
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.
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.
Accelerometers and vibration sensors are having their day. As prices have come down drastically, we are seeing more and more companies instrumenting all kinds of devices and equipment. Industrial, automotive and consumer products use cases are proliferating almost as fast as startups with “AI” in their names.
We’re an AI company, so people always ask about our algorithms. If we could get a dollar for every time we’re asked about which flavor of machine learning we use –convolutional neural nets, K-means, or whatever – we would never need another dollar of VC investment ever again.
At Reality AI, we make software that customers use to build machine learning models on sensor and signal data – and it usually works pretty well.
Industrial controls — particularly “Condition Monitoring” systems — are increasingly supported by sophisticated sensors and data analysis tools. Using artificial intelligence (AI) for data classification and analysis, these tools do not simply add intelligence to IoT sensors.
Over the last few years, as sensor and MCU prices plummeted and shipped volumes have gone thru the roof, more and more companies have tried to take advantage by adding sensor-driven embedded AI to their products.
Machine learning on high-sample-rate sensor data is different. For a lot of reasons. The outcomes can be very powerful – just look at the proliferation of “smart” devices and the things they can do. But the process that creates the “smarts” is fundamentally different than the way most engineers are used to working.
Reality AI stole the show and won the Gold Medal Best of Sensors Midwest 2017 award in Rosemont last week. We would like to thank all the attendees of the show and our customers for their trust and loyalty.
Japanese language coverage of Reality AI at Sensors Expo 2017.
IoT World sponsors a startup pitch competition in conjunction with Project Kairos, looking for the most innovative startups active in the Internet of Things.
TechCrunch interviews CEO Stuart Feffer and profiles Reality AI on the floor of Disrupt NYC 2017.
Design News names Reality AI one of “Ten Artificial Intelligence Companies You Should Know”.
IoT Evolution Expo Awards the “Most Innovative and Creative Companies” in the Industry.
We all know what a sensor is, right?
A sensor makes “sense” of physical property — it turns something about the physical world into data upon which a system can act. Traditionally, sensors have filled well defined, single-purpose roles: A thermostat, a pressure switch, a motion detector, an oxygen sensor, a knock detector, a smoke detector, a voltage arrestor.
At Reality AI we see a lot of machine learning projects that have failed to get results, or are on the edge of going off the rails. Often, our tools and structured approach can help, but sometimes not.
Reality Analytics, Inc. (Reality AI) was founded in 2016 to provide advanced signal recognition capabilities to corporate R&D and operations technology teams.
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