National Academies of Sciences%2c Engineering%2c and Medicine

Machine Learning for Safety-Critical Applications: Opportunities, Challenges, and a Research Agenda

In recent years, the rapid advancement of machine learning (ML) has led to their early integration into safety-critical systems. As noted in Chapter 2, these technologies offer significant potential ...
National Academies of Sciences%2c Engineering%2c and Medicine

Making Machine Learning Safer in High-Stakes Settings

Machine learning, a key enabler of artificial intelligence, is increasingly used for applications like self-driving cars, medical devices, and advanced robots that work near humans — all contexts ...
Semiconductor Engineering

Using Machine Learning For Characterizations Of NoC Components

Modern NoC (Network-on-Chip) is built of complex functional blocks, such as packet switches and protocol converters. PPA (performance/power/area) estimates for these ...
Hosted on MSN

The new standards of machine learning development

Machine learning has moved past its initial experimental phase. In earlier years, development often focused on creating the largest possible models to see what capabilities might appear. Today, the ...
Homeland Security Today

NIST Seeks Comment on Draft Adversarial Machine Learning Report

NIST’s National Cybersecurity Center of Excellence (NCCoE) has released a draft report on machine learning (ML) for public comment. A Taxonomy and Terminology of Adversarial Machine Learning (Draft ...