There is a natural evolution from the ideas that deep learning #deeplearning has empirical revealed to a theory of general intelligence.
A common criticism of deep learning is its lack of good theory. Deep learning is like the supercolliders in high energy physics. It reveals the inner behavior of an artificial intuitive process. It reveals to us patterns of what does work.
To build up that theory we must walk back into the ideas of past thinkers. Thinkers who have never seen the empirical evidence. What will they conclude about their ideas if they had been exposed to evidence in deep learning?
In this book, I go back into the past to see how it informs the future. How far in the past do I need to go to find an informative theory?
Perhaps I could go back enough to the precursors of AI. Back to the 1940s before computers where invented. Back to exploring Nobert Wiener's cybernetics. After all, Deep Learning is a modern rendition of cybernetics.
But there is a problem here, it is relate to the reason why Wiener was disinvited from the infamous Dartmouth conference that coined the term 'Artificial Intelligence'.
In the 1950s with the emergence of computers, there was a belief that artificial LOGICal system was the key to human intelligence. After all, isn't logic what separates us from the brutes?
This unfortunately led to decades of exploration in Artificial Logic that never achieved any semblance of general intelligence. But it was the discovery of Artificial Intuition (Deep Learning) that finally revealed a path to general intelligence.
The problem however of Wiener and is a problem also inherited by Deep Learning is that it treats cognition as a dynamical system. It employs all the tools that we have inherited from centuries of doing physics.
We have today two competing ideas about how the brain works, one is based on formulas describing dynamical systems, both cybernetics and deep learning share this commonality. We also have a discrete computational kind that originates from the 1950s conference on AI (GOFAI).
Furthermore, we have what is known as the 'symbolic grounding' problem or the semantic gap. How does language which is discrete in nature achieve semantic grounding? How can we merge the connectionist and symbolists worlds?
We know intuitively that the solution for semantic grounding is related to the solution of general intelligence. But again, what thinkers of the past have thought of this problem?
Apparently, there was one thinker, an American thinker who died impoverished in the early 1900s. A thinker who barely published any of his works but left behind an outstanding amount of theory.
This American thinker in fact actually conceived of the universal gates used in computers today. His ideas on this were never noticed, until the ideas were reinvented to create computers in 1950s.
The same American thinker was credited by Heisenberg himself for the idea of the uncertainty principle in Quantum Mechanics.
How could it be possible for someone in the 1900s to have formulated the idea of universal logic gates and inspired the idea of the uncertainty principle and have not been noticed by almost everyone?
This is a book that begins with his ideas and the subsequent refinement of his ideas from the likes of David Bohm and Christopher Alexander. This is a book about how general intelligence arises from a simple idea.
That idea that Christopher Alexander expresses best in his book Nature of Order. A series of books that took 25 years in the making that was completed in the early 2000s.
Thus we have come full circle, a theory of general intelligence is based on a theory of nature. See the book in its construction here: gum.co/empathy
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