MIT researchers have identified significant examples of machine-learning model failure when those models are applied to data ...

Forbes contributors publish independent expert analyses and insights. Writes about the future of finance and technology, follow for more. We live in a world where machines can understand speech, ...

In recent years, JupyterLab has rapidly become the tool of choice for data scientists, machine learning (ML) practitioners, and analysts worldwide. This powerful, web-based integrated development ...

Recent advancements in machine learning have ushered in a transformative era for seismic data analysis. By integrating sophisticated algorithms such as convolutional neural networks (CNNs), generative ...

The data science and machine learning technology space is undergoing rapid changes, fueled primarily by the wave of generative AI and—just in the last year—agentic AI systems and the large language ...

The integration of bioinformatics, machine learning and multi-omics has transformed soil science, providing powerful tools to ...

AI and machine learning are revolutionizing drug discovery, development, and lifecycle management, addressing industry ...

Until now, designing complex metamaterials with specific mechanical properties required large and costly experimental and simulation datasets. The method enables ...

Machine learning, a key enabler of artificial intelligence, is increasingly used for applications like self-driving cars, medical devices, and advanced robots that work near humans — all contexts ...

Cambodia is not alone in facing capacity limitations in the production and timely release of key official statistics needed for data-driven policy decisions. This paper demonstrates that combining ...