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, through the analysis of the division of different scientific fields, we deal with the nature of mathematics as a scientific discipline. Through the historical analysis of the division of science, but also the analysis of the nature of mathematics and the ontological status of the objects that mathematics deals with, we show that the now-established divisions among scientific fields are the result of social circumstances and that mathematics itself is closer to the humanities than the natural sciences.

Machine learning and essentialism have been connected in the past by various researchers, in order to state that the main paradigm in machine learning processes is equivalent to choosing the “essential” attributes for the machine to search for. Our goal in this paper is to show that there are connections between machine learning and essentialism, but only for some kinds of machine learning, and often not including deep learning methods. Similarity-based approaches, more connected to the overall prototype theory, spanning from (...) psychology and linguistics, seem more suited for pattern recognition and complex deep-learning issues, while for classification problems, mostly for unsupervised learning, essentialism seems like the best choice. In order to illustrate the difference better, we will connect both paths to their sources in other disciplines and see how human psychology influences our decision in machine-learning modeling as well. This leads to a philosophically very interesting consequence: even in the setting of supervised machine learning, essences are not present in data, but in targets, which in turn means that the categories which purport to be essences are in fact human-made, and hand-coded in the targets. The success of machine learning, therefore, does not give any substantial evidence for the independent existence of essential properties. Our stance here is to state that neither the existence nor the lack of “essential” properties in machine learning can lead to metaphysical, i.e., ontological claims. (shrink)

In this paper, we explore the possibility of applying traditional and modern aesthetical theories to logical and mathematical proofs, with the goal of better understanding the intuitive concept of mathematical beauty. This informal concept takes a central role in the work of logicians and mathematicians and can be thought of as their main motivation. In the present paper, we try to define concepts connected to mathematical beauty or beauty in mathematical proofs, so that we may lay the foundations for a (...) more precise definition of mathematical beauty which would be obtained through a detailed survey among logicians and mathematicians, presented in a future paper. The present paper brings crucial results to be used for constructing the survey. (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)

U radu se ispituju filozofski temelji dubokog učenja. Ukazivanjem na početke dubokog učenja i umjetnog neurona kao formalnog modela ljudskog neurona moguće je tvrditi da je umjetna inteligencija razvijena i prije njezinog službenog imenovanja te da je bila snažno povezana s propozicionalnom logikom. Imajući na umu neke velike zastoje u razvoju neuronskih mreža, pokazujemo da se dubinsko učenje može tretirati kao teorija umjetne inteligencije te da potpada pod paradigmu umjetne inteligencije jer je za nju dovoljno samo učenje jer se inteligentno (...) ponašanje uči. Dakle, duboko učenje je filozofski ili epistemološki pristup u kojem se mora zagovarati radikalni empirizam. Prema tome, ne samo da ne postoji ništa u umu što nije bilo u osjetilima, već u umu ne postoji ništa što se ne može naučiti. (shrink)

U ovom članku predstavljamo Da Costine sustave Cω i C1 . Da bismo ilustrirali specifična svojstva ovih sustava, koristimo mnogobrojne primjere te iznosimo poznatu konstrukciju kvazimatrica. Uz konstrukciju, dajemo svoj dokaz adekvatnosti kvazimatrica u C1, pri čemu je ovaj dokaz moguće proširiti na cijelu Cn hijerarhiju.