Catastrophic forgetting (or catastrophic interference) is a long-standing problem with neural networks, the theoretical underpinnings for deep learning and other machine learning techniques. Here’s the problem: say you teach an AI app to recognize an apple, and then you show it a pear. It will forget how to recognize an apple.
Heretofore, the only solution to this catastrophic forgetting problem is to repeatedly show the application both an apple and a pear (as well as everything else you want it to recognize) – a time-consuming, data-intensive task.
Neurala has solved the catastrophic forgetting problem via careful analysis and modeling of human learning. The result: machine learning that requires far less compute power and data than traditional approaches, making it possible to put AI on small, inexpensive hardware platforms.
First out of the gate: AI-driven drones (both for consumer and professional use), toys, and interest from vehicle manufacturers because of the potential to support self-driving cars. The big win for Neurala, however, promises to be the Internet of Things (IoT), as it can drive AI into the edge devices that make up the IoT.
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