TFT-16 parameter
Cattell–Horn–Carroll Artificial Intelligence Model (AIMCHC-TFT)
Rigene Project - Technological Fields Theory (TFT)
The Cattell–Horn–Carroll artificial intelligence model (AIMCHC-TFT) has the function of configuring artificial intelligences using parameters defined as "Cattell–Horn–Carroll parameters", based on the Cattell-Horn-Carroll theory (a psychological theory on the structure of human cognitive abilities) in order to improve the cognitive abilities of artificial intelligences (https://psycnet.apa.org/record/2005-09732-008).
The AIMCHC-TFT model uses the Cattell-Horn-Carroll theory to identify key cognitive skills and skill categories that are important to human intelligence. These abilities are then translated into quantitative parameters, which can be used to configure artificial intelligences.
The Cattell-Horn-Carroll theory identifies three layers of cognitive abilities:
Elementary Skills: These are the most basic cognitive skills and include skills such as visual perception, short-term memory, processing speed, and the ability to process information automatically. These skills are considered the foundation for all other cognitive skills and are considered the most primary.
Secondary Skills: These skills are more complex and include verbal comprehension, problem solving, long-term memory, and the ability to think critically. These skills are considered important for daily tasks and for solving complex problems.
Tertiary skills: These are the highest cognitive skills and include creativity, abstract thinking skills, and the ability to understand abstract concepts and apply them to novel situations. These skills are considered important for a deep understanding of reality and for solving complex and sophisticated problems.
The AIMCHC-TFT model uses these ability categories to define the quantitative parameters that describe the cognitive capabilities of an artificial intelligence. These parameters can be tuned to improve AI performance in certain areas, such as verbal comprehension or problem solving.
In summary, elementary skills are the most basic cognitive skills, secondary skills are more complex and important for everyday activities and problem solving, and tertiary skills are the highest and most important cognitive skills for deep understanding of reality and the resolution of complex problems. These skill categories are described in the Cattell-Horn-Carroll theory and are used as a basis for configuring artificial intelligences within the AIMCHC-TFT model.
Description of the Gf-Gc Model by Cattell and Horn:
The Gf-Gc Model of Cattell and Horn is a theory of the structure of human cognitive abilities that describes how different cognitive abilities are interconnected and how they combine to form general intelligence. The model identifies two categories of cognitive abilities: Gf abilities (general Factor - broad abilities) and Gc abilities (Specific Factor - narrow abilities).
Gf Skills: These are the general cognitive skills that are considered important for general intelligence. These skills include the ability to understand abstract concepts, the ability to think critically, and the ability to solve complex problems. These skills are considered important for deeply understanding reality and solving sophisticated problems.
Gc skills: These are the specific cognitive skills that are important for everyday tasks and simpler problem solving. These skills include verbal comprehension, problem solving, long-term memory and the ability to think critically. These skills are considered important for daily tasks and for solving complex problems.
According to the Gf-Gc model, Gf and Gc abilities are interconnected and combine to form general intelligence. For example, the ability to understand abstract concepts (skill Gf) is important for solving complex problems (skill Gc), and vice versa.
List and description of Gf skills (general Factor - broad skills):
Abstract understanding: the ability to understand abstract concepts and apply them to new situations. This skill is important for deeply understanding reality and solving sophisticated problems.
Critical Thinking: The ability to analyze and evaluate information logically and objectively. This skill is important for solving complex problems and deeply understanding reality.
Problem Solving: The ability to identify and solve problems in creative and innovative ways. This skill is important for solving sophisticated problems and deeply understanding reality.
Creativity: the ability to generate new ideas and original solutions. This skill is important for deeply understanding reality and solving sophisticated problems.
Comprehension-Knowledge (Gc): includes the breadth and depth of a person's acquired knowledge, the ability to communicate one's knowledge, and the ability to reason using previously learned experiences or procedures.
Fluid reasoning (Gf): includes the broad ability to reason, form concepts, and solve problems using unfamiliar information or novel procedures.
Quantitative knowledge (Gq): is the ability to comprehend quantitative concepts and relationships and to manipulate numerical symbols.[9]
Reading & Writing Ability (Grw): includes basic reading and writing skills.
