Thinking Machines Lab has released Inkling, its first general-purpose artificial intelligence model, giving developers access ...

Some, however, find that bridging fields opens new realms of possibility. Henry Kvinge, who trained as a mathematician but ...

Quantum computers promise to solve problems that would take even the fastest conventional supercomputers a vast amount of ...

In 1943, a pair of neuroscientists were trying to describe how the human nervous system works when they accidentally laid the foundation for artificial intelligence. In their mathematical framework ...

Objective Early prediction of long-term outcomes in patients with systemic lupus erythematosus (SLE) remains a great challenge in clinical practice. Our study aims to develop and validate predictive ...

Mental health disorders (MHDs) have significant medical and financial impacts on patients and society. Despite the potential opportunities for artificial intelligence (AI) in the mental health field, ...

The simplified approach makes it easier to see how neural networks produce the outputs they do. A tweak to the way artificial neurons work in neural networks could make AIs easier to decipher.

Here we show, via both analytical approaches and simulations, that a broad range of biologically relevant learning models generalize transitively and recapitulate the symbolic distance, terminal item, ...

Recent developments in deep learning, coupled with an increasing number of sequenced proteins, have led to a breakthrough in life science applications, in particular in protein property prediction.

Research in the field of machine learning and AI, now a key technology in practically every industry and company, is far too voluminous for anyone to read it all. This column, Perceptron, aims to ...

Conventional von Newmann-based computers face severe challenges in the processing and storage of the large quantities of data being generated in the current era of “big data.” One of the most ...