1. Introduction and Motivation

In his 2011 bestseller book “Thinking Fast and Slow” [1], Daniel Kahneman introduced one of his rules of common thinking: “A reliable way to make people believe in falshoods is frequent repetitions because familiarity is not easily distinguished from truth.” This rule applies to sentences that can be qualified as true or false. A similar rule can be applied to concepts linking words or terms with their meanings. There seems to be sociolinguistic correspondence between the frequency of the use of terms or expressions, in particular those with philosophical significance, and the unrecognized diversity of their understanding. This diversity is obscured by the fact that the frequency of the use of these words generates the impression of familiarity and familiarity the illusion of an identity and existence of a unique denotation independent from any inquiry, the conviction that the meaning is obvious and uniform. As a result, people use frequently invoked words with a diverse subjective understanding while being convinced about their objective, uniform, commonly shared meaning. If they are confronted with differences in the understanding presented by others, they claim that such a different understanding is erroneous.

Intelligence, either artificial, natural, or human, has become an illustrative instance of this regularity. The term is present everywhere, in particular when qualified as artificial and used in its staple abbreviation AI, or when it appears unqualified when understood as human.

There are some curious differences between the ways in which human and artificial forms of intelligence are viewed. For a long time, there has been an unusual consensus, among experts and laypeople, that human intelligence is not only diverse but that its different forms are independent and possibly uncorrelated, each with a separate psycho-neurological mechanism. Thus, human intelligence can be fluid or crystallized following the division introduced in 1943 by Raymond Cattell [2], splitting Charles Spearman’s concept of general intelligence present in psychology since the beginning of the 20^th century into two different capacities. The former was understood as a purely general ability to solve unexpected problems without prior preparation or experience, and the latter consisted of long-established discriminatory habits acquired through learning or training. Incidentally, this distinction can be considered a precedent to Kahneman’s popular and recent distinction of fast (habitual, rigid) and slow (goal-oriented, flexible) thinking [1].

Further divisions of intelligence came in the 1980s. Robert Sternberg introduced his triarchic theory of intelligence, separating it into analytical, creative, and practical intelligence [3]. At about the same time, Howard Gardner introduced in his 1983 book “Frames of Mind: Theory of Multiple Intelligences” [4] the idea of “intelligences” in the plural form and presented a list originally consisting of seven types: linguistic, logical–mathematical, spatial, musical, bodily–kinetic, interpersonal, and intrapersonal. In 1995, he added an eighth type: naturalistic intelligence. This triggered multiple attempts to distinguish specific types of such multiple intelligences. Divisions of human intelligence have been made using diverse criteria and different levels of argumentation, but they have always been motivated by criticism of the inadequacy of the Spearman’s original concept of general intelligence and attempts to measure it using variations of William Stern’s and Alfred Binet's/Theodore Simon’s IQ tests. Today, there is no agreement regarding the selection of criteria, names, and the degree of correlation, but the idea of multiple intelligences became a standard in the study of human cognitive abilities.

The typical view of artificial intelligence (AI) relates it to human intelligence (such as a simulation, emulation, or recreation of the latter), and the main goal of current technological research and innovation is the achievement of artificial general intelligence (AGI), which does not have a commonly accepted or even discussed definition but is presented to the general audience in descriptions of the following type: “AGI [...] a system that is capable of matching or exceeding human performance across the full range of cognitive tasks.” [5]. This category error, blurring the distinction between the abstract concept and its individual realizations, characteristic of virtually all instances of the discourse on artificial intelligence (general or otherwise), perpetuates the hidden comprehension of its unity.

There are separate names for specific types of technological realizations of AI (generative AI based on Large Language Models (LLMs) in neural networks with a deep learning architecture, followed by Large Reasoning Models (LRMs) heavily dependent on external prompts, i.e., minimizing their autonomy; agentic AI, re-engaging some forms of algorithmic computation, still dependent on initial prompts but with increasing autonomy in its operation through auto-reprompting; neuro-symbolic AI with a hybrid architecture; causal AI; etc.) However, all of these qualifications reflect not the conceptual diversity of artificial intelligences but the technological differences in the search for the realization of the same goal of artificial general intelligence (AGI).

