PP1MM-Mental Machines
Module Provider: Philosophy
Number of credits: 10 [5 ECTS credits]
Level:4
Terms in which taught: Summer term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2020/1
Email: n.d.hansen@reading.ac.uk
Type of module:
Summary module description:
This module investigates the interface between philosophy, cognitive science, and computer science. We will look at the hypothesis that the human mind is a computational system, as assumed by most contemporary work in cognitive science. We will ask: if the human mind is a computer, what kind of computer is it? Is it a classical computer, operating on structured representations in line with semi-logical rules of inference? Or is it more akin to contemporary ‘Deep Learning’ systems, trained on Big Data? We will consider substantial problems that arise for both such views, based on tasks that humans perform relatively easily, but on which computers are, as of yet, unable to perform successfully.
We will read the works of philosophers such as Ned Block, Dan Dennett, and Jerry Fodor, as well as scientists like Pedro Domingos and Gary Marcus.
Reading:
Required readings will be posted online.
Recommended:
Tim Crane, The Mechanical Mind, Routledge 2003.
Hubert Dreyfus, What Computers Still Can't Do, MIT Press 1992.
John Searle, 'Minds, brains, and programs’. Behavioral and Brain Sciences 3: 417-457, 1980
Aims:
Students in this module will learn to engage knowledgeably, critically and rigorously with the complex and pressing issues about artificial intelligence which face contemporary society. They will learn how the tools of philosophy can illuminate scientific problems, and they will be introduced to some central questions in philosophy of mind. They will learn to formulate precise arguments about these problems and questions, both orally and in writing. This module will prepare students for further Philosophy modules at Parts 2 and 3, by developing critical skills required in all Philosophy modules, as well as through subject knowledge which will be especially helpful in Meaning and the Mind (PP2MM) and The Science of Consciousness (PP3SC).
Assessable learning outcomes:
Students in this module will acquire subject knowledge in the philosophy of mind and artificial intelligence, by engaging with cutting-edge research in these disciplines. In addition, they will learn the skill of formulating precise, convincing philosophical arguments about scientific problems. They will learn how to communicate these arguments effectively in discussion and in writing, and how to criticise such arguments effectively and constructively, engaging effectively with their peers. Fina
lly, in-class presentations will give students a chance to learn about presenting themselves and their ideas effectively.
Additional outcomes:
Students in this module will develop an appreciation of how philosophy can engage effectively with the sciences, and of an appreciation of how philosophy can engage effectively with pressing practical issues facing society the world over. Students will be exposed to written work in diverse philosophical and scientific styles and traditions, learning how to translate efficiently between them.
The metaphor of the mind as a machine has been guiding naturalistic investigation of the mind for centuries. However, developments in the theory of computation in the 20th Century have made possible the idea that this claim may be literally true, not merely metaphorically suggestive. That is, the mind may be a machine. More precisely, the mind may be a computer: a device for processing and manipulating the flow of environmental information. We will look at the classical approach to cognitive science, computationalism, which aims to understand the mind precisely in these terms. We will then turn to perhaps the central problem for computational theories of mind: the frame problem. This is, roughly, the problem of figuring out the relevance of novel information to previously stored knowledge. We will then turn to a radical alternative to the classical approach to computationalism, based on recent developments in computer science under the banner of \textit{Deep Learning} and Big Data. We will close with some skeptical consideration of this novel approach.
In this course, we will be concerned with questions about what the human mind is, how it relates to the operation of various kinds of machines, and what this tells us about our place in nature, including questions about the possibility of artificial intelligence. Along the way, we will draw on classical and contemporary work in philosophy, cognitive science and psychology, and computer science.
Outline content:
The module begins with some recent achievements in work on artificial intelligence. We then ask whether any so-called 'artificial intelligence’ could be genuinely intelligent, by reference to the arguments of Margaret Boden, Hubert Dreyfus and John Searle. We first assess Boden’s views about creativity in artificially intelligent systems, before considering Searle’s ‘Chinese Room’ argument that no computer program could be sufficient for intelligenc e, along with Dreyfus’ criticisms of the idea that machines might think. We then turn to some further questions about the consequences of artificial intelligence for humans: Are we humans ourselves thinking machines, in the form of intelligent, naturally occurring computer programs, and if so, could a human mind be ‘uploaded’? What are the perils of artificial intelligence, and should we fear the Singularity? Are Andy Clark and David Chalmers correct th at technological aids to cognition which lie outside the brain might nonetheless form parts of one’s mind?
Brief description of teaching and learning methods:
The module is taught by lectures and seminars, both of which will be conducted online. Students are expected to attend 10 hours of lectures and 5 hours of seminars during the term in which it is provided. Lectures include the presentation of some material by video, as well as traditional lecturing by the module convenor. Seminars include student presentations, as well as discussion among multiple students. All students are required to write one module essay from a list of questions supplied b y the module convenor. Students are encouraged to be active in all classes, asking questions and trying to answer the questions posed by others. Readings, handouts and other study aids will be available via Blackboard.
| Autumn | Spring | Summer | |
| Lectures | 10 | ||
| Seminars | 5 | ||
| Guided independent study: | 85 | ||
| Total hours by term | 100 | ||
| Total hours for module | 100 |
| Method | Percentage |
| Written assignment including essay | 100 |
Summative assessment- Examinations:
N/A
Summative assessment- Coursework and in-class tests:
Formative assessment methods:
Students will have the opportunity to submit draft work for both their presentations and their written assignment.
Penalties for late submission:
The Module Convenor will apply the following penalties for work submitted late:
- where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day[1] (or part thereof) following the deadline up to a total of five working days;
- where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.
Assessment requirements for a pass:
A mark of 40% overall
Reassessment arrangements:
Written assignment
Additional Costs (specified where applicable):
1) Required text books:
2) Specialist equipment or materials:
3) Specialist clothing, footwear or headgear:
4) Printing and binding:
5) Computers and devices with a particular specification:
6) Travel, accommodation and subsistence:
Last updated: 19 October 2020
THE INFORMATION CONTAINED IN THIS MODULE DESCRIPTION DOES NOT FORM ANY PART OF A STUDENT'S CONTRACT.