Joe McKendrick

IoT-extended AI brings three main benefits to manufacturers: supply chain optimization, smart manufacturing, and product or service innovation,

IoT-enhanced AR could be instrumental in helping people collaborate – whether they work in factories or other installations.

A fireside chat at AI World discussed the issues encountered in extracting data from the edge.

Manufacturing is no longer just about putting items on a boat and going across oceans. It’s about being able to design something and test it before you ever go through that process

Everybody asks which industry is going to adopt IoT first. It’s manufacturing because it was the first industry to start looking at it seriously.

MLOps, like its counterpart DevOps, focuses on building and maintaining the machine learning pipeline across all the different stages in the lifecycle of AI applications.

As the use of AI and machine learning grows, there are opportunities to better serve viewers with relevant content when and how it is requested.

AI on ESA satellites will filter out less than perfect images to avoid clogging earthbound communications link.

As IoT data becomes a more important part of enterprise business operations, the ability to reduce latency in data analytics and processing can make a difference. It raises the promise of real-time to a new level.

AI is unlikely to replace a human clinician anytime soon. However, it may help with synthesizing and collecting patient data.

When trying to determine where to carry out analysis, application characteristics and edge-cloud transfer bandwidth are the key factors affecting performance.

The proposed grocery system system tracks product inventories, notifies consumers when they are running low on products, and offers available purchase options from different suppliers.