Unicist AI is a fundamentals-based AI that emulates human thinking processes to manage adaptive systems and environments. 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.
Unicist AI 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 descriptive function produces reliable results when the fundamentals (why) of the buying processes are known and there is a knowledge of the objective of the process (what for).
It implies the integration of the descriptive function with the prescriptive and predictive functions that are driven by the fundamentals of the processes. This data-based AI approach is integrated with the fundamentals-based AI approach in order to be reliable.
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 diagnosis of internal or external human/social problems of an organization.
This diagnostics function produces reliable diagnoses when the possible objectives of the processes are known (what for) and there are alternative solutions (why) available that depend on the results of the diagnoses.
It implies the integration of the diagnostics function with the predictive and prescriptive functions that are driven by the fundamentals of what is possible to be achieved.
This data-based AI approach is integrated with the fundamentals-based AI approach in order to be reliable.
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.
The predictive function generates forecasts within the possibilities that can be achieved.
It requires being integrated with the diagnostics (what) function and with the descriptive function that are based on the available data managed using analogical inferences. This fundamentals-based AI approach is integrated with the data-based AI approach in order to be reliable.
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 necessary to access the fundamentals knowledge bank to find the solution.
It uses an abductive approach to define the hypothetical objects to be used, an inductive approach to monitor the pilot test of the solutions and a deductive approach to validate that the objects provide a structural solution.
It requires being integrated with the descriptive function (How) to confirm the functionality and with the diagnostics (what) to confirm that the problems that have been diagnosed have been solved. This fundamentals-based approach is integrated with the data-based AI approach in order to be reliable.