Mark Ressler

This paper reviews arguments concerning the relation between relativism and tolerance, both whether tolerance entails relativism, and whether relativism entails tolerance. Two new arguments are offered to support the contention that there is no necessary relation between relativism and tolerance. In particular, building on the classic argument by Geoffrey Harrison, this paper argues that even if there is no strict dichotomy between facts and values, as Harrison had assumed, relativism still does not entail tolerance for every relativized perspective.

The consequences of Quine's criterion of ontological commitment epitomized in his treatment of the term 'Pegasus' in "On What There Is" are evaluated in terms of Quine's own work, in particular in "The Variable" and "Variables Explained Away". There is a cost to maintaining this criterion with regard to the empirical consequences of some non-existent objects, given considerations prompted by Quine's holism. This cost can be reduced by adopting a noneist position according to which non-existent objects can be values of bound variables as well.

In the Categories, Aristotle addresses two different cases of dual categorization, cases in which the same thing might appear in two different categories: relatives and secondary substances in the first case, qualities and relatives in the second. His treatment of these two cases is markedly different. Ackrill thinks dual categorization poses a dilemma for Aristotle’s project as a whole, but I argue that there is a dilemma only on particular understandings of Aristotle’s purpose in compiling the list of categories. I investigate various interpretations of the categories to find one that explains Aristotle’s reactions to dual categorization, and suggest an interpretation of the peculiar four-fold system of classification in Chapter 2.

Connectionism seems to avoid many of the problems of classical artificial intelligence, but has it avoided all of them? In this thesis I examine the problem that Intentionality, the directedness of thought to an object, raises for connectionism. As a preliminary approach, I consider the role of Intentionality in classical artificial intelligence from the programmer’s point of view. In this investigation, one problem I identify with classical artificial intelligence is that the Intentionality of the programmer seems to be projected onto the system, rather than the programmer creating a system whereby Intentionality arises intrinsically within the system. In considering the current practice of connectionism, the same problem with Intentionality reappears. The assignment of Intentional content to input or output nodes in a neural network likewise projects the Intentionality of the programmer onto the system, and that projection is often reinforced by the training process of the neural network. However, connectionism seems to have an advantage over classical artificial intelligence in this respect, in that there is a form of neural network in which the network itself organizes the output nodes. The challenge is to utilize these self-organizing networks without having the programmer project Intentionality onto the system in an act of interpretation. I suggest that the way to overcome these problems in connectionism is to embody the neural network within a world, whether physical or virtual, and to allow the system to develop concepts purely empirically. In this manner, connectionism can make scientific observations concerning the nature of cognition without the risk of contamination from the programmer’s own Intentionality.

There seems to be a difficulty in the practice of metaphysics, in that any methodology used in metaphysical study relies on certain presuppositions, whereby it seems that metaphysical results are relative to those presuppositions. What is needed is a methodology that can yield objective metaphysical results that are not limited by the presuppositions of that methodology. This paper argues for a way to triangulate on stable metaphysical results by using existing methodologies as perspectives on metaphysical topics, and by reducing the differences between those perspectives to non-perspectival truths, if possible.

This in-depth study aims to develop a rigorous analysis of the nature and the logic of relativism in general as a basis for evaluating the charge of self-refutation against relativism. It develops a general definition of relativism that distinguishes relativism from structurally similar notions such as conventionalism and contextualism. On the basis of this definition, it formulates a series of logical systems that each might be presented as candidates for the logic of relativism. Each system is evaluated to see whether it can sustain the charge of self-refutation. The result is that one of these systems can be proven not to be self-refuting, even under increasingly stronger challenges. Consequently, this study argues that even global relativism can be demonstrated not to refute itself, despite the long history of arguments to the contrary.

Large language models and other generative artificial intelligence systems are achieving increasingly impressive results, though the quality of those results still seems dull and uninspired. This paper argues that this poor quality can be linked to the philosophical notion of inauthenticity as presented by Kierkegaard, Nietzsche, and Heidegger, and that this inauthenticity is fundamentally grounded in the design and structure of such systems by virtue of the way they statistically level down the materials on which they are trained. Although it seems possible to create the conditions for authenticity in these systems, the resulting authenticity would be grounded in machine intelligence, not human intelligence. The argument extends the criticisms of artificial intelligence articulated by Hubert Dreyfus, updated to account for recent developments in machine learning and artificial neural networks. While more optimistic concerning the prospects for successfully creating artificial intelligence than Dreyfus had been, this paper argues that the resulting intelligence may not align well with human intelligence and may not be desirable for humans, if it is fully authentic.