In a network, pairs of individual elements, or nodes, connect to each other; those connections can represent a sprawling system with myriad individual links. A hypergraph goes deeper: It gives ...

In the ever-growing field of machine learning, one of the most significant challenges is making complex models interpretable and accessible. Enter AutoXplainAI, an innovative framework developed by ...

MIT researchers found that different algorithms can all be grouped into a ‘periodic table’ of AI. The idea for the table was ...

A recent study introduces an advanced anomaly-based intrusion detection system (IDS) designed to address the increasing cyber threats targeting Internet of Things (IoT) devices. By combining machine ...

Department of EECS, University of California at Berkeley, 485 Soda Hall, Berkeley CA 94720-1776, USA Previously at: Institute for Adaptive and Neural Computation, University of Edinburgh, UK.

It can be relatively cheap to gather a lot of bio-signal data. To teach a machine-learning algorithm to find a relationship between bio-signals and health outcomes, however, you need to teach the ...

In 2017, Ezekiel Emanuel, a well-known oncologist and health policy commentator, said radiologists would soon be out of work thanks to machine learning. That hasn’t happened, but although ...

For the past 10 years we have been developing state-of-the-art AI methods, including deep-learning algorithms and large language models for natural language processing. These methods have been ...

For the past 10 years we have been developing state-of-the-art AI methods, including deep-learning algorithms and large language models for natural language processing. These methods have been ...

The team first estimated which genes were present in ancestral genomes. They then used machine learning to predict whether or not each ancestor used oxygen to live. To best utilise fossil records ...