While the development of artificial neural memories had been earlier, the celebrated Hopfield model 1,2 was a significant milestone in the modeling of associative memory in neural networks. In this ...

Kernel functions are vital ingredients of several machine learning (ML) algorithms but often incur substantial memory and computational costs. We introduce an approach to kernel approximation in ML ...

A recent paper published in Engineering titled “Machine Memory Intelligence: Inspired by Human Memory Mechanisms” explores a novel approach to AIby drawing inspiration from the human brain’s memory ...

However, by the late 1970s, there was disappointment that the two main approaches to computing in medicine — rule-based systems and matching, or pattern recognition, systems — had not been as ...

Researchers at Google have developed a new AI paradigm aimed at solving one of the biggest limitations in today’s large language models: their inability to learn or update their knowledge after ...

Compute architectures are changing radically from bringing in memory using CXL on the PCI Express interface to integrating machine-learning and artificial-intelligence (ML/AI) accelerators with ...

In modern CPU device operation, 80% to 90% of energy consumption and timing delays are caused by the movement of data between the CPU and off-chip memory. To alleviate this performance concern, ...

Machine learning is a subfield of artificial intelligence, which explores how to computationally simulate (or surpass) humanlike intelligence. While some AI techniques (such as expert systems) use ...