Could Dream Symbol Datasets Recorded from Human Dream Aberrations Help Drive Predictive Modeling for Sentient Intent?

Your alarm sounds, and after waking up from deep sleep you feverishly blog several symbols from your dream. In this case the dream symbols were: flying, trees, moon, and rabbits. Upon pressing snooze, you enter back into even a deeper REM state of sleep and this time you have a completely new dream with different associated dream symbols. Now your dream symbols are: tornado, barn, mouse, and thunder. They all sound random, but perhaps there is a pattern.

Those who are deeply seeded into A.I. technology are familiar with Natural language processing which is concerned with the interactions between computers and human language and how to process and analyze large amounts of data. This could lead into a new subfield of A.I., that includes the study of relationships with dream blogging and its interpretative data intersections between language and symbols.

To take it one step further, could we use this summation of data to supply a type of learning, sentient, pseudo-awareness and have these systems observe their other community agents while they themselves are seemingly un-active or in sleep mode. Could a agent arrive at intuition via machine learning upon reviewing the iterations and actions of their other A.I. counterparts in simulation environments? The verdict isn’t quite conclusive that a sentient may be able to have intent, but as A.I. technologies move forward the possibilities may become clearer.

November 13th, 2020
Artificial Intelligence, General, Machine Learning
| Author: Justin Crawford