Instructor: Samir Chopra
Office: 1214 Ingersoll Hall (Mailbox is in the CIS office, 2109 Ingersoll Hall).
Class Meeting Times: Monday – Thursday 10:50 AM -12:05 PM (3214 Ingersoll Hall)
Office hours: Thursday, 12:10 PM – 1:10 PM
This course will attempt to deal with the following questions. What is artificial intelligence? Is it possible? If so, how is it realizable? What model(s) of mind does modern research on artificial intelligence assume? What are the philosophical arguments - good and bad - for and against artificial intelligence? And so on. The central question of course, is: can a machine think - or do X, where X is some uniquely human mental ability? If the answer is yes, what kind of machine can do X? To answer this question, we need to unpack the computational model of mind – what is a Turing machine and why is it sometimes held that the brain is a Turing machine? What is the neural network or neo-connectionist model and why is it said to be opposed to the computational model of mind? Does it offer a more promising avenue of research for AI practitioners? We will also look at arguments against AI – those that think that AI is impossible in principle, and those that think that AI is impossible in practice. What is the Chinese Room argument and why is it relevant to artificial intelligence? We will also discuss one of the most famous arguments against artificial intelligence – the so-called ‘qualia’ argument. Most intuitive (‘layman’) arguments against artificial intelligence are some variant of this particular argument. If artificial intelligence is possible in principle, what problems does it face? What is the so-called frame problem? What is the tractability problem? What are the arguments in the technical computer science community about how to realize artificial intelligence?
There will be one mid-term examination, two paper assignments - one before the mid-term and one before the final exam - and one final exam. Each week, readings will be assigned. All readings listed in the reading list are required in that they can, and will be, the subject of paper assignments and examination questions.
Note: Paper assignments must be handed in on time i.e., on the date and time specified. Late paper submissions will receive an automatic grade of zero. I will be willing to look over a late submission to give you feedback, but I will not assign you a grade for it.
The semester grade will be determined as follows:
Paper assignments: 15 % of the final grade (each).
Midterm exam: 30 % of the final grade.
Final exam: 40 % of the final grade.
Class Mailing List:
Go to www.sci.brooklyn.cuny.edu/cis/majordomo/?philai_fall03, pick the subscribe (default) option (the list name is philai_fall03) and enter your email address. Alternatively, send a message to firstname.lastname@example.org with a blank subject line and subscribe email@example.com as the body of the message (caution: if you are using a web-based email service, make sure it does not insert HTML in the body of the mail - using the web page above is safer). Subscription to the class mailing list is mandatory – consider this your first homework assignment.
Jack Copeland, Artificial Intelligence: A Philosophical Introduction, Blackwell Publishers, Oxford, 1993. ISBN: 0-631-18385-X
Copeland’s book has a slightly dated feel to it in the sections on computer science and AI research. However, it does a magnificent job in the philosophical sections in making things clear and accessible and provides a good road through the central debates. Where and when it mentions these, we will read the literature as necessary. Since the textbook was ordered through the CIS Department, the bookstore has it displayed in the CIS 32.1 bin.
It’s a truism but one worth paying attention to again, that the more you read this semester, the more you will get out of this class. The topics covered in this class are not simple, and require considerable attention and diligence in reading the material indicated. Fortunately, these labors pay rich dividends. To do justice to the class, yourself and your fellow students, it behooves you to work hard at the readings. If you find the vocabulary unfamiliar, work with a dictionary. If you find material hard going, don’t worry, it is. Work through the readings closely, and, if need be, more than once. Your classroom experience – and mine in turn - will be immeasurably enhanced and enriched if you come to each lecture having worked through the indicated readings. Conversely, the classroom experience – everyone’s, not just yours - will be considerably diminished if you insist on coming to class unprepared.
Bullet listed readings below are included in the course packet. You will notice as you work through the readings that the location of the readings in terms of coverage of the material is not exact – some of the readings will apply to more than one portion of the semester (for example, Crane’s chapters refer to the Chinese Room argument and so on). These parts can be revisited and other readings brought forward – we will take it as it comes.
Chapter 1: History of AI: a brief examination of the history of computing relevant to AI. The kickoff papers here will be:
Chapter 2: This is by far the most dated chapter in Copeland’s book, but is still good value. You can chase down information on the various projects mentioned in this chapter on the Internet. For an example of one of modern AI’s achievements check out IBM’s Deep Blue website: http://www.research.ibm.com/deepblue/. For a discussion following Deep-Blue’s over Kasparov, look at slate.msn.com/id/3650/, in which Daniel Dennett and Hubert Dreyfus spar over AI and at http://www.pbs.org/newshour/bb/entertainment/jan-june97/big_blue_5-12.html. Another opinion is ftp://ftp.cs.yale.edu/pub/mcdermott/papers/deepblue.txt. There is plenty more written on Deep Blue – you can find a fair amount of material on the Internet.
Chapter 3: The basic disconnection between thought and consciousness, the Turing test and responses to it. An objection to the Turing test may be found in:
Chapter 4: This covers the fundamentals of computing and the computational model of mind. It will be tempting to think “I’m just going to let my eyes glaze over on this chapter” but that would be a mistake. To understand computing, its basic assumptions and their significance for AI, this chapter is essential. The supplemental readings for this chapter are as follows:
Chapter 5: The problems with AI – the very idea! Various essays on the infamous frame problem may be found in:
An enduring critique of AI is to be found in:
Drew McDermott launches an attack on his fellow computer scientists in:
Steve Hanks and Drew McDermott offer a slightly more technical - logic wise - discussion in:
· Steve Hanks and Drew McDermott, ‘Default Reasoning, Nonmonotonic Logics, and the Frame Problem’, In Proceedings of the National Conference on Artificial Intelligence, Morgan-Kaufmann, pages 328-333, 1986. This essay is of particular interest in making the strong claim that non-monotonic formalisms are unable to capture commonsense reasoning. Much, much work has been done since then in commonsense reasoning and reasoning about action. An Internet search will be particularly useful in tracking down some of the latest work (http://www.ucl.ac.uk/~uczcrsm/researchers.html might be a good place to get started). Check out Murray Shanahan’s Solving the Frame Problem (MIT Press, Cambridge, 1997) for a book-length logic-based treatment of the frame problem.
