt Ocean Institute are a busy bunch. Over the last few years, scientists aboard the research vessel have spotted rare Antarctic squid, discovered multiple octopus near Costa Rica, and even cataloged over 100 potential new species off the coast of Chile. To kick off 2026, the Institute released a trove of new images and videos highlighting some of their latest observations from the south Atlantic Ocean.

With great biological havoc comes great opportunity. Breakthroughs, discoveries, and DIY tips sent every weekday. About 445 million years ago, our planet completely changed. Massive glaciers formed over the supercontinent Gondwana, sucking up sea water like an icy sponge. Now called the Late Ordovician mass extinction (LOME), Earth's first major mass extinction wiped out about 85 percent of all marine species as the ocean chemistry radically changed and Earth's climate turned bitter cold. However, with great biological havoc also comes opportunity.

Breakthroughs, discoveries, and DIY tips sent every weekday. Nearly one in three U.S. households harbor a cold-hearted killer. Some even have a well-known proclivity for torture. And while the popular pets are best known for downing birds and cornering mice, they are also adept at hunting all manner of bugs. Host a cat in your home long enough and you'll likely become accustomed to regular deliveries of amputated insect legs, wings, or the occasional whole carcass.

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 Arboretum aims to facilitate the development of AI models for species identification, ecological monitoring, and agricultural research. The dataset is hosted on Hugging Face. Our dataset will be available for as long as the iNaturalist Open Dataset is maintained.