QAI Agent Message Diffusion


Decades ago, through the study of the DoD Joint Tactical Information Distribution System (JTIDS), Lars Wood realized that it was possible to reproduce the Young two slit quantum interference experiment algorithmically.

JTIDS is a message distribution system that disseminates and  self-prioritizes messages in a tactical theatre of operation fundamentally taking advantage of propagation of quantum electrodynamic energy, which is rate limited by the speed of light. The result is that tactical assets not only understand evolving threat scenarios, but also self-organize so as to create optimal clusters of assets, which results in domination of the tactical battlefield. Lars Wood together with Eric Ellingson wrote the JTIDS article on Wikipedia.

This resulted in the QAI quantum signaling system deployed today and discussed below. QAI quantum signaling is extremely accurate as shown above as it replicates the quantum electrodynamics that result in the subatomic formation of the hydrogen molecular ion. The hydrogen molecule ion is the only molecule that the Schrödinger equation solves exactly. Quantum signaling implements deep learning network formation. The QAI cognitive reactor neurons spontaneously self-organize to dynamically solve problems.

Patent pending QAI signaling message diffusion is the basis for QAI deep learning network dynamic formation. The way it works is that when a quantum neuron broadcasts a message (using the QAI interface language) the message (which can be another neuron or data) spreads as a wavefront in the form of a four dimensional expanding bubble (3 dimensions plus time). Message receipt by other agents (including the neuron that sent the message if message reflection is enabled) is governed by an algorithm derived from quantum statistics based on Young's two slit-experiment.

QAI Agent Message Diffusion

The way this works is as follows: If a message is sent from neuron "b"the message spreads as a 4D wavefront expanding bubble. If there is no other neuron able to receive the message the wavefront bubble expands and then dissipates. If a single neuron is in the path of the expanding wavefront message bubble it receives the message and the bubble collapses (it pops) making the message not available to any other neuron. If there are two ("a" and "c") agents in the path of the wavefront message bubble sent by neuron "b" and they are equal distant from the transmitting neuron (in this case neuron "b"), both agents "a" and "c" receive the message upon message wavefront bubble collapse. If neuron "a" is closer to neuron "b", (the transmitter), than neuron "c" (i.e. neuron "c" is further away from the transmitter neuron "b" than neuron "a"), then neuron "a" receives the message upon message wavefront collapse resulting on neuron "c" not receiving the message. The only way that neuron "c" in this scenario can receive the message from neuron "b" is if neuron "a" is not available to receive the message due to neuron "a" not being an observable. In this case the message wavefront bubble spreads undeterred through and beyond neuron "a" and propagates to neuron "c" where it is received and the message wavefront bubble collapses.

Quantum agents have mass and have inter and intra QAI cognitive reactor movement. The mass of a quantum neuron is proportional to its velocity and acceleration, i.e. quantum agents with more mass move and accelerate more slowly but also tend to take more time to slow down. Quantum agents with more mass require more message wavefront bubble interactions to move than do trans agents with less mass. This is observed in the three trans neuron system in the video.

In the video there are three quantum agents "a", "b" and "c". Quantum neuron "a" is red, trans neuron "b" is green and trans neuron "c" is blue. Trans agents "a" and "c" have more mass than trans neuron "b". Consequently trans neuron "b" moves faster than either of the two other agents. The visual effects relate to message wavefront bubble emission, reception and collapse. The resulting trans neuron interactions are described by mass curvature, similar to how planets stay in orbit around a star. In the above video screen capture observe that the three agents are trapped in a dance with each other as a result of their message interactions. Neuron "b" cannot leave as it is trapped by agents "a" and "b". In this case the three agents form an orbital together as they move collectively within QAI. When the agents darken they are not able to receive a message and the wavefront bubble spreads beyond them until it collapses as a result of interacting with another neuron or it ultimately dissipates and received by no neuron. If the message wavefront bubble passes through a trans neuron without collapsing then the trans neuron can never see that message unless it is broadcast again or reflected back by another trans neuron (see the interface language specification). This is the basis for deep learning networks in QAI Collective Artificial Intelligence. The video showing message diffusion greatly reduced in speed so that the interactions are visible as the orbitals are traced out over time.