6.S966: A Graduate Section for 6.034

From 6.034 Wiki

(Difference between revisions)
Jump to: navigation, search
Line 90: Line 90:
-
Week 5:
+
=Week 5:=
On Friday, 13 October we begin to think about learning as well as
On Friday, 13 October we begin to think about learning as well as
Line 109: Line 109:
The paper is available at https://arxiv.org/abs/1604.00289 and
The paper is available at https://arxiv.org/abs/1604.00289 and
-
http://web.mit.edu/6.034/www/6.s966/
+
http://web.mit.edu/6.034/www/6.s966/arXiv1604.00289v3.pdf

Revision as of 17:47, 3 October 2017

Contents

Prospectus

Leader: Gerald Jay Sussman

This term I will experimentally run a graduate section of 6.034, the Introduction to Artificial Intelligence taught by Patrick Henry Winston. You will receive graduate credit if you sign up for 6.S966. However, if you sign up for 6.S966 you will be required to do a bit more work: in addition to attending the three lectures and one recitation of 6.034 each week and doing the 6.034 homework and quizzes, you will be required to attend an extra section led by me. This section will be on Fridays from 11:00AM to noon, just after the Friday lecture, in room 34-303.

While the final details for this extra section are not yet determined, each week you will be required to read a research paper selected to elaborate on the material presented in 6.034 for that week and write a one-page review of that paper to be handed in (on printed paper, not by email!) at the start of the Friday meeting. With that preparation we will have a discussion of the material of the week elaborated by the paper you have read and commented on.

Your weekly review should not be longer than one page. Your review should be readable by someone who has not read the paper that is being reviewed. The ability to write such a review is an important skill for you to develop. It is not helpful to include a pile of mathematical formulas or lots of code in your review. What I want is for you to learn to extract the essential take-away message of the paper:

  1. What is the author trying to accomplish?
  2. What technical methods is the author bringing to bear?
  3. How successful was the resulting work?
  4. Is there some lesson for us in the paper?

If you need graduate credit, you can drop 6.034 and sign up for 6.S966 (12 units) on registration day. If you are unsure about whether you want to take 6.S966, you can decide later. Either way, attend the first session, this Friday, 8 September. I will say more about what will be involved and answer questions.

Week 1:

For the meeting on Friday, 15 September you should read the famous paper "Steps toward Artificial Intelligence", by Marvin Minsky, in Proceedings of the IRE, January 1961.

You should write a 1-page review of this paper and hand it in at the beginning of the meeting (on paper!)

You can find a pdf of this paper at

https://courses.csail.mit.edu/6.803/pdf/steps.pdf

Week 2:

On Friday, 22 September we will discuss the evolution of "rule-based expert systems". The discussion will be based on your reviews of the paper:

Robert K. Lindsay, Bruce G. Buchanan, Edward A. Feigenbaum, and Joshua Lederberg. "DENDRAL: A Case Study of the First Expert System for Scientific Hypothesis Formation." in Artificial Intelligence 61, 2 (1993): 209-261.

This is a rather large paper, but it is full of deep ideas.

The paper is available at

http://web.mit.edu/6.034/www/6.s966/dendral-history.pdf

Weeks 3, 4

Whoops! I forgot that the MIT calendar says that 29 September is a "Student Holiday -- no classes." So our next class will be on 6 October rather than 29 September.

On Friday, 6 October we will discuss constraint propagation and efficient dependency-directed backtracking. The discussion will be based on your reviews of the paper:

Alexey Radul and Gerald Jay Sussman; "The Art of the Propagator," MIT-CSAIL-TR-2009-002; Abridged version in Proc. 2009 International Lisp Conference, March 2009.

I am an author of this paper! Please do not feel that you have to be nice to me: I enjoy my ideas being criticized and I do not take offence. So please, let's fight, if that seems to be appropriate.

The paper is available at

http://web.mit.edu/6.034/www/6.s966/MIT-CSAIL-TR-2009-002.pdf


Week 5:

On Friday, 13 October we begin to think about learning as well as problem solving. Recent astonishing progress in "machine learning" has eclipsed much of the traditional work on symbolic thinking. But problems remain: the systems that result from work on machine learning research have no concept of meaning--the "words" do not have referents outside of the ways in which they are used. They may perform well on many tasks but they do not smoothly interface with systems that are organized around modeling the world, which is probably essential to solving really deep problems of common sense and science.

The discussion will be based on your reviews of the paper:

"Building Machines That Learn and Think Like People", by Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum, Samuel J. Gershman

The paper is available at https://arxiv.org/abs/1604.00289 and

http://web.mit.edu/6.034/www/6.s966/arXiv1604.00289v3.pdf

Personal tools