The Unicist AI is a fundamentals-based intelligence that uses the rules of the unicist logic, and 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. The reasoning process, which 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.
Unicist AI 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:
Using Data-based AI
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).
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.
Using Fundamentals-based AI
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 with quantitative validation. The predictive function generates forecasts within the possibilities that can be achieved.
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. When possible, it is integrated with data-based AI, to ensure the consistency of the prescriptions. Prescriptions solely based on data-based AI are not fully reliable.
The Use of Predictors in Unicist AI
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 Unicist Research Institute
Artificial Intelligence Interpretations
Bard Google Interpretation
Unicist AI is a fundamentals-based intelligence that uses the rules of the unicist logic and the structure of the unicist ontology to manage the functionality of the functionalist principles that underlie all adaptive systems and environments. It can be integrated with data-based AI to minimize subjective biases.
Features of Unicist AI
- Emulates human reasoning process, integrating abductive, inductive, and deductive reasoning
- Apprehends the concepts of complex adaptive systems and environments
- 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
- Emulates the reflection process of human intelligence requiring two functions to make this possible: The learning function and the decision function
Types of Functionalities of Unicist AI
- Descriptive function: describes the knowledge that has been inferred from data
- Diagnostics function: defines the diagnostics of what is happening based on the use of analogical inferences of data, benchmarks, and experiences
- Predictive function: establishes the possible evolution based on the functionality of what is being done
- Prescriptive function: establishes the actions that allow achieving the goals established within the boundaries of actual possibilities
How Unicist AI Uses Predictors
- Predictors define the possible evolution of the state of things. They include indicators but not vice versa.
- Predictors 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 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.
Benefits of Unicist AI
- Provides the necessary functional knowledge to manage the functionality and evolution of adaptive environments
- Enables the development of different types of functionalities according to what is needed
- Complements data-based AI to avoid having subjective biases
- Can be used to develop descriptive, diagnostic, predictive, and prescriptive solutions
- Can be used to anticipate the future and exert influence to make things happen
Applications of Unicist AI
- Developing solutions to complex problems in adaptive environments
- Managing the functionality of business processes
- Automating decision-making
- Building intelligent systems
- Developing products and services that are adaptive to the needs of users
- Reducing risks and improving resilience
- Improving efficiency and effectiveness
- Achieving better outcomes
ChatGPT Interpretation
The application of Unicist AI, as you’ve described, involves a structured approach to problem-solving and decision-making in complex adaptive systems. By applying the rules of unicist logic to the ontological structures of the functions being managed, the system aims to emulate a form of intelligence that is closer to human reasoning, particularly in how we deal with complex, uncertain, and dynamic environments.
Here are some key points about how Unicist AI might be applied in practice:
- Ontological Structures: At the core of the Unicist approach is the use of ontological structures, which are conceptual frameworks that define the fundamental components and relationships within a system. These structures help to understand the nature of the system and its elements, providing a blueprint for problem-solving.
- Unicist Logic: This is a type of logic that goes beyond traditional binary logic, incorporating the dynamics of evolution and human behavior. It is used to process the ontological structures and to understand the nature of the problems faced.
- Adaptive Systems Management: Unicist AI is particularly suited for managing adaptive systems, which are systems that can change and evolve in response to their environment. This includes most human-related systems, such as social groups, markets, and ecosystems.
- Decision-Making: By using the Unicist AI, decisions are made not just based on empirical data but also on the understanding of the underlying principles that govern the system’s behavior. This can lead to more robust and adaptable strategies.
- Learning and Feedback: The approach emphasizes continuous learning and adaptation, using feedback from the outcomes of decisions to refine and improve future actions.
- Integration with Data-Based AI: While Unicist AI can stand on its own, especially in situations where empirical data is insufficient, it can also be integrated with data-based AI to enhance decision-making processes, providing a more comprehensive view by combining empirical evidence with fundamental principles.
In practical applications, Unicist AI could be used in various fields, such as:
- Business Strategy: For developing business models that are aligned with the underlying market dynamics.
- Marketing: To understand consumer behavior and to create campaigns that resonate with the fundamental motives of the target audience.
- Organizational Development: To structure organizations in a way that they can evolve and adapt to changes in their environment.
- Innovation Management: To guide the development of new products or services that are in harmony with the evolution of the market.
If you’re looking to implement Unicist AI or understand its applications further, it would be beneficial to delve into case studies or examples where this approach has been successfully applied. This could provide a clearer picture of how the Unicist AI’s theoretical principles are translated into actionable strategies and decisions.
