Computational complexity has often been ignored in the philosophy of mind, in philosophical artificial intelligence studies. The purpose of this paper is threefold. First and foremost, to show the importance of complexity rather than computability in philosophical and AI problems. Second, to rephrase the notion of computability in terms of solvability, i.e., treating computability as non-sufficient for establishing intelligence. The Church-Turing thesis is therefore revisited and rephrased in order to capture the ontological background of spatial and temporal complexity. Third, to (...) emphasize ontological differences between different time complexities, which seem to provide a solid base for a better understanding of artificial intelligence in general. (shrink)

Cyberspeak, the language of cybernetics, or its metalanguage to be more precise, consists of words that are both explaining and describing human/animal and machine forms of control and communication, while in newspeak, words were value-laden, which means they had strong positive or negative connotations connected to their use. For example, a 'spy' could only be a foreign agent, while a Russian one was a 'patriot'. First, it will be shown how there are still remnants of cyberspeak in modern science, pinpointing (...) its cybernetic background and shaping our thoughts without us realizing they had any cybernetic origin whatsoever. Second, it will be investigated how newspeak, as its counterpart, can be analyzed from the theory of speech acts. Third, the current war in Ukraine allowed us a brief analysis of newspeak present in Russian public communication today (Putin's speeches), which will again be connected to their performative aspect. (shrink)

In this paper we propose an non-machine learning artificial intelligence (AI) based approach for telecom data analysis, with a special focus on clique detection. Clique detection can be used to identify households, which is a major challenge in telecom data analysis and predictive analytics. Our approach does not use any form of machine learning, but another type of algorithm: satisfiability for propositional logic. This is a neglected approach in modern AI, and we aim to demonstrate that for certain tasks, it (...) may be a good alternative to machine learning-based approaches. We have used a simple DPLL satisfiability solver over an artificially generated telecom dataset (due to GDPR regulations), but our approach can be implemented on any telecom data by following the SAT encoding we have developed, and the DPLL solver can be substituted by a more advanced alternative such as CDCL. This paper extends the method presented in [1] for banking logs to data containing caller information, and proposes a more efficient encoding. -/- . (shrink)