6.844 Info
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This is another overview of AI paper, rather than a technical examination of a particular technique or program. It tries to take a step back and answer a core question: What is it that we're talking about when we talk about intelligence? The paper suggests that intelligence is many things and has been interpreted differently from several different intellectual foundations. | This is another overview of AI paper, rather than a technical examination of a particular technique or program. It tries to take a step back and answer a core question: What is it that we're talking about when we talk about intelligence? The paper suggests that intelligence is many things and has been interpreted differently from several different intellectual foundations. | ||
| - | Your task is to evaluate how successful the paper is in answering the questions it raises. And pay no attention to the name of the author. I expect you to be hard-headed and clear-headed in your evaluation and/or | + | Your task is to evaluate how successful the paper is in answering the questions it raises. And pay no attention to the name of the author. I expect you to be hard-headed and clear-headed in your evaluation and/or criticism. |
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Revision as of 20:47, 4 October 2019
Contents |
Welcome to the 2019 Edition of 6.844
Overview
6.844 was created in response to requests from grad students who wanted to take 6.034, but needed graduate level credit.
It is a supplement to 6.034---you will take 6.034 as usual and do all of that work (lectures, labs, quizzes), and in addition attend the 6.844 session and do the work required there. That session will meet every Friday 11am-12pm in 32-155. Each week there will be a reading assignment focusing on one or more of the foundational, provocative, or intriguing papers from the research literature. You will be expected to do the reading, write up a one page response to a set of questions that will be provided with the reading, and come to class prepared to discuss your (and others') answers to those questions.
The papers will help you learn how to read original research papers in the field and will focus on the science side of AI, addressing the larger scientific questions, rather than existing tools for building applications.
The class is heavy on interaction; you will not be able to just sit back and listen. To keep the class size manageable and to encourage active class participation, we do not allow listeners.
More information about the class can be found here.
Staff
| Prof. Randall Davis Instructor davis@mit.edu | Jack Cook Teaching Assistant cookj@mit.edu |
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Week 1
The paper below is for discussion on Friday, 13 September:
"Steps Toward AI" by Marvin Minsky, available here.
A few comments to guide your reading:
Keep in mind first that this paper was written in 1961, 58 (fifty eight!) years ago.
As the guest editor’s comment indicates, this is very early in the birth of the modern version of the field; Minsky had been invited to write a tutorial overview.
Recall that your job is to summarize the paper in one page. Do that, and also try to comment on these things as well:
1. How many of the ideas Minsky mentions do you recognize as still in use?
2. Does he do a good job of laying out the structure of the field?
3. What is that structure?
4. Consider the sentence near the top of page 9 beginning “A computer can do, in a sense.…” There are several reasons why he starts off that way. List some reasons that seem compelling to you.
Week 2
Note: There is no class on Friday, 20 September. It's a student holiday.
The paper below is for discussion on Friday, 27 September. We will discuss the evolution of rule-based expert systems. The discussion will be based on your comments and insights on this 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.
The paper is available here.
This is a rather long paper, but you have extra time to review it and to consider the interesting ideas in it. Given the size and depth of the paper, it would be a bad idea to wait until the last minute to read it.
A reminder from the overview info about the course:
Your weekly write-up should not be longer than one page and should be readable by someone who hasn't read the paper. The ability to write such a review is an important skill to develop. The idea is not to include a pile of mathematical formulas or lots of code in your review. We want you to learn to extract the essential take-away message of the paper, including such things as:
1. What is the author trying to accomplish, i.e., what is the problem they are trying to solve? Why is it difficult?
2. What technical methods is the author bringing to bear?
3. Did the work succeed? What does “succeed” mean in this case?
4. If it worked, why did it work? Where it failed, why did it fail? (Failures are typically among the most interesting and revealing behaviors of a program.)
Week 3
This paper is for discussion on Friday, 4 October: "What Are Intelligence? And Why?" by Randall Davis, available here.
This is another overview of AI paper, rather than a technical examination of a particular technique or program. It tries to take a step back and answer a core question: What is it that we're talking about when we talk about intelligence? The paper suggests that intelligence is many things and has been interpreted differently from several different intellectual foundations.
Your task is to evaluate how successful the paper is in answering the questions it raises. And pay no attention to the name of the author. I expect you to be hard-headed and clear-headed in your evaluation and/or criticism.
