Making sense of the world: AI and human intelligence
To “make sense of the world as a whole” or To “make sense of the world in its parts” or To “make sense of the world by human senses”, all request the metaphysical, scientific or religious worldviews, as all is interactions or all things are interrelated or the world is “formed on a grand rational design.”
A worldview or Weltanschauung is?encompassing the whole of the individual's or society's or machine's knowledge/data/information of the world.
A key feature of world sense-making is identity and identification and understanding:?Sense-making takes place from your worldview point of view, with your knowledge and experiences. It starts with labeling and identifying things, concepts, ideas, thoughts, actions, etc., developed progressively, as in:
[N]: naming, denotation or designation
[D]: definition by features, traits or characteristics, similarities or resemblances, species and differentia, or causes, material or formal, efficient or final, or interests
[C]: classification, the grouping of related observations or data, facts, instances or occurrences into classes, as with the living things: animal, plant, fungi, protist and monera
categorization, dividing the world into groups, classes or categories of entities whose members are in some way similar to each other. Ideally, categories need to be clearly defined, mutually exclusive and collectively exhaustive, as logical, top-down classification vs. bottom-up, statistical classification, genealogical classification vs. pragmatic, functional or teleological classification ?
conception or conceiving or conceptualization, inductive or deductive, a worldview of a part of the world, containing the objects, concepts, and other entities and relationships between them for some particular purpose; an explicit specification of a conceptualization makes an inductive ontology, and vice versa
reasoning or inferences
learning, prediction, decision-making
action and interaction
Having words to identify and understand is the first step in making sense.?You might be happy to just know the word for a phenomenon even though your don’t understand its full sense and meaning, science or history.
Examples: [N] Digital Transformation
D: Digital transformation (DX) is the use of digital technology and data analytics to make?data-driven decisions
C: Technologies associated with digital transformation include cloud computing,?big data analytics,?artificial intelligence?(AI),?blockchain, machine learning (ML), the?Internet of Things?(IoT) and?5G.
[N]: AI System
[D]: An AI system is a machine-based system that is capable of influencing the environment by producing an output (predictions, recommendations or decisions) for a given set of objectives.?
[D]: Artificial intelligence (AI) , also known as machine intelligence, is a branch of computer science that focuses on building and managing technology that can learn to autonomously make decisions and carry out actions on behalf of a human being. AI is ... an umbrella term that includes any type of software or hardware component that supports machine learning, computer vision, natural language understanding, natural language generation, natural language processing and robotics. Today’s AI uses conventional?CMOS ?hardware and the same basic algorithmic functions that drive traditional software. Future generations of AI are expected to inspire new types of brain-inspired circuits and architectures that can make data-driven decisions faster and more accurately than a human being can.
[Conception]: 'artificial intelligence system' means: ...software that is developed with [specific] techniques and approaches [listed in Annex 1] and can, for a given set of human-defined objectives, generate outputs such as content, predictions, recommendations, or decisions influencing the environments they interact with. The notion of 'AI system' would refer to a range of software-based technologies that encompasses 'machine learning', 'logic and knowledge-based' systems, and 'statistical' approaches: 'machine learning approaches', including supervised, unsupervised and reinforcement learning, using a wide variety of methods including deep learning; 'logic and knowledge-based approaches', including knowledge representation, inductive (logic) programming, knowledge bases, inference and deductive engines, (symbolic) reasoning and expert systems; and, 'statistical approaches', Bayesian estimation, search and optimisation methods.
[Classification]: Machine Learning, NLP, Expert Systems, Computer Vision, Robotics, Planning
AI is including the following technologies:
[Conceptualization]: It uses machine and/or human-based data and inputs to (i) perceive real and/or virtual environments; (ii) abstract these perceptions into models through analysis in an automated manner (e.g., with machine learning), or manually; and (iii) use model inference to formulate options for outcomes. AI systems are designed to operate with varying levels of autonomy.
[Inferences and Predictions]: AI is a general-purpose technology that has the potential to improve the welfare and well-being of people, to contribute to positive
sustainable global economic activity, to increase innovation and productivity, and to help respond to key global challenges. It is deployed in many sectors ranging from production, finance and transport to healthcare and security.
Alongside benefits, AI also raises challenges for our societies and economies, notably regarding economic shifts and inequalities, competition, transitions in the labour market, and implications for democracy and human rights.
So, definitions are nothing, Conceptualizations are everything.
How to see the world and its items
It all depends on your philosophical party membership:
Nominalism (materialism/empiricism)
Conceptualism (subjective idealism)
Realism [physicalism and naturalism or absolute idealism or dialectical?materialism]
Abstract entities (concepts, ideas, universality of particulars, qualities) are belonging to either the physical or the mental realm or to a third, transcendental world (Popper's three worlds).
I see such a type-token or abstract-concrete distinction as in:?an entity/object is abstract if it lacks causal agency, interaction,?the power to change something, to?act on other objects.?
