AI & Machine Learning

AI in Manufacturing Market Forecast to Skyrocket to $155 Billion by 2030 The global market for artificial intelligence in manufacturing is predicted to grow from USD 34.18 billion in 2025 to USD 155.04 billion by 2030, at a compound annual growth rate (CAGR) of 35.3%. This significant growth is attributed to the increasing use of AI for improving production efficiency, predictive maintenance, and data-driven decision-making. Key technologies driving this expansion include machine learning and computer vision, with major adoption seen in the automotive and aerospace sectors. The report highlights Europe as a key region poised for substantial growth due to widespread industrial modernization and digital innovation initiatives. ...

AI Infrastructure: OpenAI, Oracle, and SoftBank Announce 5 New US Data Centers OpenAI, Oracle, and SoftBank have announced the development of five new artificial intelligence (AI) data center locations in the United States as part of the Stargate AI supercomputer project. The new sites will be located in Shackelford County, Texas; Doña Ana County, New Mexico; Lordstown, Ohio; Milam County, Texas; and an unannounced Midwest location. This expansion brings the total planned capacity for the Stargate project to nearly 7 gigawatts, with a total investment exceeding $400 billion over the next three years. The companies are now ahead of schedule to meet their goal of a $500 billion, 10-gigawatt commitment by the end of 2025. The project is expected to generate over 25,000 onsite jobs. These new data centers are designed to provide the massive computing power required to train and operate next-generation AI models. ...

Mount Sinai’s ‘AEquity’ AI Tool Aims to Eliminate Healthcare Algorithm Bias Researchers at the Icahn School of Medicine at Mount Sinai have introduced AEquity, a novel AI tool designed to detect and mitigate biases in healthcare machine learning algorithms. As detailed in the Journal of Medical Internet Research, this tool confronts the critical challenge of algorithmic bias, which can compromise the accuracy of medical diagnoses and treatment strategies. AEquity was validated on diverse health datasets, including medical imagery and patient records, successfully identifying both known and previously undiscovered biases. The primary objective of AEquity is to ensure AI-driven healthcare solutions are both effective and equitable for all patient demographics, particularly those underrepresented in training data. The framework is built for adaptability, supporting a wide array of machine learning models. ...