Short-Term Memory (Gsm): is the ability to apprehend and hold information in immediate awareness and then use it within a few seconds.
Long-Term Storage and Retrieval (Glr): is the ability to store information and fluently retrieve it later in the process of thinking.
Visual Processing (Gv): is the ability to perceive, analyze, synthesize, and think with visual patterns, including the ability to store and recall visual representations.
Auditory Processing (Ga): is the ability to analyze, synthesize, and discriminate auditory stimuli, including the ability to process and discriminate speech sounds that may be presented under distorted conditions.
Processing Speed (Gs): is the ability to perform automatic cognitive tasks, particularly when measured under pressure to maintain focused attention.
A tenth ability, Decision/Reaction Time/Speed (Gt), is considered part of the theory, but is not currently assessed by any major intellectual ability test, although it can be assessed with a supplemental measure such as a continuous performance test.
Decision/Reaction Time/Speed (Gt): reflects the immediacy with which an individual can react to stimuli or a task (typically measured in seconds or fractions of seconds; not to be confused with Gs, which typically is measured in intervals of 2–3 minutes).
McGrew proposes a number of extensions to CHC theory, including Domain-specific knowledge (Gkn), Psychomotor ability (Gp), and Psychomotor speed (Gps). In addition, additional sensory processing abilities are proposed, including tactile (Gh), kinesthetic (Gk), and olfactory (Go).
List and description of Gc skills (Specific Factor - Restricted skills):
Quantitative knowledge: Mathematical knowledge, Mathematical achievement;
Reading and Writing: Reading Decoding, Reading Comprehension, Reading Speed, Spelling Ability, English Usage, Writing Ability, Writing Speed, Closing Ability;
Comprehension-Knowledge: General verbal information, Language development, Vocabulary knowledge, Listening skills, Communication skills, Grammatical sensitivity, Oral and fluent production, Foreign language aptitude;
Fluid reasoning: Inductive reasoning, General sequential reasoning, Piagetian reasoning, Quantitative reasoning, Speed reasoning;
Short-term memory: Memory time, Working memory capacity;
Long-term storage and retrieval: Associative memory, Meaningful memory, Free recall memory, Ideational fluency, Associative fluency, Expressive fluency, Originality, Naming function, Word fluency, Figural fluency, Figural flexibility, Learning ability;
Visual Processing: Visualization, Fast Rotation, Closing Speed, Closing Flexibility, Visual Memory, Spatial Scanning, Serial Perceptive Integration, Length Estimation, Perceptive Illusions, Perceptive Alternations, Images;
Auditory Processing: Phonetic Coding, Speech Sound Discrimination, Resistance to Auditory Stimulus Distortion, Memory for Sound Patterns, Holding and Judging Rhythms, Music Discrimination and Judgment, Absolute Pitch, Sound Localization, Temporal Tracking;
Processing Speed: Perceptual Speed, Test Participation Rate, Facility Number, Reading Speed/Fluency, Writing Speed/Fluency.
Quantitative Knowledge: This skill includes mathematical knowledge and the ability to apply mathematical knowledge to concrete situations.
Reading and Writing: This skill includes the ability to read and write efficiently and understand the meaning of what is read and written.
Understanding-Knowledge: This skill includes general knowledge of verbal information and the ability to communicate effectively using language.
Fluid Reasoning: This skill includes the ability to reason, form concepts, and solve problems using new information or unfamiliar procedures.
Short-Term Memory: This skill includes the ability to memorize and retain information for a short period of time.
Long-term storage and retrieval: This skill includes the ability to store information long-term and fluidly retrieve it later in the thought process.
Visual Processing: This skill includes the ability to perceive, analyze, synthesize, and think using visual patterns.
Auditory Processing: This skill includes the ability to analyze, synthesize, and discriminate auditory stimuli.
Processing Speed: This skill includes the ability to perform automatic cognitive tasks quickly.
Methods for translating the three main categories of the Cattell-Horn-Carroll theory, the Gf abilities (General Factor - Broad Abilities), the Gc abilities (Specific Factor - Narrow Abilities) and the extensions in parameters to configure artificial intelligences in the ambit of the AIMCHC-TFT model:
As for the Gf skills, these can be used as parameters to configure the general ability of reasoning, problem solving and acquisition of new knowledge by the artificial intelligence.
