Unicist AI Emulates Human Intelligence
The unicist AI emulates the human thinking process to apprehend the concepts of adaptive systems and environments and make functional decisions. It uses the rules of the unicist logic and allows developing solutions and learning from the pilot tests of their implementation until their functionality has been confirmed.
The unicist logic is an emulation of the ontogenetic intelligence of nature that drives the functionality and evolution of adaptive systems and environments. The unicist artificial intelligence allows emulating the solutions of adaptive systems to build structural adaptive solutions..
The emulation of human intelligence requires two functions to make this possible: the learning function and the decision function.
- The learning function allows confirming the functionality of actions based on the feedback of pilot tests.
- The decision-making function of a Unicist AI approach to reality, allows making automated decisions that work as conscious decisions based on the recycling through the learning function..
The Unicist Logic emulates the structure of Intelligence
Human intelligence works with the triadic structure of the intelligence that underlies nature, that has been synthesized in the unicist logic. The following description of human intelligence helps to understand the basics behind the unicist logic.
The adaption process of humans is sustained by the ontointelligence that drives the intentions of individuals, the reactive intelligence that drives the reactions of individuals to the stimuli of the environment and the active intelligence that drives the functionality of the actions necessary to adapt.
The process of intelligent conscious behavior begins with the definition of the intentions of the individual that are defined by the ethical intelligence. Unconscious intentions or automatisms cannot drive conscious behavior.
The reactive intelligence is driven by the emotional intelligence that defines and controls the purpose of the reactive behavior that is materialized in rational actions driven by the IQ. The entropy inhibitor of the reactive intelligence is the capacity to overcome frustration, named “speed of resilience”, that defines the timing of the actions that drive the success of the use of this intelligence.
Active intelligence is managed by the concepts individuals have that drive their actions. This intelligence is transformed into functional actions by the necessary functional intelligence while the entropy inhibitor is given by the intra-personal intelligence that allows emulating the external reality in mind.
The reactive intelligence and the active intelligence are integrated by the ontointelligence that defines the roots of intelligent adaptive behaviors.
About the Unicist Artificial Intelligence
The Unicist AI allows organizing adaptive systems and environments and managing the root drivers of their functionality. It is based on the use of the ontogenetic maps of business functions that have been researched, which define the concepts and fundamentals of such functions. The Unicist AI can work as an artificial substitute for the mental emulation of processes when dealing with adaptive environments. It is also a facilitator of the mental emulation of processes when human management is needed.
The Unicist AI is based on an inference engine that uses the rules of the unicist logic that allow dealing with the evolution of adaptive systems and environments.
As it was mentioned, the unicist logic is an emulation of the ontogenetic intelligence of nature that drives the functionality and evolution of adaptive systems and environments.
The Unicist AI emulates the human reflection process to apprehend the concepts of adaptive systems and environments.
The Unicist AI allows developing solutions and learning from the pilot tests of their implementation until their functionality has been confirmed. Its intelligence allows emulating solutions based on the unicist ontological structure of business functions using the rules of the unicist logic that allow managing the dynamics and evolution of complex adaptive systems and environments.
Analogical and Homological AI
The goal of human conscious intelligence is to deal with the root causes of problems to adapt to the environment. Conscious intelligence has two possible approaches: an analogical approach or a homological approach.
The Analogical Approach
The analogical approach to reality deals with the observable facts and actions that are the consequences of the underlying concepts and fundamentals that define the root causes of their functionality.
The artificial intelligence approaches that deal with observable facts and actions can only learn empirical knowledge and make analogical decisions. This is an emulation of the human reactive intelligence approach that is functional when the goal is to develop operational reactions.
The Homological Approach
The unicist AI approach is needed to manage homologies when the goal is to influence adaptive environments. Two entities are homologous when they share the underlying concept.
This homological approach deals with the concepts of things, which define the ontogenetic maps of the unified fields of adaptive environments and establish the rules of their dynamics and evolution. The unicist homological approach includes the analogical approach but not vice versa.
The unicist artificial intelligence is based on using a homological approach that allows defining the necessary actions to influence adaptive environments and measure the consequences of these actions to learn from them.
The unicist AI approach includes both learning and deciding actions based on a homological approach that deals with the essential concepts of functions that have been transformed into operational solutions.
The feedback from the environment defines the functionality of actions or drives the learning of the system until the results become functional.
The Use of Predictors
Predictors are signs that can be read to anticipate the future. They are ambiguous signs that are required to be read considering the conditions of the restricted and wide contexts. Predictors are observable events that make the fundamentals of specific aspects of reality observable.
Everyone uses predictors to interpret actions. For example, a smile is a predictor of what can be expected. Non-verbal communication necessarily includes the observation of “predicting signs” in order to act or react.
The rational use of predictors requires being aware of the structure of fundamentals that rule a given reality and the external forces of the restricted and wide contexts that influence it.
It is necessary to use predictors to deal with the adaptive aspects of reality. The unicist algorithms and the unicist ontogenetic maps provide the structure of predictors to be observed and measured to anticipate the future to react or exert influence to make things happen.
The Functionality of Unicist AI
Unicist Artificial Intelligence allows developing different types of functionalities according to what is needed. There are basically 4 types of solutions that are homologous to the human decision-making processes that are being emulated:
- Descriptive function
Driven by “how” things work
- Diagnostics function
Driven by “what” is being done
- Predictive function
Driven by the “what for” of actions
- Prescriptive function
Driven by “why” things work
This function describes the knowledge that has been inferred from data, using an analogical inference model based on the inductive approach used by data-based AI. A typical application of this is the use of neural networks to define the segments of buyers of products or services.
This function defines the diagnostics of what is happening based on the use of analogical inferences of data, benchmarks and experiences. It is based on the inductive-deductive approach used by data-based AI. A typical application is the diagnostics of internal or external human/social problems of an organization.
This function establishes the possible evolution based on the functionality of what is being done based on fundamental knowledge and the use of homological inferences. It is based on an abductive process that defines the hypotheses, an inductive approach to validate their functionality and a deductive approach to transform these hypotheses into possible solutions. A typical use is its application in business strategy building.
This function establishes the actions that allow achieving the goals established within the boundaries of actual possibilities. A typical application is the solution of complex problems in adaptive environments. It is based on developing homological inferences that allow integrating the functions that need to be established with the objects that provide the solutions. To do so it is needed to access the fundamental knowledge bank to find the solution.
The discovery of the triadic functionality of intelligence, that is based on the triadic functionality of the ontogenetic intelligence of nature, introduces a structural shift in the understanding of human adaptive behavior and drove to the development of artificial intelligence based on the functionality of things.
The research on human intelligence began in 1976 at The Unicist Research Institute and was led by Peter Belohlavek. The final goal of the research was to find the functionality of the intelligence that allows people to adapt to an environment, develop strategies to generate value and find how the evolution of intelligence can be stimulated. This research drove to the development of the unicist logic, that allowed managing the functionality of processes, and made the development of the Unicist AI possible.