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The thesis develops solutions to two main problems for mental realism. Mental realism is the theory that mental properties, events, and objects exist, with their own set of characters and causal powers. The first problem comes from the philosophy of science, where Psillos proposes a notion of scientific realism that contradicts mental realism, and consequently, if one is to be a scientific realist in the way Psillos recommends, one must reject mental realism. I propose adaptations to the conception of scientific realism to make it compatible with mental realism. In the process, the thesis defends computational cognitive science from a compelling argument Searle can be seen to endorse but has not put forth in an organized logical manner. A new conception of scientific realism emerges out of this inquiry, integrating the mental into the rest of nature. The second problem for mental realism arises out of non-reductive physicalism- the view that higher-level properties, and in particular mental properties, are irreducible, physically realized, and that physical properties are sufficient non-overdetermining causes of any effect. Kim’s Problem of Causal Exclusion aims to show that the mental, if unreduced, does no causal work. Consequently, given that we should not believe in the existence of properties that do not participate in causation, we would be forced to drop mental realism. A solution is needed. The thesis examines various positions relevant to the debate. Several doctrines of physicalism are explored, rejected, and one is proposed; the thesis shows the way in which Kim’s reductionist position has been constantly inconsistent throughout the years of debate; the thesis argues that trope theory does not compete with a universalist conception of properties to provide a solution; and shows weakness in the Macdonald’s non-reductive monist position and Pereboom’s constitutional coincidence account of mental causation. The thesis suggests that either the premises of Kim’s argument are consistent, and consequently his reductio is logically invalid, or at least one of the premises is false, and therefore the argument is not sound. Consequently, the Problem of Causal Exclusion that Kim claims emerges out of non-reductive physicalism does not force us to reject mental realism. Mental realism lives on.

While a symbol based system externally, there is evidence that, internally the realization of language is much different. Through revisiting the foundations of our perceptions and assumptions about language and cognition, the presented argument will coalesce into an illustration of the unsuitability of symbolic systems for recreating the functions which we call "mind". Simply stated, computational models of mind are the latest arguments of the Cartesian paradigm. The thesis concludes with an argument for the exploration of a symbol-less architecture of cognition based upon a model found repeatedly throughout nature: swarms. Discussions of some of the impacts are presented.

The reemergence of artificial intelligence during the last 30 years has introduced severalforms of weak AI to our everyday lives, be it in our smartphones or how the weather ispredicted. Modern approaches to AI, using methods like neural networks and machinelearning, also feel confident about creating strong AI, intelligence that is human-like orsuperior to humans. In this thesis, I discover the philosophical premises of artificialintelligence, how the research program views the mind and what implications this has for theform of intelligence that is being constructed. Furthermore, I derive at several criteria thatneed to be met to qualify a system as intelligent. To cover this rather wide field, the works ofHubert Dreyfus, an early commentator on AI, and David Chalmers, one of the most widelyread philosophers of mind, are interrogated about their views on human intelligence and howsuch a theory relates to the possibility of intelligent machines.Key

