Machine learning is transforming the world. Algorithms that used to work with the cloud are now scaling beyond to the edge. From surveillance to ADAS, and robotics to data centers—explore our resources to get technical expertise and stay up to date with machine learning trends and technology.
Below, we answer key questions about the Arduino Pro series, highlighting its role in industrial applications, its capabilities, cloud integration, and the ease of developing machine learning models on the edge.
This article looks at how these modules have evolved from development tools, for teaching and prototyping, to enterprise-quality components that can simplify your product development while meeting industrial standards.
With the number of connected nodes that make up the Internet of Things (IoT) growing on a daily basis, it is universally accepted that the way in which machine learning (ML) inferencing is executed has to change.
Artificial Intelligence (AI) will become so commonplace it will be taken for granted. We can say this with some confidence, because so many semiconductor manufacturers already have embedded processors that are designed for AI.
Putting more real-time intelligence at the heart of industrial automation is creating a new breed of control technology. Connectivity and control must go hand in hand in the Industrial IoT, but it requires the best of both worlds.
Work on 6G is already underway to define what the standard will do and how it will do it. If 5G is about enabling the Internet of Things (IoT), 6G is about enabling the Internet of Everything – including you.
Predictive maintenance, using IoT to anticipate and prevent breakdowns by collecting and analyzing machine data, has been gaining momentum in recent years.
Machine vision (MV) and artificial intelligence (AI) provide valuable inspection and analysis capabilities to a wide range of leading-edge applications. As with all advanced technologies, there are pitfalls to avoid.
Field programmable gate arrays (FPGAs) deliver many advantages to artificial intelligence (AI) applications. How do graphics processing units (GPUs) and traditional central processing units (CPUs) compare?
AI at the edge will have profound effects on many industries, including transportation, defense, manufacturing and healthcare to name a few. How will AI at the edge change these industries, and why does it matter?
Technologies like machine learning and AI help retailers to process massive amounts of customer data. And with this data in hand, retailers can thoroughly understand their customers’ buying behavior.
Whether you know it or not, this technology has a lot of potential to deal with business-specific challenges and the benefits of deep learning outnumber its drawbacks.