Home of the NeuroMem technology

Trainable, Responsible and Explainable AI
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AI for your live data streams and stored datasets

The NeuroMem technology allows the design of AI engine with real-time lifelong learning capabilities and a range of applications from cognitive sensors to cognitive storage.

NeuroMem-powered Smart Sensors

Power your sensor hubs to learn and make decisions locally. Whether on an industrial machinery, under the hood of a vehicle, or around a wrist, the ability to learn and classify patterns coming directly from a sensor is game-changing. 

NeuroMem -powered Smart Storage

Turn your storage devices into local, secure and configurable engines to analyze your stored data. Whether it is a text, audio, image or video file, you can comprehend its content as a single item or as part of a larger dataset. 

Inspect

Pass/Fail, grade quality, locate defects, classify anomalies

Find

Locate people, measure cell density, detect obstacles

Track

Track vehicles, people, cells under a microscope

Match

Semiconductor inspection, QC of printed materials

Trainable lifelong AI

The NeuroMem neurons are both a learning and inference engine and they perform both tasks with fixed latencies  independent of the number of neurons already in use. You can learn a new example at anytime, and this example can describe a new category if you wish. It will be taken into account immediately and contribute to the next recognition. If by mistake, you (or an unsupervised algorithm) teach an example contradicting prior learning, the neurons in conflict will autonomously flag themselves as such and correct their influence field. Neurons can also be pre-trained and this means that they are loaded with a knowledge file previously built by a NeuroMem network.

Responsible AI

The NeuroMem neurons are responsible because they are not ashamed to output when they do not know or are unsure about a classification. In applications with cost of the mistake, such behavior is much preferred than probabilities or the famous Top3 or Top5 criteria used in Deep Learning benchmarks. Acknowledgment of uncertainties enriches the decision process by suggesting more training of the neural network or the recourse to a second opinion such as another network trained with a different feature. For the best robustess, the final decision may require minimum consensus between neurons of a same or multiple networks.

Explainable AI

The NeuroMem neurons are not a black box. When they learn a new pattern, the decision is actually taken collectively between all the neurons already committed. The next available neuron in the chain will store the new pattern in its own memory and become committed. Consequently, the content of the neurons not only represent a knowledge that they have built together, but also the log of all the patterns that have been retained as novelty at the time they were taught.