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Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations

Without careful dissection of the ways in which biases can be encoded into artificial intelligence (AI) health technologies, there is a risk of perpetuating existing health inequalities at scale. One major source of bias is the data that underpins such technologies. The STANDING Together recommendations aim to encourage transparency regarding limitations of health datasets and proactive evaluation of their effect across population groups. Draft recommendation items were informed by a systematic review and stakeh…

The Lancet Digital Health · Science

Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations

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Artificial intelligence for modeling and understanding extreme weather and climate events

In recent years, artificial intelligence (AI) has deeply impacted various fields, including Earth system sciences, by improving weather forecasting, model emulation, parameter estimation, and the prediction of extreme events. The latter comes with specific challenges, such as developing accurate predictors from noisy, heterogeneous, small sample sizes and data with limited annotations. This paper reviews how AI is being used to analyze extreme climate events (like floods, droughts, wildfires, and heatwaves), hig…

Climate

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Overview of AI and communication for 6G network: fundamentals, challenges, and future research opportunities

Abstract With the growing demand for seamless connectivity and intelligent communication, the integration of artificial intelligence (AI) and sixth-generation (6G) communication networks has emerged as a transformative paradigm. By embedding AI capabilities across various network layers, this integration enables optimized resource allocation, improved efficiency, and enhanced system robust performance. This paper presents a comprehensive overview of AI and communication for 6G networks, with a focus on their fou…

Science

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A foundation model for the Earth system

Reliable forecasting of the Earth system is essential for mitigating natural disasters and supporting human progress. Traditional numerical models, although powerful, are extremely computationally expensive1. Recent advances in artificial intelligence (AI) have shown promise in improving both predictive performance and efficiency2,3, yet their potential remains underexplored in many Earth system domains. Here we introduce Aurora, a large-scale foundation model trained on more than one million hours of diverse ge…

Climate

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Harnessing data science and artificial intelligence to advance implementation research and practice

Implementation science aims to bridge the gap between research evidence and routine health care practice by understanding and optimizing the integration of evidence-based interventions. In this paper, we identify seven persistent challenges limiting implementation progress, including (1) overwhelming volume of implementation materials (e.g., reports, interviews, surveys); (2) contextual variability; (3) complex interactions between contextual factors, interventions, and outcomes; (4) interest holder engagement c…

Science

Evidence-backed gain

DynStabNet: A Deep Learning Framework for Fast Dynamical Stability Prediction of Crystal Structures

Semiconductor materials are widely used in electronic, optoelectronic, and energy applications. While DFT-based phonon calculations provide highly accurate assessments for dynamical stability of structures, their prohibitive computational cost poses a significant bottleneck for large-scale materials screening. Herein, we develop DynStabNet, an E(3)-equivariant graph neural network (E3GNN) framework that learns dynamical stability from phonon-informed data, enabling rapid prediction without the need for explicit…

Science

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A Review of Water Quality Forecasting and Classification Using Machine Learning Models and Statistical Analysis

The prediction and management of water quality are critical to ensure sustainable water resources, particularly in regions like Malaysia, where rivers face increasing pollution from industrialisation, agriculture, and urban expansion. This review aims to provide a comprehensive analysis of machine learning (ML) models and statistical methods applied in forecasting and classification of water quality. A particular focus is given to hybrid models that integrate multiple approaches to improve predictive accuracy an…

Climate

A Review of Explainable Artificial Intelligence from the Perspectives of Challenges and Opportunities

The widespread adoption of Artificial Intelligence (AI) in critical domains, such as healthcare, finance, law, and autonomous systems, has brought unprecedented societal benefits. Its black-box (sub-symbolic) nature allows AI to compute prediction without explaining the rationale to the end user, resulting in lack of transparency between human and machine. Concerns are growing over the opacity of such complex AI models, particularly deep learning architectures. To address this concern, explainability is of param…

Science

The Illusion of Thinking

Recent generations of frontier language models have introduced Large Reasoning Models (LRMs) that generate detailed thinking processes before providing answers. While these models demonstrate improved performance on reasoning benchmarks, their fundamental capabilities, scaling properties, and limitations remain insufficiently understood. Current evaluations primarily focus on established mathematical and coding benchmarks, emphasizing final answer accuracy. However, this evaluation paradigm often suffers from da…

Science

Review of machine learning approaches for predicting mechanical behavior of composite materials

In recent years, machine learning (ML) has emerged as a powerful tool for predicting the mechanical behavior of composite materials, offering a faster, more cost-effective alternative to traditional testing and simulation methods. This review explores how various ML techniques, including random forests, support vector machines, artificial neural networks, and deep learning models, are used to forecast key material properties such as tensile strength, hardness, fracture toughness, and fatigue life. From a broad s…

Science

Environmental impact and net-zero pathways for sustainable artificial intelligence servers in the USA

The rapidly increasing demand for generative artificial intelligence (AI) models requires extensive server installation with sustainability implications in terms of the compound energy–water–climate impacts. Here we show that the deployment of AI servers across the United States could generate an annual water footprint ranging from 731 to 1,125 million m3 and additional annual carbon emissions from 24 to 44 Mt CO2-equivalent between 2024 and 2030, depending on the scale of expansion. Other factors, such as indus…

Climate

Environmental impact and net-zero pathways for sustainable artificial intelligence servers in the USA

Advances in machine learning and IoT for water quality monitoring: A comprehensive review

Water holds great significance as a vital resource in our everyday lives, highlighting the important to continuously monitor its quality to ensure its usability. The advent of the. The Internet of Things (IoT) has brought about a revolutionary shift by enabling real-time data collection from diverse sources, thereby facilitating efficient monitoring of water quality (WQ). By employing Machine learning (ML) techniques, this gathered data can be analyzed to make accurate predictions regarding water quality. These…

Climate

A survey on large language model based autonomous agents

Abstract Autonomous agents have long been a research focus in academic and industry communities. Previous research often focuses on training agents with limited knowledge within isolated environments, which diverges significantly from human learning processes, and makes the agents hard to achieve human-like decisions. Recently, through the acquisition of vast amounts of Web knowledge, large language models (LLMs) have shown potential in human-level intelligence, leading to a surge in research on LLM-based autono…

Science

Deepfake Technology and Gender-Based Violence: A Scoping Review

Online violence against women (OVAW) is a growing global problem with deepfakes in gender-based violence as one manifestation of this that has recently attracted considerable attention. This scoping review aims to explore emerging complexities in current academic understandings of deepfake in relation to its use in gender-based violence. The review considers how these issues impact and shape what is currently known about deepfakes in relation to OVAW. Articles were collected between July and September 2024 and t…

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Artificial intelligence for quantum computing

Artificial intelligence (AI) advancements over the past few years have had an unprecedented and revolutionary impact across everyday application areas. Its significance also extends to technical challenges within science and engineering, including the nascent field of quantum computing (QC). The counterintuitive nature and high-dimensional mathematics of QC make it a prime candidate for AI's data-driven learning capabilities, and in fact, many of QC's biggest scaling challenges may ultimately rest on development…

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