John McCarthy mounts some defenses of AI in (what else?):
· John McCarthy, Defending AI Research (CSLI Publications, Stanford, 1996). We will read essays #s 5,8,9,10,12,13 (each piece is fairly short).
You might want to track down Roger Penrose’s The Emperor’s New Mind (Oxford, 1989, reprinted 2002) and Shadows of the Mind, (Oxford, 1996). These books were written as popular science books, but have a fair amount of detail on Turing machines, Goedel’s theorem, technical notions of computability and quantum mechanics. An online symposium on Penrose may be found at: http://psyche.cs.monash.edu.au/psyche-index-v2.html. I do not think that we will cover the Penrose argument because of the technical details, but time permitting, we might.
Allied to the Penrose argument is the Lucas argument against AI (again, its not clear at this time that we will have the time to cover this):
· J.R Lucas, ‘Minds, Machines and Gödel’, Philosophy, 36:112-127. An online version can be found at: http://cogprints.ecs.soton.ac.uk/archive/00000356/00/lucas.html
A simple search on the Internet will also bring you to many, many arguments against these two positions (look at the section in David Chalmer’s bibliography listed below).
Chapter 6: A full discussion of the Chinese Room argument will be found in:
The Chinese room and the responses to it are a little cottage industry all by itself. Look at Chalmer’s bibliography below to chase down references and to check out all the various shadings of the debate. A book length treatment of the Chinese Room argument can be found in John Preston’s Views into the Chinese Room, (Oxford University Press, Nov 2002).
Chapter 7: (Free Will): We will cover this only if time permits – more interesting material elsewhere is available for discussion.
Chapter 8: (Consciousness): The nasty business of qualia: the basic argument against AI on the basis of qualia is easily understood. Supplemental reading for this chapter is:
· Daniel Dennett, ‘Quining Qualia’, in Alvin Goldman ed., Readings in Philosophy and Cognitive Science, MIT Press, Cambridge, pp 381-414, 1993.
Chapter 9 (The Brain just is a computer!): Copeland unpacks the strong symbol system hypothesis and its problems. Supplementary readings are the same as those indicated for Chapter 4 i.e., Newell and Simon in Mind Design I and the extended treatments by Block and Pylyshyn.
Chapter 10: (Connectionism, neural nets, PDP, cybernetics): This chapter explains connectionism and the neural network model as a competitor to the computational model of mind. Supplemental reading for this chapter is as follows:
· David Rumelhart, ‘The Architecture of Mind: A connectionist approach’, in John Haugeland, Mind Design II, MIT Press, Cambridge 1997.
· Paul Smolensky, ‘Connectionist Modeling: Neural Computation/Mental Connections’, in John Haugeland, Mind Design II, MIT Press, Cambridge 1997.
· Jay Rosenberg, ‘Connectionism and Cognition’, in John Haugeland, Mind Design II, MIT Press, Cambridge 1997.
· J. Fodor and Z. W. Pylyshyn Z., ‘Connectionism and Cognitive Architecture: A critical analysis’, in John Haugeland, Mind Design II, MIT Press, Cambridge 1997.
· Jack Copeland, ‘The Church-Turing Thesis’, in Stanford Encyclopedia of Philosophy.
A history of cybernetics (the movement that led to connectionist models of the mind) and its philosophical foundations can be found in Jean-Pierre Dupuy’s Mechanization of Mind, Princeton University Press, 2000.
Time permitting, we will consider some anti-representational polemics:
Participation in class discussion is of paramount importance. Your understanding of topics will often be best displayed by your asking intelligent, well reasoned questions, raising valid objections to arguments and displaying an awareness of the issues raised in any particular reading. Nothing you say in class will cause your grade to be lowered. Ask questions on my lectures and the readings. You will aid your understanding of the material, and more often than not, help me as well. Each class discussion will build upon previous sessions. Absence from a class means that you will not profit from the class discussion and that your ability to answer exam questions and write relevant papers will be diminished.
Web resources and other miscellany:
Our reading list above is neither complete nor exhaustive. (It makes no pretensions in this regard). Please use the following links – the collection below is not exhaustive either! - to look for additional reading for topics covered in class, as research resources for your paper and examination preparation and of course, to further pursue your readings if this area interests you. A good strategy for broadening your readings on AI would be to look at the websites of the authors mentioned in the reading list to look at other writings of theirs – and to chase down links they provide to other writers and so on.
Jack Copeland provides an overview of AI in www.cs.usfca.edu/www.AlanTuring.net/turing_archive/pages/Reference%20Articles/What%20is%20AI.html.
Rodney Brook’s website at MIT (www.ai.mit.edu/people/brooks/index.shtml) has lots of interesting information on Brook’s alternative paradigm for AI. Look for information on Cog and Kismet. Brooks also makes an appearance in the movie ‘Fast, Cheap and Out-of-Control’. Check it out: http://us.imdb.com/title/tt0119107/.
John Sutton has an excellent page (http://www.phil.mq.edu.au/staff/jsutton/363reading.html) for his class on Philosophy and Cognitive Science; you might find readings here that are more to your taste on some of the topics that we cover in class. I might make some readings from this collection available to the class during the semester.