Managing Complex Adaptive Environments using AI
Unicist artificial intelligence was researched and developed to manage complex adaptive systems and environments dealing with the root causes and root drivers of their functionality. It is a core tool when dealing with the concept of “Industry 4.0” applied to businesses. Complex adaptive environments or systems have open boundaries, which hinder the existence of observers because they become part of the system.
Machine learning is dependent on the quality of learning data sets, that is why it is subject to cognitive bias. The Unicist Artificial Intelligence has been developed to deal with complex adaptive systems, but it also avoids a bias to creep into Artificial Intelligence based on fallacious data sets. The use of the ontogenetic maps of social, economic or business functions avoids the bias and ensures the quality of learning and decision-making processes.
The use of unicist artificial intelligence empowers the management of systems with open boundaries by making the intelligent function become part of the systems while individuals manage the feedback working as observers. This is what has been named “deep supervised learning”.
Humans cannot fly as birds”. But humans learned functional flying when “lift” could be separated from “propulsion”. Dualization allowed humans to fly.
Humans have a great difficulty to apprehend and deal with reality when it is complex and adaptive, because they are part of it and cannot observe it. The management of complex adaptive systems became possible due to the dualization of the management of the ontogenetic maps of reality and the feedback obtained through pilot testing in the real world.
While the management of the ontogenetic maps allows understanding the underlying concepts, the application process of the pilot tests allows learning from the feedback until the actions become fully functional.
The Basics Behind Unicist Artificial Intelligence
Unicist AI allows monitoring adaptive solutions by using the unicist logic that emulates the intelligence that underlies nature. It provides a tool for root cause management, unicist business strategy building and conceptual management.
When dealing with big data, it is complemented with neural networks to develop reliable big data analytics.
The unicist artificial intelligence allows developing monitors to manage business intelligence, business strategy and marketing and designing business functions and business objects. It also allows managing the root causes of business processes and emulating and supporting the development of solutions in the mind of deciders.
The unicist artificial intelligence allows building monitors to emulate and develop adaptive systems in social, economic and business environments.
Unicist Artificial Intelligence: An Emulation of the Intelligence of Nature
Unicist Artificial Intelligence is based on the ontogenetic maps of the functions of the adaptive systems or environments that are being managed. These ontogenetic maps describe the underlying concepts and fundamentals that define the root causes of the functionality of an adaptive system. They are an emulation of the triadic ontogenetic intelligence of nature and define the structure of the unicist artificial intelligence.
In the human brain, the knowledge of concepts and fundamentals is stored in the episodic, procedural and semantic long-term memory and their use is triggered by the conceptual short-term memory. The unicist artificial intelligence emulates this process by storing the information in an intelligent knowledge base.
This intelligence is structured by the unicist logic, that emulates the intelligence of nature, and establishes the rules of the functionality and evolution of the ontogenetic maps, which define the unified field of the adaptive system, including the restricted and wide contexts.
Emulating the Structure of Human Intelligence
Human intelligence works with the triadic structure of the intelligence that underlies nature. 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 reactive intelligence is driven by the emotional intelligence that defines and controls the purpose of the reactive behavior that it materialized in rational actions driven by the IQ. The entropy inhibitor of 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. It 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.
Ontointelligence is the deepest intelligence humans have and allows apprehending the nature of things to better adapt to the environment. It is integrated by ethical intelligence, strategic intelligence and the logical type of thought.
The triadic structure of conscious intelligence integrates the ontointelligence, the reactive intelligence and the active intelligence to define, implement and monitor adaptive actions in the environment.
Unicist Artificial Intelligence Emulates the Human Reflection Process
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.
Unicist Artificial Intelligence 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 the 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 artificial intelligence 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 the recycling though the learning function.
Artificial Intelligence: The 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.
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 measure 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 Artificial Intelligence and Ontogenetic Maps
Unicist Artificial Intelligence is based on the use of ontogenetic maps and their pilot testing process to ensure their functionality and learning. Ontogenetic maps describe the conceptual structure of a specific complex adaptive system or environment. They define the “DNA” of a specific function by defining the concepts and fundamentals that drive the root causes and root drivers of the system.
