Harnessing the Power of True Artificial Intelligence (TAI)
Most of BigTech’s AI applications are based on 1990's technology called neural networks, and because they are built with state devices [see the definition of state devices], they cannot demonstrate this astronomical power of geometric learning that biological neurons and the Massively Asynchronous Machine can demonstrate.
The learning behavior of their artificial neural networks is a process of “gradient descent”, conducted through what is called a back-propagation cycle.
Through the iterations of the back-propagation cycle, all of the elements in an artificial neural network move an “error target” towards a smaller and smaller value, a process of ever-decreasing learning for each cycle. In the end, just a shinier penny.
From the standpoint of artificial intelligence, it's like cutting your lumber before the house blueprints are even drawn up.
Contrast this with the geometrically increasing learning as biological neurons and MAM units conduct their reciprocal cycles, starting off with a small number of neurons participating in the abstraction process, with each cycle adding a geometrically increasing population to the learning. Pennies to riches.
