Thanks Higgs.The AI Time Journal article is well worth revisiting, but I can't see the bit you quoted, although I'm sure I've seen something along those lines somewhere.
Interview with Peter Van Der Made, Founder and CTO at BrainChipDecember 26, 2019 - by Jagan G. M.
https://www.aitimejournal.com/@jagan/interview-with-peter-van-der-made-founder-and-cto-at-brainchip
Some nice quotes:
A similar system, but at significantly lower power consumption, could be built around the Akida chip within a camera. Akida goes further than that; It can be trained either in the traditional way through Machine Learning, and it can also learn on-chip through either one-shot or few-shot learning. Combinations of those two methods can be used within the same chip, creating endless possibilities for learning to recognize specific individuals or generalize and recognize many different people or objects, with incremental learning after training.
...
It was stated that training one Deep Learning CNN uses enough power to run five electric cars for a lifetime. With Akida, that power requirement is reduced to the power needed to run a flashlight. Event-based processing is a ‘green’ technology.
...
The Akida chip technology is great at rapid learning of threat patterns in data streams. At the moment these patterns are learned through Deep Learning, which is expensive and slow. The rapid learning method of Akida shines in these applications; we trained the Akida technology on the challenging and large 220 Gb CSE-IDS-2018 cyber-security dataset in just 2.2 hours, and it learned to recognize all 15 attack types. With these rapid learning capabilities it is quick and easy to add new threat patterns ...
Future versions of the Akida technology will incorporate more of the structure of the brain, with the aim to make AI more intelligent ...
We have accomplished everything that Deep Learning has to offer, using a very different philosophy, a philosophy that has a clear path to the future. Where standard CNN technology runs into a brick wall when they try to go beyond image classification, our event-based Spiking NN excels ...
Akida can learn from the environment in which it exists, an ability that we will expand in future generations of Akida with episodic memory, that is to remember sequences of events.
This is exciting technology.
(20min delay)