Diagnostics, Asset Management

Data flow is no longer hierarchical

Can industrial edge computing fit into the Purdue model?

Reducing AI’s carbon footprint

MIT researchers have developed an automated AI system for training and running certain neural networks that also cuts down the pounds of carbon emissions involved.

Machine learning model quantifies quarantine measures on COVID-19’s spread

A machine learning algorithm developed by MIT researchers combines data from COVID-19’s spread with a neural network to assess the impact of quarantine measures and predict when infections will slow down in each country.

Remote monitoring and data visualization

Remote monitoring and data visualization are key to improving overall manufacturing efficiency.

Edge computing terms and skills

Six edge computing questions to ask about data collection, networking and control systems.

Coronavirus reveals weaknesses, potential opportunities in supply chain

Even in the wake of COVID-19 (coronavirus), manufacturers still need human workers to manage the supply chain. The need for more automation and information is an opportunity for manufacturers.

Doing machine learning the right way

MIT Professor Aleksander Madry strives to build machine-learning models that are more reliable, understandable and robust.

How to compete and win with automation

With automation at an inflection point, a variety of technologies and interoperable standards efforts are bringing higher levels of flexibility, integration and optimization.

Software readiness for data analytics and Big Data

Expand industrial data access and get more out of it with tools such as message queuing telemetry transport (MQTT), which can help manufacturers realize Industry 4.0’s benefits.

Preventing coronavirus through preparation

Many manufacturers are becoming increasingly concerned with Covid-19 breakouts on the production floor. There are ways to improve worker safety through preventive best practices and proper preparation.