The inconsistency between conceptualizations of human multiple intelligences and the uniform idea of artificial general intelligence matching or even surpassing human cognitive abilities is only one of the many manifestations of conceptual chaos in the study of intelligence. The situation becomes even more complex when we consider the extensions of intelligence understood as a characteristic of natural but non-human entities present in diverse forms of life on Earth or the expected but not yet known forms of extraterrestrial life. This complication arises not only from the association of intelligence with life, whose conceptualization has been similarly convoluted, but also from the increased diversity of the morphological and behavioral forms involved in manifestations of intelligence in living objects, which require the careful avoidance of anthropomorphization.

2. The Philosophical Significance of Intelligence in Terms of Information and Complexity

Is this conceptual complexity of intelligence or intelligences a good reason to question the feasibility of, or the justification, for any attempts to seek a unifying perspective on such a complex variety of related yet diverse forms of what in multiple contexts is called by the same common-sense name of “intelligence”? This paper has as its objective the justification of a negative answer to this question. In this case, as in many other cases known from the intellectual history of humanity, the phenomenal complexity of the manifestations of intelligence is unquestionable, but the complexity of their perception and comprehension can be overcome with the use of the appropriate intellectual tools.

The tools proposed here are appropriately general concepts of information and its complexity, together with already known methods of reducing or controlling the complexity of information, with their long history going back at least to the Law of Requisite Variety proposed by W. Ross Ashby [6]. In this paper, the methods of reducing complexity are traced much further back in their association with intelligent or efficient inquiry. The use of the concept of information brings its own challenges, as the term information is still a subject of the rule of illusionary uniformity of meaning in its common-sense use considered at the beginning of this paper. However, in the philosophy of information, this issue has been adequately addressed, and this intellectual experience can be now applied to intelligence.

Of course, the proposed tools can be used only if we assume that intelligence can be relativized to the concept of information. However, all existing studies of intelligence contain this assumption, even if sometimes in a hidden form. Moreover, the philosophical significance of the concept of information, in particular in its association with complexity, brings into the inquiry an extensive toolbox of philosophical inquiry that allows for the integration of methodologies developed for the research of particular domains of reality. At the same time, the study of intelligence acquires an interdisciplinary status.

After reviewing a wide range of fundamental concepts associated with intelligence in its diverse contexts, this paper presents an initial formulation of the general characterization of intelligence as the ability to minimize the resources necessary to effectively perform a maximal range of actions. However, it is shown that the use of the terms “resource” and “action” leads to overgeneralization in the absence of their qualification or identification of their meaning. For instance, the motion of every object can be described by the mechanical law of minimal action. On the other hand, the identification of resources may lead either to excessive restrictions, resulting in undergeneralization, or a vicious circle of reasoning. For this reason, the term “resource” is replaced with “information” and “action” with “overcoming complexity”. Then, intelligence is defined as the capacity to overcome complexity. Incidentally, with this understanding of intelligence comes the elimination of the complexity of its manifestations in diverse contexts by lifting the level of abstraction through the overarching concept of information. Therefore, our inquiry can be considered an intelligent inquiry into intelligence.

References

1. Kahneman, D. Thinking, Fast and Slow. New York: New York: Farrar, Straus and Giroux, 2011.
2. Cattell, R. B. The measurement of adult intelligence. Psychological Bulletin, 1943, 40(3), 153–193. https://doi.org/10.1037/h0059973
3. Sternberg, R. J. Beyond IQ: A Triarchic Theory of Intelligence. Cambridge University Press, Cambridge, 1985.
4. Gardner, H. Frames of Mind: The Theory of Multiple Intelligences. Basic Books, New York, N.Y. 1983, ISBN 978-0133306149.
5. Jones, N. How AI can achieve human-level intelligence: researchers call for change in tack. Nature, News March 4, 2025, Retrieved March 7, 2025, from: How AI can achieve human-level intelligence: researchers call for change in tack.
6. Ashby, W. R. An Introduction to Cybernetics. Chapman & Hall, London, 1956.