Joe McKendrick

Survey finds a great boom in IoT and edge computing deployments. As a result, the number of assets tied to edge and IoT is expanding.

Generative AI is giving developers more ways to impart human-like qualities and more functionality into modern robots.

The lack of standards in data generation and sharing hinders the interoperability of data required for digital twins. That and other factors make a cross-agency digital twin strategy all the more important.

Rather than focusing on data science metrics, predictive analytics efforts must be tied to business outcomes.

Digital twins are helping to address and provide transparency into matters keeping manufacturing leaders awake at night, such as forecasting abilities, material and labor constraints caused by rising costs, and talent gaps.

A recent survey found that businesses believe generative AI has massive potential to enable their real-time data analytics efforts.

Wearables and other sensors designed to collect data about humans are finding new applications across a number of fields.

AI-boosted business intelligence will simplify query generation and empower data analysts to delve deeper into data analysis by producing comprehensive reports and visualizations.

AI is increasingly being seen as a way to derive greater intelligence from the vast amount of data generated by connected vehicles.

The merging of AI and IoT enables autonomous connected devices that have the ability to independently decide and execute the best course of action.

AI and machine learning are going to shake up how companies operate, and streaming data amplifies the leverage that can be gained from these powerful tools.

Wireless AI, made possible by 5G, makes new system efficiencies and user experiences possible, and also lays the foundation for future innovations.