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.
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.
Date: January 26, 2021
Time: 8am PST / 4pm BST / 11pm CST
Stuart Feffer, CEO & Co-Founder Reality AI
With current tools, integrating new options for machine learning for signals (like Reality AI) it is getting simpler.
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 Selected to Work with Sellafield Limited and National Nuclear Laboratory on Industrial Safety Inspection Accelerator
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 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.
Welcome to Reality AI 4.0!
“No, no! The adventures first. Explanations take such a dreadful time!” – Lewis Carroll, “Alice in Wonderland Explanations for model behavior are starting to get
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.
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.
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.