Artificial Intelligence Notes

Neuromorphic

1. Start with a neural net with N Inputs, NxN hidden layers and N outputs
   1. Choose perceptron behavior
2. Assign weights and biases stochastically [0.0, 1.0]
3. Feed inputs [0.0, 1.0]
4. Observe output [0.0, 1.0]
5. Learn (Gradient descent)

Thoughts

• What is curiosity? A strong desire to know or learn something.
• What is knowledge? Facts, information, and skills acquired through experience or education; the theoretical or practical understanding of a subject.
• What is learning? The acquisition of knowledge or skills through study, experience, or being taught.
• What is imagination? The faculty or action of forming new ideas, or images or concepts of external objects not present to the senses.
• What is creativity? The use of imagination or original ideas to create something.
• What is intuition? The ability to acquire knowledge without proof, evidence, or conscious reasoning, or without understanding how the knowledge was acquired.
• What is science? The intellectual and practical activity encompassing the systematic study of the structure and behavior of the physical and natural world through observation and experiment.
• What is philosophy? The study of the fundamental nature of knowledge, reality, and existence, especially when considered as an academic discipline.
• What is a pattern? A discernible coherent system based on the intended interrelationship of component parts.

• Are there different form of creativity? Artistic and scientific creativities
• What is discovery and invention?
• Evolution and adaptation haphazard learning method
• A system that accumulates knowledge
• Learn by analogy, pattern recognition

GAI (General Artificial Intelligence)

Is there a fundamental pattern to learning? Is there a fundamental pattern to discovery?

One thing is to know another is being able to do it.

Artificial Intelligence

Machine Learning

Deep Learning

Philosophical consequences

Endowing a machine of intuition sets the stage for geometric evolution

Force Field Computing

1. Imagine a system of particles described by N properties p(x1, x2, … xN)
2. Imagine a space subject to M force fields each acting on properties Fm()
3. Shake (vibrate) the system
4. The individual particles will eventually sattle in a pattern