One of the key challenges of machine learning is the need for large amounts of data. Gathering training datasets for machine learning models poses privacy, security, and processing risks that ...

SAN FRANCISCO--(BUSINESS WIRE)--Today MLCommons™, an open engineering consortium, released new results for three MLPerf™ benchmark suites - Inference v2.0, Mobile v2.0, and Tiny v0.7. These three ...

This post details the beginning of Bloomberg’s journey to build a machine learning inference platform. For those readers who are less familiar with the technical concepts involved in machine learning ...

Machine learning (ML)-based approaches to system development employ a fundamentally different style of programming than historically used in computer science. This approach uses example data to train ...

Forbes contributors publish independent expert analyses and insights. I write about neuroscience and its intersection with technology. Despite the continued progress that the state of the art in ...

The promise of artificial intelligence (AI) technology is finally enjoying commercial success in many industries, including automotive, manufacturing, retail, and logistics, in the form of machine ...

In the past few years, Python has become the preferred programming language for machine learning and deep learning. Most books and online courses on machine learning and deep learning either feature ...

The company chose to implement one-dimensional Tensor processors (Fig. 2), which can be combined to handle two- and three-dimensional tensors. The units support a high-precision Winograd acceleration ...

SAN FRANCISCO – April 6, 2022 – Today MLCommons, an open engineering consortium, released new results for three MLPerf benchmark suites – Inference v2.0, Mobile v2.0, and Tiny v0.7. MLCommons said the ...

One of the key challenges of machine learning is the need for large amounts of data. Gathering training datasets for machine learning models poses privacy, security, and processing risks that ...