Intelligence has been defined in many ways: the capacity for logic, understanding, self-awareness, learning, emotional knowledge, reasoning, planning, creativity, critical thinking, and problem-solving. More generally, it can be described as the ability to perceive or infer information, and to retain it as knowledge to be applied towards adaptive behaviors within an environment or context.
Intelligence is most often studied in humans but has also been observed in both non-human animals and in plants. Intelligence in machines is called artificial intelligence, which is commonly implemented in computer systems using programs and, sometimes, specialized hardware.
History of the term
The word intelligence derives from the Latin nouns intelligentia or intellēctus, which in turn stem from the verb intelligere, to comprehend or perceive. In the Middle Ages, the word intellectus became the scholarly technical term for understanding, and a translation for the Greek philosophical term nous. This term, however, was strongly linked to the metaphysical and cosmological theories of teleological scholasticism, including theories of the immortality of the soul, and the concept of the active intellect (also known as the active intelligence). This approach to the study of nature was strongly rejected by the early modern philosophers such as Francis Bacon, Thomas Hobbes, John Locke, and David Hume, all of whom preferred "understanding" (in place of "intellectus" or "intelligence") in their English philosophical works. Hobbes for example, in his Latin De Corpore, used "intellectus intelligit", translated in the English version as "the understanding understandeth", as a typical example of a logical absurdity. "Intelligence" has therefore become less common in English language philosophy, but it has later been taken up (with the scholastic theories which it now implies) in more contemporary psychology.
A very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience. It is not merely book learning, a narrow academic skill, or test-taking smarts. Rather, it reflects a broader and deeper capability for comprehending our surroundings—"catching on," "making sense" of things, or "figuring out" what to do.
Individuals differ from one another in their ability to understand complex ideas, to adapt effectively to the environment, to learn from experience, to engage in various forms of reasoning, to overcome obstacles by taking thought. Although these individual differences can be substantial, they are never entirely consistent: a given person's intellectual performance will vary on different occasions, in different domains, as judged by different criteria. Concepts of "intelligence" are attempts to clarify and organize this complex set of phenomena. Although considerable clarity has been achieved in some areas, no such conceptualization has yet answered all the important questions, and none commands universal assent. Indeed, when two dozen prominent theorists were recently asked to define intelligence, they gave two dozen, somewhat different, definitions.
|Alfred Binet||Judgment, otherwise called "good sense", "practical sense", "initiative", the faculty of adapting one's self to circumstances ... auto-critique.|
|David Wechsler||The aggregate or global capacity of the individual to act purposefully, to think rationally, and to deal effectively with his environment.|
|Lloyd Humphreys||"...the resultant of the process of acquiring, storing in memory, retrieving, combining, comparing, and using in new contexts information and conceptual skills".|
|Howard Gardner||To my mind, a human intellectual competence must entail a set of skills of problem solving — enabling the individual to resolve genuine problems or difficulties that he or she encounters and, when appropriate, to create an effective product — and must also entail the potential for finding or creating problems — and thereby laying the groundwork for the acquisition of new knowledge.|
|Linda Gottfredson||The ability to deal with cognitive complexity.|
|Robert Sternberg & William Salter||Goal-directed adaptive behavior.|
|Reuven Feuerstein||The theory of Structural Cognitive Modifiability describes intelligence as "the unique propensity of human beings to change or modify the structure of their cognitive functioning to adapt to the changing demands of a life situation".|
|Shane Legg & Marcus Hutter||A synthesis of 70+ definitions from psychology, philosophy, and AI researchers: "Intelligence measures an agent's ability to achieve goals in a wide range of environments", which has been mathematically formalized.|
|Alexander Wissner-Gross||F = T ∇ S|
"Intelligence is a force, F, that acts so as to maximize future freedom of action. It acts to maximize future freedom of action, or keep options open, with some strength T, with the diversity of possible accessible futures, S, up to some future time horizon, τ. In short, intelligence doesn't like to get trapped".
