AI is consuming more energy than ever, with data centers struggling to keep up with demand. A breakthrough training method ...

The brain's ability to process information is known to be supported by intricate connections between different neuron ...

The enormous computing resources needed to train neural networks for artificial intelligence (AI) result in massive power consumption. Researchers have developed a method that is 100 times faster and ...

Some panelists talked about the needs for AI support in the future.

For instance, if a farmer has chickens and cows, and together they have 40 heads and 120 legs, you might need to write down a ...

By training machine learning models with examples of basic science, Miles Cranmer hopes to push the pace of scientific ...

A research team, led by Professor Jimin Lee and Professor Eisung Yoon in the Department of Nuclear Engineering at UNIST, has ...

The aim of this study was to advance the imputation of missing values for some autoregressive moving average models (ARMA) with generalized autoregressive conditioned heteroscedastic (GARCH) models.

The study presents a useful computational analysis of how the ratio between excitatory and inhibitory neural numbers affects coding capacity. The authors show that increasing the proportion of ...

This study presents useful findings on the differences between male and hermaphrodite C. elegans connectomes and how they may result in changes in locomotory behavioural outputs. However, the study ...

New brain-inspired hardware, architectures and algorithms could lead to more efficient, more capable forms of AI.

Deep neural networks have hit a wall. An entirely new, backpropagation-free AI stack promises to be orders of magnitude more ...