They were developed to deal with universal solutions in order to be cross-cultural and timeless. Therefore, they need to be transformed into operational structures to deal with specific solutions that are adapted to a specific environment. These specific ontogenetic maps are used in the development of solutions using artificial intelligence.
Therefore, the ontogenetic maps of specific functions are part of the Unicist AI Monitor. Based on the laws of evolution, that have been transformed into logical rules, they allow managing the functionality and evolution of the adaptive function they describe and define.
The Learning Process
The ontogenetic maps define the different objects that integrate the adaptive system that allow developing the necessary pilot testing process that drives the learning of the monitor.
The analogical learning is based on the learning from the observable facts. But as the Unicist AI monitor deals with homological learning, a supervised learning process is necessary to ensure the expansion of the boundaries to find solutions.
The learning process is driven by unicist destructive tests, where a solution that is initially functional is tested beyond its actual use until the expanded boundaries of this use become dysfunctional to learn the limits of its functionality. The process needs to be restarted when the tests do not begin by being functional.
The Unicist Artificial Intelligence Monitor in Business
The Unicist Artificial Intelligence Monitor is an intelligent interface that allows organizing adaptive systems and environments and finding the root causes of their functionality. It is based on the use of the ontogenetic maps of business functions that have been researched. This monitor 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 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 business functions using the rules of the unicist double dialectical logic that allow managing the dynamics and evolution of complex adaptive systems and environments.
The monitor is an intelligent interface that defines the concepts and fundamentals of a business function as objects and establishes their relations and functionality. The system defines the value the objects produce 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 of business functions that define their concepts and fundamentals. This information allows defining what is needed and comparing it with the actual state to define the actions that are necessary to achieve the established goals.
The unicist artificial intelligence allows emulating the solutions of a complex adaptive system to build structural adaptive solutions. The use of the monitor requires managing the unicist strategy model, that 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.
Unicist Logic applied to Reality
The dialectical logic of Hegel and Marx follow the natural dualistic operation of neurons (on/off). It is functional to rationalism.
The unicist double dialectical logic uses the dualistic operation of neurons to build a mental emulation of the structure of nature that allows dealing with the adaptive aspects of reality.
Unicist thinking is the name given to the process that allows building the double dialectical logic.
Unicist thinking allows emulating in mind the structure of adaptive aspects of reality in order to manage them. It provides the necessary operating system to manage adaptive systems to expand the boundaries of an activity and infer future scenarios in order to adapt.
Unicist thinking allows defining the nature of reality in a reasonable and provable way. It is based on double dialectical thinking in order apprehend nature emulating the ontogenetic intelligence of nature.
It is necessary to diagnose, build strategies and design architectures. It provides the structure to understand the fundamentals of an activity and integrate the fundamental knowledge with the technical analytical knowledge to make decisions.
The discovery of the unicist thinking approach is based on:
1) The discovery of the ontogenetic intelligence of nature that drives the evolution of living beings.
2) The discovery of onto-intelligence which is the human intelligence to adapt to the environment and is integrated by the ethical intelligence, the type of thought, and the strategic intelligence.
Unicist Thinking allows emulating the ontogenetic intelligence of nature using the double dialectical thinking. It is a demonstration that the dualistic dialectical approaches that Hegel and Marx developed to explain the evolution of human behavior are fallacious.
The functionality of double dialectics
With this double dialectical approach (purpose – active function, purpose – energy conservation function) one can understand the structure of an adaptive system and its evolution.
Unicist Dialectics is based on the emulation of adaptive systems, emulating the ontogenetic intelligence of nature (purpose, active principle, energy conservation principle).
Its application to human adaptive systems made the emulation of individual, institutional and social evolution possible.
To approach a reality integrated by three elements with a dualistic mind it is necessary to consider it as a dualistic integration of binary elements. To perceive dialectics it is necessary to have a high abstraction capacity.
