Google DeepMind has released Perch 2.0, an enhanced open-source audio-recognition model that broadens species coverage to mammals, amphibians and insects beyond birds. With training on over 1.5 million recordings and a streamlined architecture, the tool enables conservationists to analyse vast soundscapes faster, supporting efforts to monitor ecosystems from forests to reefs.

A team at USC’s Viterbi School of Engineering has introduced Allegro-FM, an AI model that simulates billions of atoms with quantum-level accuracy. By modeling how concrete can reincorporate CO₂ during production, the research points toward more sustainable construction methods and faster material innovation.

The University of Southern California’s Viterbi School of Engineering is leading a project called PINCER, a physics-informed causal deep learning model designed to improve predictions of fluid, CO₂, and heat movement in underground formations. Backed by NSF funding, the work supports applications in carbon storage, geothermal energy, and groundwater management, while addressing challenges in modeling subsurface systems.

Researchers at North Carolina State University have developed an AI-powered dynamic flow method that enables self-driving laboratories to generate up to 10 times more data than earlier approaches. The system reduces chemical consumption, accelerates experimentation, and enhances sustainability, potentially shortening materials discovery cycles from years to days.

Scientists at Tufts University have created Anthrobots—microscopic constructs formed from human tracheal cells without genetic modification. These self-assembling entities can move using outward-facing cilia, aid in neural repair in lab tests, and demonstrate epigenetic age reversal. The research suggests new possibilities for regenerative medicine and understanding how cellular organization influences aging.

UK Power Networks has partnered with Open Climate Fix to develop AI-driven tools for estimating untracked rooftop solar generation. The project aims to close data gaps from unmetered installations, improve grid forecasting accuracy, and support the UK’s transition to renewable energy.

A UNESCO and UCL report outlines ways to reduce energy consumption in large language models by up to 90% through smaller models, concise prompts, and compression techniques. The study addresses AI's environmental impact, promotes sustainability, and highlights potential for global accessibility without performance loss.

Australian researchers at UNSW Sydney used artificial intelligence to identify a five-metal alloy catalyst that increases green ammonia production sevenfold at room temperature, achieving near-100% energy efficiency. The plasma-based method, detailed in a June 2025 study, aims to reduce emissions from traditional fertilizer production while enabling on-site generation. Challenges include handling toxicity and scaling with renewables.

New findings reveal that OpenAI's latest AI models, o3 and o4-mini, are generating false information—known as "hallucinations"—at significantly higher rates than their predecessors. This growing issue threatens AI reliability in fields like healthcare and law, prompting researchers and developers to pursue advanced mitigation strategies to curb inaccuracies.

A new study from the University of the Philippines explores how AI can predict antibiotic resistance in E. coli found in Metro Manila’s urban farms. Using machine learning models and genomic data, researchers aim to support faster, cost-effective monitoring of antimicrobial resistance in agricultural settings.

InnoEX 2025 concluded at the Hong Kong Convention and Exhibition Centre, featuring global advancements in AI, smart public services, and drone technology. With over 2,800 exhibitors and 88,000 visitors, the event reinforced Hong Kong’s position as a leader in regional innovation and smart city development.

Brisbane City Council has announced a $15 million AI-driven traffic management trial focused on reducing congestion and optimizing travel across key suburban corridors. The Smarter Suburban Corridors initiative aims to deliver more consistent travel times and prioritize public transport through advanced, data-driven signal control.

Charles Sturt University researchers have introduced an AI-based system that automates the identification of woodland bird calls, streamlining conservation efforts across Australia. The technology delivers real-time data on species distribution, supporting more effective habitat protection and recovery strategies for at-risk bird populations.

A recent study introduces an artificial intelligence system that integrates satellite data and climate variables to monitor harmful algal blooms in inland waters. This technology offers faster, large-scale detection of blooms, helping authorities manage risks to ecosystems, public health, and local economies.

Artificial intelligence is increasingly used in urban planning to monitor air quality, optimize energy use, manage smart grids, and improve waste collection. This report explores verified case studies and discusses both the benefits and challenges of AI adoption for more sustainable and efficient cities.

Chinese researchers have introduced FengShun-CSM, an advanced AI climate model capable of forecasting global weather conditions up to 60 days ahead. This new tool promises to enhance disaster response and agricultural planning by providing more accurate predictions of rainfall and land surface conditions.

Lawrence Berkeley National Laboratory has adopted artificial intelligence across multiple research areas, including materials discovery, climate data analysis, and biofuel production. While AI improves efficiency and enables new scientific approaches, ongoing challenges include data quality, algorithm transparency, and funding stability.

The EnviroPiNet model uses artificial intelligence and physics-based ratios to predict how well water filters will remove pollutants. The approach, developed at the University of Glasgow, offers more accurate and understandable predictions than traditional models.

Researchers at IISER Bhopal have introduced XenoBug, an AI-powered platform designed to identify bacterial enzymes for breaking down environmental pollutants. This web-based tool aims to accelerate bioremediation strategies and offers scientists a new approach to environmental cleanup.

Aardvark Weather is a new AI-powered forecasting system that delivers accurate weather predictions using standard computers, eliminating the need for supercomputers. Developed through a global collaboration, it offers a faster, more accessible solution for weather and climate challenges worldwide.