Artificial Intelligence and Compatibilism: Possibility of Emergence of the Free Mind in the Determined Body

Ovaj rad istražuje mogućnost davanja kompatibilističkog argumenta iz aspekta umjetne inteligencije. Ključna pretpostavka našeg rada jest da je umjetna inteligencija načelno moguća i da se realizira na računalnim arhitekturama u bitnome nalik današnjim. Uz taj je uvjet moguće dati definiciju slobode koja je pomirljiva s determiniranim izračunom, uz pomoć načelne nedokučivosti inteligentnog procesa. Ovo se temeljem funkcionalizma može translatirati u filozofiju (ljudskog) uma. Pitanje je li moguće naš argument adaptirati za drugačije teorije filozofije uma ostavljamo otvorenim.This paper explores the possibility of a compatibilistic argument from the aspect of artificial intelligence. A key assumption for our argument is that artificial intelligence is in principle possible and that it is realized on computer architectures similar to today’s architectures. With these assumptions, it is possible to give a definition of freedom which is compatible with a deterministic calculation, by using unattainableness of intelligent process computation. By using functionalism as a background theory, this can be translated in philosophy of (the human) mind. The question whether our argument is adaptable to different theories in philosophy of mind is left open

Prolegomena filozofijskog utemeljenja dubokog učenja kao teorije (umjetne) inteligencije

This paper examines the philosophical foundations of deep learning. By pointing to the beginnings of deep learning and artificial neuron as a logical model of a human neuron, it is possible to claim that artificial intelligence was developed even before its official creation and that it was strongly connected to propositional logic. Bearing in mind some major setbacks in the development of neural networks, we show that deep learning can be treated as the theory of artificial intelligence and that it falls under artificial intelligence paradigm by claiming that everything can be done with learning alone and that all intelligent behavior is learnable. Thus, deep learning is a philosophical or an epistemological approach in which a form of radical empiricism must be advocated. Therefore, there is nothing in the mind that was not in the senses, and there cannot be anything in the mind that is not learnable.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

Formalna nekonzistentnost i kvazimatrice

U ovom članku predstavljamo Da Costine sustave Cω i C1 (1974: 497– 510). Da bismo ilustrirali specifična svojstva ovih sustava, koristimo mnogobrojne primjere te iznosimo poznatu konstrukciju kvazimatrica. Uz konstrukciju, dajemo svoj dokaz adekvatnosti (pouzdanosti) kvazimatrica u C1, pri čemu je ovaj dokaz moguće proširiti na cijelu Cn hijerarhiju

An Application of Fuzzy Inductive Logic Programming for Textual Entailment and Value Mining

The aim of this preliminary report is to give an overview of textual entailment in natural language processing (NLP), to present our approach to research and to explain the possible applications for such a system. Our system presupposes several modules, namely the sentiment analysis module, the anaphora resolution module, the named entity recognition module and the relationship extraction module. State-of-the-art modules will be used but no amount of research will go into this. The research focuses on the main module that extracts background knowledge from the extracted relationships via resolution and inverse resolution (inductive logic programming). The last part focuses on possible economic applications of our research

Croatian Emotional Speech Analyses on a Basis of Acoustic and Linguistic Features

Acoustic and linguistic speech features are used for emotional state estimation of utterances collected within the Croatian emotional speech corpus. Analyses are performed for the classification of 5 discrete emotions, i.e. happiness, sadness, fear, anger and neutral state, as well as for the estimation of two emotional dimensions: valence and arousal. Acoustic and linguistic cues of emotional speech are analyzed separately, and are also combined in two types of fusion: a feature level fusion and a decision level fusion. The Random Forest method is used for all analyses, with the combination of Info Gain feature selection method for classification tasks and Univariate Linear Regression method for regression tasks. The main hypothesis is confirmed, i.e. an increase of classification accuracy is achieved in the cases of fusion analyses (compared with separate acoustic or linguistic feature sets usages), as well as a decrease of root mean squared error when estimating emotional dimensions. Most of other hypothesis are also confirmed, which suggest that acoustic and linguistic cues of Croatian language are showing similar behavior as other languages in the context of emotional impact on speech

Analogical Reasoning and Word-Meanings in a Multidimensional Space

Rad istražuje temeljnu misao i pretpostavku simboličke logike oko pojmova kao atomarnih komponenti (uvedenih prilikom definiranja sustava i koji se ne mogu dalje razlagati), i uvodi drugačiji formalizam, baziran na umjetnim neuralnim mrežama za formalizaciju logičkog zaključivanja kao kognitivnog procesa, što definira pristup koji nazivamo subsimboličkom logikom primijenjenoj na analogijsko zaključivanje kao punopravnom obliku zaključivanja. Istražujemo i kognitivne aspekte takvog pristupa, posebice u kontekstu izolacije i reprodukcije spontanih, ali neispravnih formi zaključivanja (logičkih pogreški) svojstvenih logičkom zaključivanju kao kognitivnom procesu. Ovo je danas dominantna tehnika u umjetnoj inteligenciji, no filozofijske su posljedice ovog pristupa u potpunosti neistražene. Prema našim spoznajama, ovo je prvi pokušaj da se uz pomoć umjetnih neuralnih mreža analizira fenomen analogijskog zaključivanja.The present work explores the underlying thought behind symbolic logic which accepts concepts as atomic components, and we introduce a different formalism based on artificial neural networks for the formalization of logical reasoning as a cognitive process, which defines an approach we call subsymbolic logic. We apply this approach to analogical reasoning, which we argue is the proper reasoning. We also explore the cognitive aspects of this approach, especially in isolating and reproducing spontaneous but erroneous forms of reasoning (cognitive biases) which are a part of logical reasoning viewed as a cognitive process. Today, it is the dominant technique in artificial intelligence, but the philosophical aspects of such an approach remain mostly unexplored. To the best of our knowledge, this is the first such attempt at using artificial neural networks to analyse analogical reasoning

The Possibility of Applying Traditional and Modern Aesthetical Theories to Logical and Mathematical Proofs

U ovom radu istražujemo mogućnost primjene tradicionalnih i suvremenih estetičkih teorija na logičko-matematičke dokaze, s ciljem boljeg razumijevanja intuitivnog pojma matematičke ljepote. Ovo je neformalan pojam koji zauzima središnje mjesto u radu logičara i matematičara i može se smatrati njihovom glavnom motivacijom. U ovom radu pokušavamo definirati pojmove vezane uz matematičku ljepotu, odnosno ljepotu u matematičkim dokazima, da bismo postavili temelje za preciznu definiciju matematičke ljepote koju bismo dobili preko detaljnog anketiranja logičara i matematičara, a što bismo proveli u odvojenom radu. Ovaj rad donosi važne rezultate za izradu te ankete.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