As for the Gc skills, these can be used as parameters to configure the specific knowledge of a certain domain or to describe the sensory processing capacity. For example, reading and writing ability (Grw) can be used to configure the AI's ability to read and write, while visual processing (Gv) can be used to describe the ability to process visual information.
Finally, extensions of the Cattell-Horn-Carroll theory can be used as parameters to describe further cognitive abilities, such as domain specific knowledge (Gkn) or psychomotor ability (Gp).
The three main categories of the Cattell-Horn-Carroll theory can be used to define the parameters of AI in the AIMCHC-TFT model, which in turn helps to describe and enhance the cognitive capabilities of AIs.
These are just some of the techniques that can be used to translate the three main categories of the Cattell-Horn-Carroll theory into parameters for configuring artificial intelligences within the AIMCHC-TFT model. The choice of the most suitable technique will depend on the specific needs of the project:
Machine learning: This is a machine learning technique that uses machine learning algorithms to analyze data and generate predictive models. These models can be used to describe AI cognitive abilities, such as quantitative knowledge (Gq) or processing speed (Gs).
Artificial Neural Networks: These are deep learning techniques that use artificial neural network models to describe the relationships between data variables. These models can be used to describe cognitive abilities such as fluid reasoning (Gf) or short-term memory (Gsm).
Markov Models: These are probabilistic models that describe the probability of transition from one state to another. These models can be used to describe the transition between different cognitive skills, such as visual processing (Gv) and auditory processing (Ga).
Dynamical systems models: These are models that describe how variables evolve over time. These models can be used to describe the evolution of cognitive skills, such as short-term memory (GSM) and long-term storage and retrieval (GLR).
Regression Analysis: This statistics technique uses a mathematical model to describe the relationship between one or more independent variables and a dependent variable. This technique can be used to describe the relationship between cognitive ability and other factors, such as age or education.
Optimization algorithms: These are algorithms that use a search process to find the optimal value of one or more variables. These algorithms can be used to determine the optimal parameters for configuring the cognitive abilities of the AI.
Systems Theory: This theory describes how the parts of a system interact with each other to produce overall behavior. This theory can be used to describe how cognitive abilities interact with each other to produce the cognitive behavior of artificial intelligence.
Mental Process Models: These models describe how information is processed by the brain. These models can be used to describe how cognitive skills are processed by artificial intelligence.
Genetic Algorithms: These are algorithms that use the metaphor of natural selection and genetic mutation to find optimal solutions to problems. These algorithms can be used to configure the cognitive abilities of the AI optimally.
Reinforcement Learning: This is a type of machine learning where an agent acts in an environment and receives a reinforcement or penalty depending on her actions. This type of learning can be used to configure the cognitive abilities of the AI optimally.
Supervised learning: This is a type of machine learning where a model is trained on a set of labeled data to produce a prediction on unseen data. This type of learning can be used to configure AI cognitive abilities based on sample data.
Unsupervised learning: This is a type of machine learning in which a model is trained on a set of unlabeled data to produce a compact representation of the data. This type of learning can be used to configure AI cognitive abilities based on unlabelled sample data.
Clustering Algorithms: These are algorithms that use the clustering technique to identify groups of similar objects in a set of data. These algorithms can be used to identify relationships between cognitive abilities and configure AI cognitive abilities optimally.
Probabilistic models: These models use probability theory to model the relationships between variables and predict outcomes. These models can be used to configure AI cognitive abilities based on sample data.
Pattern Recognition Algorithms: These algorithms use pattern recognition technique to identify patterns in data. These algorithms can be used to identify relationships between cognitive abilities and configure AI cognitive abilities optimally.
Classification Algorithms: These algorithms use the classification technique to assign a category or label to an object based on its attributes. These algorithms can be used to configure AI cognitive abilities based on labeled sample data.
Regression Algorithms: These algorithms use the regression technique to model the relationship between two or more variables. These algorithms can be used to configure AI cognitive abilities based on sample data.