PAPER INSTRUCTIONS POSTED AT https://courseweb.pitt.edu

HPS 1616 — Artificial Intelligence and Philosophy of Science — Fall 2018

Instructor

Colin Allen, Professor, Dept. of History & Philosophy of Science, University of Pittsburgh <colin.allen@pitt.edu>

Course Description [Jump to Readings]

Artificial Intelligence (A.I.) is one of the core disciplines of cognitive science. It raises fascinating questions: Can robots think? Is artificial intelligence really intelligence? Could artifacts be conscious? What can we learn about the human mind from building robots? How should intelligent robots be built? We will survey the main controversies that artificial intelligence has provoked.

Course Objectives

By the end of this course, you should have a grasp of the history of the field and the technologies underlying the current developments that have pushed A.I. into the forefront of public discussion, as well as a framework for thinking about the scientific significance and the ethical and social implications of these technologies. No experience with computer programming is required for this course, but you will be expected to develop accurate, non-technical understanding of concepts such as algorithm, knowledge representation, machine learning, and artificial neural network. Given this knowledge you will be expected to reflect critically upon the claims about human nature and the future of humanity and society that are driven by the seemingly rapid developments in technology.

This course satisfies the University General Education requirement in PHILOSOPHICAL THINKING OR ETHICS.

Consultation Hours

Make use of open office hours! I actually enjoy talking about this stuff, and you should not feel ashamed or embarrassed if there's something you don't understand. Philosophy deals with hard questions and sometimes seemingly intractable problems, and there are no "stupid" questions, just questions and ideas that are sometimes hard to put into words, and that are best worked out in dialogue with others.

Course Format, Assessments, and Attendance

Classes will be a mixture of lecture and discussion. I will sometimes use presentation slides for class, sometimes not — some material lends itself better to more structured presentation, some lends itself to a more free-flowing Q&A driven classroom style. When I do use slides, they will be made available through Pitt's Courseweb after class, but they will not reflect the full content of the lecture that day. So you may use them for later study, but they are not a substitute for attendance and taking your own notes. Concerning note-taking and studying, you are advised to read the following two articles from the cognitive science of learning:

• Mueller & Oppenheimer 2014 The Pen is Mightier than the Keyboard
• Karpicke & Roediger 2006 Test-Enhanced Learning

You will be assessed on your ability both to accurately convey the concepts covered in the readings and classroom concepts in your own words and to synthesize ideas from different areas into coherent arguments for and against claims involving A.I. Your grade will be based on the following pieces of work:

• 10% Pop quizzes, unannounced, in class.
• 30% Midterm exam, in class; Tuesday Oct 23.
• 30% 8-10 page paper due Nov 29, topic and format will be announced by Nov 1.
• 30% Final exam, Tuesday Dec 11 8:00-9:50 a.m. in 406 Information Sciences Building

Because you will be evaluated on your ability to synthesize ideas discussed in class and in the readings, you should not expect to do well if you do not attend lecture or do the readings. However, this is not grade school, so attendance will not be officially enforced. In all cases of absence, excused or unexcused, it is your responsibility to get missed notes and information from a classmate.

Missed Assessment Policy

You may request to make up for missed exams or other assessments only for university-sanctioned activities, predictable absences for university-sanctioned activities, or genuine emergencies.

• For predictable absences e.g., due to university activities such as student-athletic events, field trips in other classes, etc., or for religious observances, you should talk to me at least two weeks prior to the absence.
• For emergencies such as illnesses, or deaths in the family, please let me know as soon as possible. In some circumstances you may be asked to provided written documentation.

Readings and Reading Schedule

These books are required for this class:

1. Andy Clark, 2013, Mindware 2nd edition.
2. Oliver Theobald, 2018, Machine Learning For Absolute Beginners, 2nd Edition. [See list of errata]
3. Wendell Wallach & Colin Allen, 2010,Moral Machines: Teaching Robots Right from Wrong

Readings from these books will be supplemented with additional readings that will be provided electronically through the links below. These items should be read in the order listed, but there is no set schedule. We will take the lectures and discussions at a pace that is necessary for mastery of the concepts and arguments. The → in the list will be updated to show what you should be reading currently. As appropriate, too, I will insert extra readings to provide additional context and information, especially in the optional readings list.

Required Readings [→ indicates where you should be now]

• Alan Turing 1950 Can a Machine Think?

• John Searle 1980 Minds, Brains, & Programs

• Clark Intro & Chapters 1

• Clark Ch 2

• Theobald Chs 1-3

• Theobald Chs 5 & 6 [yes, you can skip Ch 4]

• Theobald Chs 7-9

• Theobald Ch 10

• Clark Ch 4 [yes, we're skipping Ch 3]

• Clark Ch 5

• Clark Ch 6

→ Clark Ch 7

→ Nick Bostrom 1998 How Long until Superintelligence?

→ Wallach & Allen (whole book)

 

Optional readings for additional background [→ as before]

• M. James 2017 What is a Turing Machine?

• Newell & Simon 1975 Computer Science as Empirical Enquiry

• Schank & Abelson 1977 Scripts, Plans and Goal Understanding

• Ferrucci et al. 2010 Building Watson

• Doug Hofstadter on why Watson is not real AI and a counterpoint from two AI researchers

• Oppenheimer and Zalta 2011 A Computationally-Discovered Simplification of the Ontological Argument

• Blei (2012) Review article on Probabilistic topic models - Columbia CS

• Murdock, Allen & Dedeo (2018) application of topic modeling: Quantitative and Qualitative Approaches to the Development of Darwin’s Origin of Species

→ Theobald Chs 11 & 12

Statement for Students with Disabilities

If you will need extra time for testing or other accommodations due to either short-term or long-term disabilities, please contact the Pitt Office of Student Affairs for information about resources available to help you do you best work.

University rules and the Pitt student code concerning academic misconduct will be rigorously enforced in this class. It is important to cite sources properly and avoid plagiarizing the work of others. If you are unsure about what is required to avoid plagiarism, Indiana University's School of Education has a very comprehensive set of plagiarism tutorials and certification that you should work through.