Those who do not have the abstraction capacity consider the dialectical behavior based on observable facts of reality. They cannot differentiate essential correlations from cause-effect relations.
Individuals who have the necessary functional intelligence and the will to add value to an environment, and are able to see the double dialectics, develop two different actions to ensure results: on the one hand, they impulse action and on the other hand, they develop actions to inhibit entropy.
Individuals who approach reality using unicist thinking define strategies based on taxonomies and planed actions to influence the environment.
Unicist AI allows Emulating Adaptive Systems
The objective of emulating an action in mind before doing it, is to be able to have a maximal and a minimum strategy to ensure results.
The emulation process is based on apprehending the concept of the activity in order to have a behavioral object that is stored in the long-term memory to be used when necessary. It is based on the use of the unicist pilot test driven reflection process.
The risk of this approach is the building of fallacious structures, which can be avoided using destructive and non-destructive pilot tests.
Therefore, the key to emulate secure action processes in mind is to be able to develop the pilot tests to ensure that the root causes of the problems are being managed.
Every person needs to imagine action processes to develop them. There are two natural approaches when a person emulates action processes in mind:
1) The use of an instinctive approach using dualistic logic.
2) The use of a conscious approach using integrative logic.
The dualistic logical approach is driven by the needs of an individual, which transform the pilot tests into trial and error processes that cannot emulate a homology of the actions to be developed and substitutes it by an analogical approach.
This approach does not allow accessing the root causes of problems or defining secure solutions.
The integrative logical approach, on the other hand, requires designing the pilot tests based on the functional aspects of reality and the essential aspects that are defined by the concepts, fundamentals and root causes of the problems.
It allows emulating the structure and content of the action processes achieving a level of reliability of results that depends on the level of emulation that can be achieved based on the functional and essential knowledge that is being managed.
The emulation needs to end as a simple system that establishes univocal cause-effect relations and actions that can be developed without needing the knowledge of the strategy behind the operational aspects of the solution.
The Basics Behind Artificial and Mental Emulation
1) The discovery of the ontogenetic intelligence of nature allowed discovering the concepts that underlie natural and artificial adaptive entities and explain their evolution. It provided the basics for secure strategy building and for the management of the root causes of things.
2) The discovery of the triadic functionality of human conscious intelligence allowed emulating nature to develop strategies to influence the environment. It provided the basics to understand and influence individual actions.
3) The discovery of the triadic functionality of collective intelligence allowed understanding the evolution of groups, institutions and societies. It provided the basics to understand and influence social behavior.
These basics sustain the emulation of the concepts and fundamentals of an adaptive environment that allow managing its root causes and the development of unicist strategies to ensure the generation of results.
The models that are emulated in mind
Level 1 – Universal models
Level 2 – Specific models
Level 3 – Functional models
Level 4 – Adaptive models
Level 1 – Universal models
These models define the category of an activity. They are based on the pre-concepts that are stored in mind and respond to the beliefs of the person who is emulating actions. They imply universal actions that are fully dependent on the existence of successful experiences and their applicability. These models are extremely rigid because they have subjective empirical groundings.
Level 2 – Specific models
These models are based on the universal models an individual has in mind. They can be built when the level of consciousness of the individual allows her/him to accept that the specific characteristics of the environment might change the structure of the solution. These models are based on the development of pilot tests of the universal models that define the boundaries of their applicability. These models are generic but not universal.
Level 3 – Functional models
These models are based on the specific models an individual has in mind. They are based on the knowledge of the technical and operational aspects of their implementation that allow going beyond generic approaches and entering functional solutions. They are dominantly efficiency driven and are based on approaching processes with operational and functional objects that allow organizing processes in controlled environments.
Level 4 – Adaptive models
These models are based on the existence of functional models in mind. They are based on the integration of dynamic approaches to define processes. They imply having future scenarios that allow emulating the dynamic of the processes to define an adaptive model. These models include the existence of maximal and minimum strategies for each function included in the field. They include the wide and restricted contexts and their evolution. This allows establishing a dynamic approach to reality.
Unicist Artificial Intelligence: The Use of Predictors
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.