The Chinese room argument (CRA) was first stated in 1980. Since then computer technologies have improved and today spiking neural networks (SNNs) are “arguably the only viable option if one wants to understand how the brain computes.” (Tavanei et.al. 2019: 47) SNNs differ in various important respects from the digital computers the CRA was directed against. The objective of the present work is to explore whether the CRA applies to SNNs. In the first chapter I am going to discuss computationalism, the Chinese room argument and give a brief overview over spiking neural networks. The second chapter is going to be considered with five important differences between SNNs and digital computers: (1) Massive parallelism, (2) subsymbolic computation, (3) machine learning, (4) analogue representation and (5) temporal encoding. I am going to finish by concluding that, besides minor limitations, the Chinese room argument can be applied to spiking neural networks.:1 Introduction 2 Theoretical background 2.I Strong AI: Computationalism 2.II The Chinese room argument 2.III Spiking neural networks 3 Applicability to spiking neural networks 3.I Massive parallelism 3.II Subsymbolic computation 3.III Machine learning 3.IV Analogue representation 3.V Temporal encoding 3.VI The Morse code room and its replies 3.VII Some more general considerations regarding hardware and software 4 Conclusion / Das Argument vom chinesischen Zimmer wurde erstmals 1980 veröffentlicht. Seit dieser Zeit hat sich die Computertechnologie stark weiterentwickelt und die heute viel beachteten gepulsten neuronalen Netze ähneln stark dem Aufbau und der Arbeitsweise biologischer Gehirne. Gepulste neuronale Netze unterscheiden sich in verschiedenen wichtigen Aspekten von den digitalen Computern, gegen die die CRA gerichtet war. Das Ziel der vorliegenden Arbeit ist es, zu untersuchen, ob das Argument vom chinesischen Zimmer auf gepulste neuronale Netze anwendbar ist. Im ersten Kapitel werde ich den Computer-Funktionalismus und das Argument des chinesischen Zimmers erörtern und einen kurzen Überblick über gepulste neuronale Netze geben. Das zweite Kapitel befasst sich mit fünf wichtigen Unterschieden zwischen gepulsten neuronalen Netzen und digitalen Computern: (1) Massive Parallelität, (2) subsymbolische Berechnung, (3) maschinelles Lernen, (4) analoge Darstellung und (5) zeitliche Kodierung. Ich werde schlussfolgern, dass das Argument des chinesischen Zimmers, abgesehen von geringfügigen Einschränkungen, auf gepulste neuronale Netze angewendet werden kann.:1 Introduction 2 Theoretical background 2.I Strong AI: Computationalism 2.II The Chinese room argument 2.III Spiking neural networks 3 Applicability to spiking neural networks 3.I Massive parallelism 3.II Subsymbolic computation 3.III Machine learning 3.IV Analogue representation 3.V Temporal encoding 3.VI The Morse code room and its replies 3.VII Some more general considerations regarding hardware and software 4 Conclusion

Le comportement moteur est un phénomène où les différentes composantes d’un système biologique sont organisées de façon à assurer la coordination d’un mouvement intentionnel. Selon les théories computationnelles, le comportement est défini comme un problème moteur dont la solution peut être trouvée par des systèmes divisés de manière hiérarchique. Les composantes traitent et communiquent entre elles de l’information représentant les aspects pertinents du problème moteur (positions, trajectoires, vitesses, forces, etc.) lesquels sont censés être organisés à leur tour selon une hiérarchie d’abstraction et de complexité ascendante. Le défi est de faire face à quatre problèmes centraux du comportement : a) Le nombre élevé de degrés de liberté et d’interactions ; b) La redondance des degrés de liberté ; c) L’anticipation des effets du mouvement ; d) L’incertitude dans l’information. Les théories computationnelles classiques proposent des schémas explicatifs composés d’un agencement de différents modèles internes (prospectifs et inverses). Plus récemment, l’approche bayésienne propose un schéma hiérarchique plus homogène lequel est censé faire face aussi à l’incertitude de l’information. Cette recherche démontre que les théories computationnelles, y compris l’approche bayésienne, sont paralysées par un dilemme insurmontable : soit elles peuvent passer à l’échelle de manière computationnellement faisable - les calculs peuvent être réalisés en un temps raisonnable - mais dans ce cas elles ne peuvent pas reproduire la versatilité caractéristique du comportement des êtres vivants ; soit elles aspirent à reproduire la versatilité biologique mais alors elles sont infaisables. / Motor behavior is a phenomenon where the components making up a biological system are organized so as to ensure the coordination of a purposeful movement. According to computational theories, behavior is defined as a motor problem the solution of which can be found by systems divided hierarchically. The components process and communicate information representing the relevant variables of the motor problem (positions, trajectories, velocities, forces, etc.) which are, in turn, assumed to be organized as a hierarchy of increasing abstraction and complexity. The challenge is to tackle the four core problems of behavior: a) The high number of degrees of freedom and their interactions; b) The redundancy of degrees of freedom; c) The anticipation of the effects of movement; d) The uncertainty in information. Classical computational theories advance explanatory schemas made of structured sets of internal models (forward and inverse). More recently, the Bayesian approach advances a more homogeneous hierarchical schema which is supposed to account for uncertainty in information. This research shows that computational theories, including the Bayesian approach, are crippled by an unsolvable dilemma: The first horn is that if the models can scale up while staying computationally tractable, i.e. the computations can be carried out in a reasonable amount of time, then they fail to reproduce the versatility which characterizes the behavior of living beings. The second horn is that if the models aspire to reproduce biological versatility, then they are intractable.