Tom Veatch

Flames of inspiration often leave smoke signals behind.
From mine, these.

Artificial (and natural) Intelligence

Foundations of Future AI

Towards a New Cognitive Science

Human Evolution

Logic constrains biology, biology constrains us, and yet we think and speak and feel. How did we get here, then, and what parts came in what order? This was a lot of fun to write. Includes Tautology, and Cognitive Biology, and Conceptual Archeology.

N+V Humor Theory

Veatch's 1998 "A Theory of Humor"

Gesture Learning

How to learn effective physical movement.
A research program for reinforcement learning by embodied systems, like people.

Math as Language

Underlying intuition reads out as discrete expression.

Robot Emotion

On the design of emotional systems for humans and robots. Motivational frames and their relative priorities. Metrics to guide reinforcement learning.

Evolutionary/Functional/Logical Decomposition

of the elements of Language

Synthetic Perception

Examples include image merger, color qualia, rhythm, object coherence, and stereoscopic movement as object learning.

Neural Networks + Fuzzy Logic + Space

A careful, accessible introduction to neural networks assuming only high school algebra and a little geometry and differentiation. NNs are defined mathematically, along with how to run them, how to train them (by the usual gradient descent), how to train them better (so I suppose: using 'Newton-Raphson', which really ought to kill!). I also discuss how to understand the training algorithm's implicit reasoning about the adjustments it decides to make; I share an interpretation that backpropagation is like an Anti-Dunning-Kruger learning system (and therefore morally superior to most men?). Then I give a whole Fuzzy Logic re-interpretation of NNs, along with suggestions on how to enhance their logical reasoning capabilities. I tried the wikipedia page, and got so frustrated I wrote my own introduction. So yes, I suggest reading this if you want to really understand neural networks, and if your other resources have made it seem inscrutable. It's a few pages of actual math, yes, but all the steps are laid out: no leaps! It's not short, but you don't have to be a math major to follow along. I encourage your study here if you are interested in really knowing how neural nets work.

Also this adds Fuzzy Logic to neural networks, including how to train them. Finally this goes into Space Representing Neural Networks so robots can represent space, or humans' representation of space can be understood better. Three months of work is in here.

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Copyright © 2000-2021, Thomas C. Veatch. All rights reserved.
Modified: 12/20/2021