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Based on the Introduction Presentation on Matrix-Q A.I. Nonary Intelligence by the author, Luis Daniel Maldonado Fonken, complementary relevant research publications are included in this collection with focus on A.I.

[Matrix-Q A.I. Nonary Intelligence]

Blind spots of A.I. Design : Futurist Prediction on Machine Learning, from binary to nonary thinking

[Perception and Training digital platform on Nonary Thinking and Nonary Intelligence by the Matrix-Q Research Institute]


The researcher review through a philosophical and futurist assessment A.I. Machine Learning, validation of data, binary thinking, nonary thinking, matrix thinking and Matrix-Q Intelligence.

Through examples on data generation and validation in relation to human phenomena, suggests the need of a new approach for machine learning and technology innovation, thanks for scientific research on human intelligence, human potential, stages of development and human species evolution.

The researcher describes new form of algorithms being developed at the Matrix-Q Research Institute for machine learning and A.I. design, based on nonary thinking, matrix-Q Intelligence, which are as well nature inspired, giving as example the algorithms being developed thanks to the discovery of a pattern of 36 cyphers generated by the Fibonacci sequence of numbers.

Through this presentation, article, the researcher introduces the reader to the following conclusions:

There is a stream of thought made available by nonary thinking that can unveil the blind spots of the trendy A.I. design of our modern culture tech. innovation.

It is necessary a futurist assessment of impact technology (and the source of the algorithms modern technology utilizes), will create in human culture, civilization and human evolution.

Studies on human evolution, human potential, education methods, human creativity, skills, leadership, effectiveness would become extremely more important for science, technology innovation and human society, culture.

Data validation methods, or algorithms for the identification or corrupted or not valid data, will become more important for A.I. in a very close future.

Understanding of the influence on perception has binary thinking may change the way technology and knowledge is being created and the possibilities for development that both of them make available for their users.

The Matrix-Q Research Institute is aiming for a new possibility for machine learning, deep learning algorithms creation based on nonary thinking and Matrix-Q Intelligence