Both approaches identified hemoglobin as one of the most significant predictors of CKD risk. Additional top-ranked features included blood urea, sodium levels, red blood cell count, potassium, and ...

According to Mercer's 2024 AI in Investment Management global manager survey, 91% of asset managers either currently use AI ...

Examining the difficulties in training LLMs to handle contemporary creative writing styles. Julia Cameron, a renowned American writer, argues in her book The Artist’s Way that creativity is not an ...

Using machine learning, an electronic nose can “smell” early signs of ovarian cancer in the blood. The method is precise and, according to the researchers behind the study, it could eventually be used ...

AI transforms digital wallets from transaction processors into intelligent systems. Instead of enforcing fixed rules, machine learning models evaluate context like user behavior, device ...

Designing and deploying DSPs FPGAs aren’t the only programmable hardware option, or the only option challenged by AI. While AI makes it easier to design DSPs, there are rising complexities due to the ...

People with aphantasia have no mental imagery—and they’re offering brain scientists a window into consciousness ...

Discover how AI can transform eCommerce marketing with personalization, automation, and data insights. Learn top tools, strategies, and benefits.

The most obvious speed bump for autonomous vehicle makers is ensuring that they do not collide with other vehicles, people or objects. However, that is only part of autonomous vehicle safety. Just ...

Linkage mapping is one of the most commonly used methods to identify genetic loci that determine a trait. However, the loci identified by linkage mapping may contain hundreds of candidate genes and ...

Machine Learning (ML) is a subset of Artificial Intelligence that allows computers to “learn” from data. Ordinarily, in programming, we provide data and the expected output, and the machine does the ...

MIT researchers have identified significant examples of machine-learning model failure when those models are applied to data other than what they were trained on, raising questions about the need to ...