LPHIL7254 Minds and Machines Course Descriptor

Course Overview

This course investigates the ontological, epistemological, and methodological dimensions of issues that emerge in relation to systems that exhibit intelligent behaviour, whether these are biological or artificial. Students will learn about the main theories of mind and  the way these theories enrich our understanding of intelligent machines. Conversely, students will consider how advances in artificial intelligence throw light on the human mind. IThe course will address questions such as: What is the correct theory of mind? Do mental states reduce to brain states? Does the mind extend beyond the confines of our heads? What is computation? Does the mind compute in a similar way to machines? Under what conditions can we say that a physical system computes? What is intelligence? Is thinking more than mere intelligence? Can we standardise intelligence tests for both machines and humans? Are we also machines of some sort? What can we know about the world around us? Is the universe just a massive computer simulation? 

Learning Outcomes

On successful completion of the course, students will be able to:

Knowledge and Understanding

Subject-Specific Skills

Transferable and Employability Skills

Teaching and Learning

This course has a dedicated Virtual Learning Environment (VLE) page with a syllabus and a range of additional resources (e.g. readings, question prompts, tasks, assignment briefs, and discussion boards) to orientate and engage students in their studies.

Teaching and learning strategies for this course will include:

• Lectures: Instructor-led classes.
• Seminars/workshops: Interactive sessions on project management principles, focused on applying theoretical concepts.
• Experiential Learning, which may include simulations and role-playing for hands-on experience, or guest speakers for insight from professionals.
• Online Resources: Flexible learning with additional study materials.

Faculty hold regular ‘office hours’, which are opportunities for students to drop in or sign up to explore ideas, raise questions, or seek targeted guidance or feedback, individually or in small groups.

Students are to attend and participate in all the scheduled teaching and learning activities for this course and to manage their directed learning and independent study.

Indicative total learning hours for this course: 150, including a minimum of 16.5 scheduled hours

Employability Skills

The study of philosophy cultivates skills that are employable across a range of sectors. These include the abilities to:

• Work independently, creatively, and to deadlines.
• Conduct research and explore relevant existing knowledge.
• Analyse, contextualise, and interpret complex ideas and materials.
• Synthesise and evaluate information against a backdrop of uncertainty.
• Solve problems through logical reasoning.
• Present findings and opinions in a clear, structured manner, whether orally or in writing.
• Engage in collaborative and constructive discussion.

Assessment

Formative

Students will be formatively assessed during the course by means of one or more set assignments, which may take the form of essays, case studies, group work or reports, among other types of assessment. These do not count towards the end of year results, but will provide students with developmental feedback, both written and oral.

Summative

Assessment will be in one form:

Feedback

Students will receive feedback in a variety of ways, written and oral, within one-to-one tutorials, in discussion phases of lectures, and on formatively and summatively assessed assignments.

Indicative Reading

Note: Comprehensive and current reading lists are produced annually in the Course Syllabus or other documentation provided to students; the indicative reading list provided below is for a general guide and part of the approval/modification process only.

Boden, M. (ed.) (1990) The Philosophy of Artificial Intelligence, Oxford: Oxford University Press.

Bostrom, N. (2014) Superintelligence: Paths, Dangers and Strategies, Oxford: Oxford University Press.

Clark, A. (2008) Supersizing the Mind: Embodiment, Action, and Cognitive Extension, New York: Oxford University Press.

Copeland, B. (2000) ‘The Turing Test’, Minds and Machines, vol. 10 (4):519-539.

Dennett, D. C. (2013) ‘The Seven Secrets of Computer Power Revealed’, in Intuition Pumps and other Tools for Thinking, New York: W. W. Norton & Company.

Egan, F. (2019) ‘The Nature and Function of Content in Computational Models’, in M. Sprevak and M. Colombo (eds.), The Routledge Handbook of the Computational Mind, New York: Routledge, pp. 247–258.

Heil, J. (2004) Philosophy of Mind: A Contemporary Introduction, London: Routledge.

Leonelli, S. (2016). Data-centric biology: A philosophical study. Chicago: University of Chicago Press.

Putnam, H. (1975) Mind, Language, and Reality: Philosophical Papers, vol. 2, Cambridge: Cambridge University Press.

Russell, S. and P. Norvig (2009) Artificial Intelligence: A Modern Approach, 3rd edition, Saddle River, NJ: Prentice Hall.

Searle, J. (1980) ‘Minds, Brains, and Programs’, Behavioral and Brain Sciences, vol. 3: 417–457.

Shanahan, M. (2015) The Technological Singularity, Cambridge, MA: MIT Press

Turing, A. M. (1950) ‘Computing Machinery and Intelligence’, Mind, vol. 59, 433-460.

Indicative Topics

• General Theories of Mind
• Extended Cognition
• Computationalism
• Human and Machine Intelligence
• The Turing Test
• Transhumanism
• Scepticism and Computer Simulations

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