Blog

Peaks and Valleys: How data segmentation can conserve power and CPU cycles in Edge AI systems

By J. Sieracki When working with real-time streaming data, segmentation will be one of the first issues you encounter. Real-time streaming data has to be

Reality AI CEO, Stuart Feffer, Featured on The Global Safety Podcast from Lloyd’s Register Foundation

Each year, an estimated 2.8mil people die from accidents in the workplace. Families are torn apart, reputations ruined, share prices crash and consumer confidence tumbles.

Reality AI CEO Featured on Latest Mobility Now! Podcast

Stuart explains why sound detection is a critical part of ADAS and autonomous driving. He shares the science behind Reality AI’s technology, and how it finds unique features in sounds to accurately identify them.

Reality AI to Host ARM AI Virtual Tech Talk on January 26

Date: January 26, 2021
Time: 8am PST / 4pm BST / 11pm CST
Stuart Feffer, CEO & Co-Founder Reality AI

EMBEDDED AI AND MACHINE LEARNING ADDING NEW ADVANCEMENTS IN TECH SPACE

With current tools, integrating new options for machine learning for signals (like Reality AI) it is getting simpler.

Mobility Xlab Interviews Future Mobility Award Winners

Nalin Balan is the head of business development at Reality AI and he took some time to talk to us about their work and winning the Future Mobility Award.

Reality AI to work on Industrial Safety Inspection Accelerator

Reality AI Selected to Work with Sellafield Limited and National Nuclear Laboratory on Industrial Safety Inspection Accelerator

Want to Reduce the Cost of Data Collection for Edge AI with Sensors? Only do it once.

If you have ever attempted or completed a machine learning project using sensor data, you probably know already that data collection and preparation is both the most costly part of the project and also the place where you are most likely to go off track.

Edge AI – Difference Between a Project and a Product

Edge AI and TinyML are having a moment. The tech world has woken up to the fact that it is possible to put machine learning models on small, inexpensive microcontrollers, and GitHub is now full of examples of TinyML models for all sorts of things.

Comprehensive AI Engineering Software for Making Smart Edge Devices with Sensors

Welcome to Reality AI 4.0!

How Do You Make AI Explainable? Start with the Explanation.

“No, no! The adventures first. Explanations take such a dreadful time!” – Lewis Carroll, “Alice in Wonderland Explanations for model behavior are starting to get

Bias Isn’t Always Bad

“Facts are stubborn things. But statistics are pliable” -Mark Twain This blog is about statistical bias in machine learning models. But unlike most of what

Reality AI demonstrates Edge AI solutions for real problems running on Cortex M4 and M7 platforms

Edge AI is finally starting to get the attention of the technical trade press. It’s been a real thing for a while – particularly in autonomous driving applications and wearables – but other applications are starting to get some attention too.

FFTs and Stupid Deep Learning Tricks

Deep Learning has nearly taken over the machine learning world — in large part due to its great success in using layers of neural networks to discover the features in underlying data that actually matter to other, higher-level layers of neural networks.

Successful Data Collection for Machine Learning with Sensors

It’s R&D time. The product guys have dreamed up some new features, and now you have to see if its possible to deliver them. If it is possible, you’ll need to build it.