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.
Edge AI will be at the center of the fifth industrial revolution. The focus will shift from machinery toward people, productivity and sustainability. Embedded electronics is the foundation for this shift. While it may be difficult to predict the magnit...
Pre-built integrations eliminate stumbling blocks of edge AI development. Engineers can skip weeks of configuration and jump straight into building their core applications.
Artificial intelligence (AI) and machine learning (ML) can be used to pull insights out of huge volumes of information quickly and efficiently. AI/ML can also give machines the ability to process information like humans do. AI/ML can perform recogniti...
Machine learning has been embraced by smart applications to solve virtually unsolvable problems. Also known as predictive analytics, it is a mathematical method that "learns" from known good data.
Some 82% of engineers surveyed by Avnet say they have products or are working on new designs that feature AI. The latest Avnet Insights survey explored how engineers are using AI in the design process and incorporating it into product design. The surv...
Production-ready demo code and quick implementation. That’s what they tell you, but is it too good to be true? We break down some of the myths surrounding AI software implementation.
We are hearing more about AI at the edge, but where exactly is the edge? Is it just a concept, or something more tangible? Is it a hub or router in a building, a sensor in a field, or everywhere in between?
Autonomous Mobile Robots are performing tasks that are too dangerous, tedious, or costly for humans. Designing AMRs involves many technical and business challenges. This article covers these challenges and how Avnet will help you overcome them.
Today’s most popular 3D imaging technologies are Time of Flight (ToF), stereo vision and structured light. This article overviews the basic operating principles and the relative strengths, weaknesses and trade-offs associated with each option.
From planes, trains, and automobiles to farm equipment and Martian rovers, engineers are extending the capabilities of autonomous technologies to achieve simple productivity improvements and a better understanding of big issues like global warming.
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.
Some may think of machine learning as the supporting act for AI, but in the industrial sector it is the main attraction. We spoke with Avnet Silica’s Michaël Uyttersprot, manager, system solutions, AI/ML and Vision, EMEA, to find out more.
A small intelligent condition-monitoring project provides a great entry point to the IIoT. Modules are available that make light work of sensing, data analytics and device management.
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.
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?
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.
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?
The latest developments in AI and IoT are shaping the future of logistics with data-driven, intelligent supply chains employing machine learning (ML). AI can deliver increased optimization in logistics while giving businesses speed and flexibility.
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.
Companies across markets from consumer electronics to factory floors are finding power in the introduction of two letters: AI (artificial intelligence).