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Relevance and artificial intelligence.

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Τίτλος: Relevance and artificial intelligence.
Συγγραφείς: Saariluoma, Pertti, Rauterberg, Matthias
Πηγή: Discover Artificial Intelligence; 8/10/2026, Vol. 6 Issue 1, p1-24, 24p
Θεματικοί όροι: Artificial intelligence, Cognitive ability, Natural languages, Connotation (Linguistics), Formal languages, Programming languages, Cognition
Περίληψη: This article explores the distinct characteristics of human intelligence in comparison to Artificial Intelligence (AI), highlighting the concepts of relevance and meaning giving as fundamental properties of intelligent thought and action. Highlights the limitations of human cognition, such as limited time, computational power, and communication, which shape human intelligence. The text argues that relevance is crucial for understanding human and AI intelligence, as it determines the meaningfulness of actions and thoughts in various contexts. The article also explores the distinctions among formal languages, computational languages, and natural languages, asserting that formal languages cannot express relevance because of their formal nature. It emphasises the importance of human mental processes in assigning meaning to information and the need for relevance in AI systems for practical applications. Furthermore, the article examines the role of physical symbol systems in modelling human thought and the limitations of AI in replicating human-like intelligence. It critiques the assumptions underlying AI, such as the analogy between brain processes and digital computation, and discusses the challenges of defining relevance in mathematical and formal systems. Ultimately, the article concludes that relevance is essential for the development of effective AI systems, as it guides the selection of pertinent information and actions in specific contexts. It posits that, while the number of computational devices is vast, the construction of general AI remains unattainable because it requires defining specific semiotic action spaces for meaningful operations. [ABSTRACT FROM AUTHOR]
Copyright of Discover Artificial Intelligence is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων: Complementary Index
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  – Url: https://dx.doi.org/doi:10.1007/s44163-026-01936-3
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  Data: Discover Artificial Intelligence; 8/10/2026, Vol. 6 Issue 1, p1-24, 24p
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  Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+ability%22">Cognitive ability</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+languages%22">Natural languages</searchLink><br /><searchLink fieldCode="DE" term="%22Connotation+%28Linguistics%29%22">Connotation (Linguistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Formal+languages%22">Formal languages</searchLink><br /><searchLink fieldCode="DE" term="%22Programming+languages%22">Programming languages</searchLink><br /><searchLink fieldCode="DE" term="%22Cognition%22">Cognition</searchLink>
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  Data: This article explores the distinct characteristics of human intelligence in comparison to Artificial Intelligence (AI), highlighting the concepts of relevance and meaning giving as fundamental properties of intelligent thought and action. Highlights the limitations of human cognition, such as limited time, computational power, and communication, which shape human intelligence. The text argues that relevance is crucial for understanding human and AI intelligence, as it determines the meaningfulness of actions and thoughts in various contexts. The article also explores the distinctions among formal languages, computational languages, and natural languages, asserting that formal languages cannot express relevance because of their formal nature. It emphasises the importance of human mental processes in assigning meaning to information and the need for relevance in AI systems for practical applications. Furthermore, the article examines the role of physical symbol systems in modelling human thought and the limitations of AI in replicating human-like intelligence. It critiques the assumptions underlying AI, such as the analogy between brain processes and digital computation, and discusses the challenges of defining relevance in mathematical and formal systems. Ultimately, the article concludes that relevance is essential for the development of effective AI systems, as it guides the selection of pertinent information and actions in specific contexts. It posits that, while the number of computational devices is vast, the construction of general AI remains unattainable because it requires defining specific semiotic action spaces for meaningful operations. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Discover Artificial Intelligence is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: 8/10/2026
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