Human intelligence is the intellectual power of humans, which is marked by complex cognitive feats and high levels of motivation and self-awareness. Intelligence enables humans to remember descriptions of things and use those descriptions in future behaviors. It is a cognitive process. It gives humans the cognitive abilities to learn, form concepts, understand, and reason, including the capacities to recognize patterns, innovate, plan, solve problems, and employ language to communicate. Intelligence enables humans to experience and think.
Much of the above definition applies also to the intelligence of non-human animals.
Nonhuman animal intelligence
Although humans have been the primary focus of intelligence researchers, scientists have also attempted to investigate animal intelligence, or more broadly, animal cognition. These researchers are interested in studying both mental ability in a particular species, and comparing abilities between species. They study various measures of problem solving, as well as numerical and verbal reasoning abilities. Some challenges in this area are defining intelligence so that it has the same meaning across species (e.g. comparing intelligence between literate humans and illiterate animals), and also operationalizing a measure that accurately compares mental ability across different species and contexts.
Wolfgang Köhler's research on the intelligence of apes is an example of research in this area. Stanley Coren's book, The Intelligence of Dogs is a notable book on the topic of dog intelligence. (See also: Dog intelligence.) Non-human animals particularly noted and studied for their intelligence include chimpanzees, bonobos (notably the language-using Kanzi) and other great apes, dolphins, elephants and to some extent parrots, rats and ravens.
Cephalopod intelligence also provides important comparative study. Cephalopods appear to exhibit characteristics of significant intelligence, yet their nervous systems differ radically from those of backboned animals. Vertebrates such as mammals, birds, reptiles and fish have shown a fairly high degree of intellect that varies according to each species. The same is true with arthropods.
g factor in non-humans
Evidence of a general factor of intelligence has been observed in non-human animals. The general factor of intelligence, or g factor, is a psychometric construct that summarizes the correlations observed between an individual's scores on a wide range of cognitive abilities. First described in humans, the g factor has since been identified in a number of non-human species.
Cognitive ability and intelligence cannot be measured using the same, largely verbally dependent, scales developed for humans. Instead, intelligence is measured using a variety of interactive and observational tools focusing on innovation, habit reversal, social learning, and responses to novelty. Studies have shown that g is responsible for 47% of the individual variance in cognitive ability measures in primates and between 55% and 60% of the variance in mice (Locurto, Locurto). These values are similar to the accepted variance in IQ explained by g in humans (40–50%).
It has been argued that plants should also be classified as intelligent based on their ability to sense and model external and internal environments and adjust their morphology, physiology and phenotype accordingly to ensure self-preservation and reproduction.
A counter argument is that intelligence is commonly understood to involve the creation and use of persistent memories as opposed to computation that does not involve learning. If this is accepted as definitive of intelligence, then it includes the artificial intelligence of robots capable of "machine learning", but excludes those purely autonomic sense-reaction responses that can be observed in many plants. Plants are not limited to automated sensory-motor responses, however, they are capable of discriminating positive and negative experiences and of "learning" (registering memories) from their past experiences. They are also capable of communication, accurately computing their circumstances, using sophisticated cost–benefit analysis and taking tightly controlled actions to mitigate and control the diverse environmental stressors.
Artificial intelligence (or AI) is both the intelligence of machines and the branch of computer science which aims to create it, through "the study and design of intelligent agents" or "rational agents", where an intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success. Kaplan and Haenlein define artificial intelligence as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation”. Achievements in artificial intelligence include constrained and well-defined problems such as games, crossword-solving and optical character recognition and a few more general problems such as autonomous cars. General intelligence or strong AI has not yet been achieved and is a long-term goal of AI research.
Among the traits that researchers hope machines will exhibit are reasoning, knowledge, planning, learning, communication, perception, and the ability to move and to manipulate objects. In the field of artificial intelligence there is no consensus on how closely the brain should be simulated.
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- History of Influences in the Development of Intelligence Theory and Testing – Developed by Jonathan Plucker at Indiana University
- The Limits of Intelligence: The laws of physics may well prevent the human brain from evolving into an ever more powerful thinking machine by Douglas Fox in Scientific American, 14 June 2011.
- A Collection of Definitions of Intelligence
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