Unicist Artificial Intelligence


Unicist AI to Manage Adaptive Environments

Unicist AI is an advanced artificial intelligence approach that integrates fundamentals, logical rules, and conjunctive reasoning to manage complex adaptive systems and environments. It leverages the fundamentals of functionalist principles, the rules of unicist logic, and their integration through the conjunction ‘and’ to develop effective solutions.

The fundamentals of functionalist principles provide the foundational elements that define the purpose, active function, and energy conservation function of any system. These principles are essential for understanding the underlying structure and dynamics of complex adaptive systems.

 Unicist logic, which emulates the intelligence of nature, provides the logical framework to manage the functionality, dynamics, and evolution of these systems. It is based on a triadic structure defined by the unicist ontology, which includes a purpose, an active function, and an energy conservation function.

The integration of these elements through conjunctive reasoning allows Unicist AI to handle the unified field of the inherent functions of adaptive systems. This approach is non-dualistic and avoids exclusive disjunctions (‘or’), focusing instead on conjunctions (‘and’). This ensures a holistic understanding and management of complex systems, enabling more accurate and reliable decision-making.

The discovery of the ontogenetic intelligence of nature, that defines the roots of evolution, led to the conception of the Unicist Logic developed by Peter Belohlavek at The Unicist Research Institute.

The unicist logic integrates all logical approaches because on the one hand it is an abstract emulation of the intelligence of nature, and on the other hand, it provides a concrete description of the functionality, dynamics, and evolution of adaptive systems and environments.

The unicist logic is a double dialectical logic that made the development of the Unicist Evolutionary Approach, the Unicist Artificial Intelligence, and the Unicist Ontological Diagnostician possible.

Abstract

The Unicist AI is a fundamentals-based intelligence that uses the rules of the unicist logic, the structure of the unicist ontology to manage the functionality of the functionalist principles that underlie all adaptive systems and environments.

The use of Unicist AI also allows the integration of data-based AI and fundamentals-based AI to minimize subjective biases.

On the one hand, the categories defined by fundamentals-based AI provide the autonomous universes that are needed to use data-based AI.

On the other hand, data-based AI allows quantifying the specific structure of Unicist AI to establish the aspects of the categories and segments of entities to build solutions.

When the quantity of data does not suffice to develop data-based AI, the use of  destructive tests, followed by non-destructive tests provides the quantitative information to manage the categories and segments defined using Unicist AI.

The unicist AI emulates the human reasoning process, integrating abductive, inductive, and inductive reasoning. It allows apprehending the concepts of complex 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.

The unicist logic is an emulation of the ontogenetic intelligence of nature that drives the functionality and evolution of complex adaptive systems and environments. The unicist AI allows the emulation of the solutions of a complex adaptive system to build structural adaptive systems.

The reasoning process includes the emulation of the reflection process 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.

Dealing with Adaptive Systems and Solving the Biases of Data-based AI

Data-based AI is functional in systemic environments but tends to generate biased responses when it is applied to adaptive environments. To avoid biases, data-based AI needs to be complemented with unicist AI when dealing with adaptive environments. Adaptive environments are such because they are feedback-dependent and have open boundaries in which there is a consequent objective impossibility of the existence of observers.

Unicist Evolutionary Approach

Adaptive systems are composed of elements that have bi-univocal relationships and are integrated by conjunctions without the existence of exclusive disjunctions. This approach is managed using the unicist logic that allows dealing with adaptive environments. Typical application fields in business are marketing, strategy building, and root cause management in adaptive business environments.

It must be considered that data-based AI is managed using inductive and deductive approaches without considering the abductive approach that allows for dealing with the abstractions that are needed to manage systems that are evolving because they have open boundaries.

Therefore, the use of data-based AI in complex adaptive environments might generate biased responses because in these environments small changes may produce significant consequences (the butterfly effect) and big changes may produce no consequences. When the unicist logic and the consequent unicist ontology are used, the significance of changes becomes evident, and the consequences are predictable.

One must consider, that the basic schooling systems are based on teaching inductive reasoning and mainly deductive (analytical) reasoning, disregarding the use of the abductive reasoning approach (Charles S.. Peirce 1839-1914) to manage complex adaptive environments. https://en.wikipedia.org/wiki/Abductive_reasoning

Abductive reasoning had no logical structure; therefore, the learning process of abduction could not be guided. The unicist logic provided the logical structure of the functionality of the real world and therefore, established the logical structure of abductive reasoning and allowed including it in the artificial intelligence.

The unicist AI is part of the unicist evolutionary approach that was developed to develop prescriptive diagnoses in adaptive social, economic, business, and individual environments.

It allows complementing data-based AI to generate reliable knowledge for decision-making, where the subjective biases and the biases resulting from the adaptive processes of the environments have been eliminated or minimized.

About the Unicist Artificial Intelligence

The Unicist Artificial Intelligence Monitor is an intelligent interface that allows organizing adaptive systems and environments and manage the root causes of their functionality.

It is based on the use of the ontogenetic maps of the functions involved that have been researched. This monitor can work as a palliative 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 Monitor is driven by a Unicist Inference Engine that is based on the rules of the unicist logic that allows dealing with the evolution of adaptive systems and environments.

The Unicist AI Monitor 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 functions using the rules of the unicist logic that allow managing the dynamics and evolution of adaptive systems.

The monitor is an intelligent interface that defines the concepts and fundamentals of a function as objects and establishes their relationships and functionality. The monitor defines the purpose that needs to be achieved and establishes a pilot test system to learn from the feedback until the goal of the business function has been achieved.

The solutions are based on the ontogenetic maps that define the concepts and fundamentals the of functions involved. define the actions that are necessary to achieve the established goals.

