1000X More Efficient Neural Networks: Building an Artificial Brain With 86 Billion Physical (but Not Biological) Neurons

What if in our attempt to build artificial intelligence we don’t simulate neurons in code and mimic neural networks in Python, but instead build actual physical neurons connected by physical synapses in ways very similar to our own biological brains? And in so doing create neural networks that are 1000X more energy efficient than existing AI frameworks?

That’s precisely what UF startup Rain Neuromorphics, is trying to do: build a non-biological yet very human-style artificial brain.

Which at one and the same time uses much less energy and is much faster at learning than existing AI projects. And that learns, in short, kind of like we meatspace humans do. Plus, that is built with analog chips, not digital.

Rain Neuromorphics is a graduate of UF Innovate┃The Hub

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