"The problem was even the most basic neural networks were very...

"The problem was even the most basic neural networks were very computationally intensive, it just wasn’t a practical approach. Still, a small heretical research group led by Geoffrey Hinton at the University of Toronto kept at it, finally parallelizing the algorithms for supercomputers to run and proving the concept, but it wasn’t until GPUs were deployed in the effort that the promise was realized.

If we go back again to our stop sign example, chances are very good that as the network is getting tuned or “trained” it’s coming up with wrong answers — a lot. What it needs is training. It needs to see hundreds of thousands, even millions of images, until the weightings of the neuron inputs are tuned so precisely that it gets the answer right practically every time — fog or no fog, sun or rain. It’s at that point that the neural network has taught itself what a stop sign looks like; or your mother’s face in the case of Facebook; or a cat, which is what Andrew Ng did in 2012 at Google.

Ng’s breakthrough was to take these neural networks, and essentially make them huge, increase the layers and the neurons, and then run massive amounts of data through the system to train it. In Ng’s case it was images from 10 million YouTube videos. Ng put the “deep” in deep learning, which describes all the layers in these neural networks."



Consider the above against 'one shot learning and incremental learning on chip' and what many on here have done under the guidance or nudging of @uiux without a team of data scientists and engineers assisting. The gap between what has been achieved by Brainchip and the world is beyond the understanding of many and as the so called informed have admitted "what's a neuromorphic chip anyway". The answer is of course the future of Ai and computing at the edge and far edge.

My opinion only DYOR
FF

AKIDA BALLISTA