Unicist AI allows for the emulation of the solutions of an adaptive system to build structural adaptive solutions. The use of the monitor requires managing the unicist strategic approach that is needed to deal with adaptive environments. This strategic approach emulates the intelligence of nature, to build maximal strategies to grow and minimum strategies to ensure results.

The main business applications are Strategy Building – Business Intelligence – Business Process Management – Root Cause Management – Market Laboratories – Conceptual Design – IT Architecture Design – People Management – Business Scenario Building – Future Scenario Building – Business Education.

Fundamentals-based AI and Data-based AI

The use of AI in adaptive environments requires integrating data-based AI with unicist AI to develop functional knowledge that integrates the know-how and the know-why of processes.

The unicist AI is a fundamentals-based AI that is based on the use of the ontogenetic maps of the concepts and fundamentals that drive the functionality of the process involved and the use of pilot tests to learn from the environment.

Fundamentals-based AI uses indicators and predictors to monitor the functionality of processes and as an input to the inference engine that allows defining the actions that are tested to confirm their functionality.

Data-based AI is complemented by fundamentals-based AI to avoid having subjective biases. When the quantity of data does not suffice, data-based AI is replaced using destructive and non-destructive testing that provides analogous information.

Unicist AI provides the necessary functional knowledge to manage the functionality and evolution of adaptive environments.

Unicist Artificial Intelligence in Adaptive Environments

Unicist AI allows the development of different types of functionalities according to what is needed.  There are basically 4 types of solutions that are homologous to human decision-making processes that are being emulated:

Descriptive function

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.

Diagnostics function

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 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.

Predictive function

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 to be reliable.

Prescriptive function

This function establishes the actions that allow achieving the goals established within the boundaries of actual possibilities. A typical application is the solution to complex problems in adaptive environments. It is based on developing homological inferences that allow integration of 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.

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 by the data-based AI approach in order to be reliable.

Unicist Artificial Intelligence emulates Human Intelligence

The purpose of conscious intelligence is the development of functional actions that allow adapting to an environment.

The use of conscious intelligence in complex adaptive environments requires using unicist reflection to apprehend the unified field of a system including its restricted and wide contexts.

This requires an action-reflection-action process to apprehend the functionality of the system considering that the individual is part of it.

Unicist AI emulates the reflection process of human intelligence requiring 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.

Learning begins when the functionality fails. The learning function drives the resilience of the system and expands its boundaries towards a better adaption to the environment.

This learning function uses the application of ontogenetic maps and the evolution rules to define and monitor the functionality of hypothetical actions, which are monitored through pilot testing that provides the learning of the system.

The unicist AI based learning allows building intelligent knowledge systems to manage and monitor complex adaptive environments.

The decision-making function of a UAI approach to reality allows making automated decisions that work as conscious decisions based on recycling through the learning function.

Artificial Intelligence:
An Integration of Analogical and Homological Approaches

The goal of human conscious intelligence is to deal with the root causes of things 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. This AI works based on the use of operational patterns. 

The Homological Approach

The unicist artificial intelligence approach is needed to manage homologies when the goal is to influence complex 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 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 a complex adaptive environment and measuring the consequences of these actions to learn from them.

The UAI 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. Unicist AI works with unicist ontological patterns to diagnose and decide, and with operational patterns to prescribe.

Unicist Artificial Intelligence: The Use of Predictors

Indicators define a state of things, while predictors define the possible evolution of the state of things. Predictors include indicators but not vice versa.

Predictors are signs that can be read to anticipate the future. They are ambiguous signs that require 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. The fundamentals of a specific reality are able to define a concept if there is a catalyst or a gravitational force that is influencing it.

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 complex 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 in order to react or exert influence to make things happen.

Diego Belohlavek

Unicist AI emulates human reasoning by integrating abductive (solution generation), inductive (solution testing), and deductive (rule application) reasoning processes. This comprehensive approach mirrors complex human cognitive processes more closely than traditional AI models, which may focus predominantly on either data-driven or rule-based approaches.

The Unicist Logic behind Unicist AI

Main Markets

• Automobile • Food • Mass consumption • Financial • Insurance • Sports and social institutions • Information Technology (IT) • High-Tech • Knowledge Businesses • Communications • Perishable goods • Mass media • Direct sales • Industrial commodities • Agribusiness • Healthcare • Pharmaceutical • Oil and Gas • Chemical • Paints • Fashion • Education • Services • Commerce and distribution • Mining • Timber • Apparel • Passenger transportation –land, sea and air • Tourism • Cargo transportation • Professional services • e-market • Entertainment and show-business • Advertising • Gastronomic • Hospitality • Credit card • Real estate • Fishing • Publishing • Industrial Equipment • Construction and Engineering • Bike, motorbike, scooter and moped • Sporting goods

Country Archetypes Developed

• Algeria • Argentina • Australia • Austria • Belarus • Belgium • Bolivia • Brazil • Cambodia • Canada • Chile • China • Colombia • Costa Rica • Croatia • Cuba • Czech Republic • Denmark • Ecuador • Egypt • Finland • France • Georgia • Germany • Honduras • Hungary • India • Iran • Iraq • Ireland • Israel • Italy • Japan • Jordan • Libya • Malaysia • Mexico • Morocco • Netherlands • New Zealand • Nicaragua • Norway • Pakistan • Panama • Paraguay • Peru • Philippines • Poland • Portugal • Romania • Russia • Saudi Arabia • Serbia • Singapore • Slovakia • South Africa • Spain • Sweden • Switzerland • Syria • Thailand • Tunisia • Turkey • Ukraine • United Arab Emirates • United Kingdom • United States • Uruguay • Venezuela • Vietnam