Module 06-02483 (2011)

• present an appropriate methodology for the identification and resolution of the philosophical problems that inescapably arise in some of the component disciplines of Cognitive Science (including elements of Artificial Intelligence and Computer Science)
• give examples of how philosophical theories influence and sometimes distort work being done, both theoretical and also practical software development, in some of the component disciplines of Cognitive Science (including elements of Artificial Intelligence and Computer Science)
• present and discuss some of the philosophical problems that arise in the mathematical and theoretical foundations of some of the component disciplines of Cognitive Science (including elements of Artificial Intelligence and Computer Science)

• use an appropriate methodology for the identification of philosophical problems that inescapably arise in Cognitive Science
• use an appropriate methodology for the resolution of philosophical problems that occur in Cognitive Science
• put into practice a variety of methods for the criticism of rival theories
• identify the influence of philosophical theories on theoretical work being done in Cognitive Science
• identify the influence of philosophical theories on practical software development being done in Cognitive Science
• relate foundational issues in Cognitive Science to more practical work being done there, including programming and software development

Ten 1 hr weekly lectures.

• Sessional: Continuous assessment (100%).
• Supplementary: Repeat only.

1. Introduction: assessment; style of presentation; philosophical background; methodology; content and topics; treat the essay as a mini-project; possible case studies to be used throughout the module (Can computers think?',Is the Turing test adequate to determine whether computers can think?', Can physical systems think?',Can Chinese rooms think?', Can connectionist networks think?',Can computers think in images?', Do computers have to be conscious to think?',Are thinking computers mathematically possible?').
2. Disciplines: why what-is questions are best avoided; academic disciplines are useful for administrators but not researchers; essentialism and nominalism; theories and larger units (scientific research programmes, paradigms, research traditions); the computational-representational understanding of mind; the CRUM research programme; metaphysical research programmes.
3. Philosophy: rationale for studying; methodological rules; bad methodology (what-is questions, definition, premature implementation, induction); good methodology (nominalism, proliferation, anti-justificationism, thought-out implementation, the method of multiple working hypotheses and pluralism); background; epistemology (the bucket theory of the mind, the belief-filter component of an android, testimony).
4. Definitions: terminology, what-is questions; essentialism; real; abbreviatory; bad advice 'Define your terms!'; etymological fallacy; family resemblance; Popper's table of ideas; infinite regress.
5. Problems: psychology and AI; inconsistencies; facts; difficulties; questions; search problems; philosophical (change, personal identity, body-mind, other minds, universals, testimony, free will); Tye's (ownership, mechanism, phenomenal causation, duplicates); epistemological (justificationist, anti-justificationist, belief-filter component); non-philosophical (practical, theoretical, historical, mathematical); methodological advice; erotetic narratology.
6. Philosophy of science and creativity: problems as starting points; Popper's tetradic schema; creativity (Hadamard, Evans); context of discovery and criticism; the myth of induction.
7. Explanation and prediction: covering law model; poor methodology; falsification as good methodology: `We predict by reference to our present theories; we learn by refuting our present theories' (Bartley).
8. Philosophy of mind: mind-body problem; Popper's three worlds; Tye's ten problems of consciousness; functionalism; closed-world assumption; hierarchical organisation of reality; reduction; emergence; upward and downward causation; evolution.
9. Belief-filter component: ultimate goal of AI; philosophical problem of testimony; rationalism (uncritical or comprehensive, pancritical); Reid's principle of credulity; Price's principle of trust; the strategy of attacking foundations and Searle's use of; how not to win an argument (Gilbert); infinite regress; ultimate commitment; irrationalism and relativism.