The most advanced party could be conceptualist realism?stating that our conceptual framework maps reality.
The general quality of beauty exists as an idea/concept/mental?representation in the mind, a symmetry or pattern?in nature?and as an abstract concept word "beauty" in natural?language.
Or, take the concept of dark energy. It has no temporal or spatial location. But how do you know about it? DE exerts a negative, repulsive force, reverse gravitation, affecting the universe on?the cosmological?scales.?
Naming is not Conception
If it were like this, our life would be much easier.
You might have a thousand/million/billion words, but no conception, as a class, type, kind, or a set of entities in a domain. This is the critical issue with dumb and dull AI/LLM models operating with a billion tokens (letters, words, sentences), but without any conception.
The concept formation needs a lot of intelligence, thinking and reasoning.
Even learning natural low-level perceptual conceptions, as sound, smell. and color, space and time, have taken a long mental development, not mentioning highly abstract scientific or metaphysical concepts.?
Due to the concept formations, people are intelligent: they use language, make complex inferences, develop and use scientific theories, make laws, adapt to complex dynamic environments and form technologies.
Analogy is the gist of Intelligence
Analogy is the core of intelligence, human and machine, It is less studied than other forms of inference: deduction, induction or abduction. It involves similarity and resemblance, comparison?and correspondence is?transferring information or meaning or knowledge from one particular subject (the source, feature space, domain) to another ( the target, the label space, range)
Stem cells are unique cells with the potential to develop into many different cell types in the body. They have the ability to self-renew, which means they can divide and produce more stem cells, and differentiate, which is the process of transforming into specialized cells with specific functions.?
Stem cells play a crucial role in the growth, maintenance, and repair of various tissues in the body. There are two main types of stem cells: embryonic stem cells and adult (or somatic) stem cells.?https://www.earth.com/news/breakthrough-gray-hair-will-soon-be-a-thing-of-the-past/
Ontology is about the categorization of entities into kinds, or partitioning reality into the fundamental categories of classes.?Ontological categories are unique entity classes with the potential to develop into many different entity types in the body of the world. They have the ability to self-renew, which means they can divide and produce more entities, and differentiate, which is the process of transforming into specialized things with specific functions.
A 5-tuple inductive ontology: individuals/instances, concepts, axioms, functions and relations or a 4-tuple analytic ontology of Kant: Quantity, Quality, Relation and Modality.
Reality, Existence, Being, World or Universe;?Entity or Thing or Object; State, Quantity or Quality; Change, Action or Process; Relationship, Association or Interaction
Questions and Answers
Q: - a concept, thinking in concepts, appears only at a certain level of development of the mind. And even after that, it happens that a person has to act when he has no idea what he is dealing with.
A: Most?of our thinking is instinctive, intuitive, unconscious, fast thinking, when you first sense and then perceive the environment, internal or external, its sensory information via your sensory organs. Your conception starts as a psychological stimulation of sensory qualities. a leapfrog from physiological stimulations of agents or forms of energy, as light or pressure. It could be qualified as forming concrete/perceptual concepts in terms of stimuli, observations, facts, instances and data. Perception concepts are formed by the mind/intelligence by recognizing, organizing, analyzing and interpreting the sense data. Such a cognitive function is absent in the generative AI applications, as LLMs, dealing only with data and data patterns.???
Q: - how thinking occurs in everyday life is just beginning to be closely studied, i.e. this has never been a problem. Until they decided to teach the robots of everyday life.
A: You must enjoy creative, analytical and practical thinking. The last one is an everyday life thinking. Machines are missing all of them, if inly some imitations.
Q: - some terms do not have definitions (not only concepts but also relationships)
A: It might be better without definitions than with a thousand of them, like AI. The key thing is concepts,?not definitions.
Q: - most general purpose terms are very very polymorphic: it is a property of the mind to use the same word in different situations with different meanings.
A: Words and terms should be polymorphic and ambiguous, unlike definitions and conceptions. This enables the digital infinity of natural?languages.
Q: - why did such a subjective characteristic become "important matters"?
A: We have different conceptions of the same things due to different?experience and learning, education and culture, biases and prejudices.
Q: - an interesting question: what are the forms of thinking that do not use concepts?
A: It is an instinctive, intuitional or non-conscious thinking, reminding the mindless "thinking" of statistical AI models, operating with scalars, vectors and tensors of numbers.?
Q: Knowledge brought into the system is scientific knowledge. We formalize them in formal ontologies. Is it?
A: Scientific knowledge is universal knowledge, which is organized, coordinated and systematized.
World knowledge is intelligently condensed world's data,?say, as the knowledge graphs or scientific models or theories or natural laws.
Re. formal ontologies. There is no?such existence, yet. Its conception was confused from the very beginning,?like an explicit specification?of a conceptualization or?a formal, explicit specification of a shared conceptualization.
Ontology is a conceptualization of reality as a whole or its specific domain. You might have?deductive conceptualization and inductive conceptualization.