Determine what you need

We have solutions for each step in the R&D process

Collect and Label Data

Data Collection

We offer several different options for data collection hardware covering accelerometer, vibration, sound and electrical signals.

Data Labeling

Reality AI can facilitate crowdsourced labeling for all kinds of signal input, including image, sound, and others. Just provide us with examples and we can do the rest. 

Ask about our new solution for crowdsourced waveform labeling!

Evaluate Data and Determine Readiness

Spot consistency, quality and coverage issues that will impact your machine learning project before you start building models.

Coming very soon!

Develop your Product

Reality AI is available through a subscription service during your R&D phase that provides software-enabled access to our IP. Licenses are available for inclusion of classifiers, detectors, predictors created using Reality AI in products for resale.

Reality AI Tools™ is a cloud-based application for R&D engineers working with sensors and signals. It can be used to generate code for detecting real-world events and conditions using signal and sensor inputs.

Users can load or link to their sensor data, curate training and validation sample lists, use AI Explore™ to create optimized feature sets and generate machine learning models, then train and test those models in the field.

Reality AI Tools allows users to make trained classifiers, detectors and predictors available in the cloud, where they can be used with a simple API. Or, with a subscription upgrade, export them into a form where they can be integrated into firmware and run in real-time, at the edge, on your device.

Reality AI will work with any kind of sensor input. Customers have used a wide range of sensor and signal inputs, including accelerometry and vibrationsoundimage, LiDAR, 3D imageryelectrical signals and proprietary sensor types.

Reality AI Tools is generally more appropriate for non-image applications where the sensor data carries a sample rate of 25Hz or greater. For slower sample rates, traditional machine learning techniques geared for statistical time series are likely to give good results and should be tried first.

For image applications, Reality AI is generally most appropriate for problems related to identifying different surface textures and discontinuities in surface textures. For object identification and scene classification problems, a solution based on deep learning is more likely to give good results and should be tried first.

For sound applications, Reality AI is appropriate for a wide range of problems. However, we do not have solutions for natural language processing or speech recognition. Other tools will be more appropriate for those kinds of problems.

The amount of data needed depends on the amount of variation in target classes and in the environmental background. In many cases, we can get useful results with small datasets. For some use cases, even a few dozen examples are sufficient to get started.

Eventually, to ensure a solution that will perform adequately in the field, it will be necessary to collect data that covers the full range of variation expected both in target and in the background. For classification problems, that means examples of target classes occurring in as many different circumstances as possible, as well as counter-examples of non-target classes that could be confounded with targets.

To work with Reality AI Tools, data must be loaded or linked in one of our standard file formats. Please refer to our Standard File Format Guide.

Traditional signal data analysis is a “model-driven” approach based on the engineer’s understanding of the physics of the device, the physics of the sensor and a physical model of how target phenomena will be manifested in sensor output. It is typically an iterative, trial-and-error approach. Often, the engineer will use a fast-Fourier transform (FFT), filter banks, or a linear systems analysis to discover the amount of energy in the signal’s frequency and time domain and use these outputs with statistical methods.

Reality AI uses a “data-driven” approach that makes no assumptions about the underlying physics and instead employs advanced mathematics and machine learning to identify relevant features (which can be very different than frequency domain features found by an FFT), and then learn to classify on the basis of those features. Data-driven approaches to signal analysis are relatively new and can be a powerful complement to traditional model-driven approaches.

You can find more information in our Technical Whitepaper, available for download on our technology page.

Reality AI uses a proprietary method for generating features from sensor data that is grounded in the fundamental mathematics behind signal processing. This method creates optimized features that enable highly accurate detection/prediction in an extremely computationally efficient form. Reality AI Tools typically does not use neural networks at all (although it will for certain types of problems).

Deep Learning is an approach to machine learning that typically uses layers of convolutional neural networks to discover features and learn to accomplish a detection or prediction. It has been highly effective in many use cases, including object recognition in images and natural language processing in sound. But Deep Learning requires a great deal of training data, requires significant expertise to configure, and is not very computationally efficient -- deep learning solutions often require expensive, specialized hardware based on GPUs to run in the field.

Reality AI's approach, on the other hand, is usually much faster to configure, requires much less training data, and delivers results that are highly computationally efficient -- often fitting into inexpensive, commodity hardware when running at the edge.

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