Picture shows Albert Einstein, Joseph Louis Lagrange and Richard P. Feynman

Generic AI

Generic AI applies the deepest principle in physics to understanding the laws of the virtual world
In 1942 Richard P. Feynman received a PhD from Princeton, under his advisor John Wheeler, with a thesis in which he developed an approach to quantum mechanics governed by the principle of “least action”
Einstein, Lagrange, Feynman


The theoretical basis for Generic AI has been developed over 25 years. The essence of the theory is the application of the Principle of Least Action to Information Theory. The Principle of Least Action is a well established mathematical concept employed by leading scientists such as the Nobel Laureates A. Einstein and R. P. Feynman


Theoretical Background

Richard Feynman and Albert Einstein both employed the Principle of Least Action as a fundamental component of their work. Einstein called it a Geodesic in Relativity theory; Feynman used it as the basis for the Path Integral Formulation of Quantum Mechanics and the theory of Quantum Electrodynamics


Brief History: -

Fermat's Principle of Least Time

In 1662 Pierre de Fermat described the principle that the path taken between two points by a ray of light is the path that can be traversed in the least time

Maupertuis' Principle of Least Action

In 1744 Pierre Louis Maupertuis extended Fermat's Principle of Least Time to formulate an integral equation that determines the path followed by a physical system. This is now known as Maupertuis' Principle. This principle is a special case of the more generally stated Principle of Least Action

Lagrangian Mechanics

In 1788 Joseph Louis Lagrange developed a re-formulation of classical mechanics that combines the conservation of momentum with the conservation of energy, now known as Lagrangian mechanics. Lagrange showed that solving his equations is equivalent to finding the path for which the 'action' function (the integral of the Lagrangian over time) is stationary

Hamilton's Principle

In 1833 Irish mathematician William Rowan Hamilton extended Lagrangian mechanics to create an alternative formulation of classical mechanics known as Hamiltonian mechanics by refining Maupertuis' Principle. The 'Hamiltonian' (obtained by performing a Legendre transformation on the Lagrangian) is extensively used in quantum mechanics. Hamilton's formulation of the principle of 'stationary action' states that the dynamics of a physical system are determined by a function based on the Lagrangian which contains all physical information concerning the system and the forces acting on it.

Feynman's Path Integral Formulation of the Principle of Least Action

In 1948 Feynman invented the Path Integral Formulation of Quantum Mechanics, extending the Principle of Least Action to quantum mechanics for electrons and photons. In Feynman's formulation, particles travel every possible path between the initial and final states; the probability of a specific final state is obtained by summing over all possible trajectories leading to it. The path integral formulation reproduces Hamilton's Principle, and Fermat's Principle in optics.


The 'Least Action' in Information Theory

A number of principles related to the Principle of Least Action have been formulated, such as Gauss' principle of least constraint, Hertz's principle of least curvature and the principle of maximum entropy.

The extension of the Principle of Least Action and these related principles to Information Theory forms the basis for Generic AI. The solution is related to the apparent teleological character of the Principle of Least Action and the relationship between Occam's Razor and the concept of Information Entropy. Attributes of the Wave aspects of the Principle of Least Action are extended to information.

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