6.844 Info

From 6.034 Wiki

(Difference between revisions)
Jump to: navigation, search
Current revision (21:45, 15 September 2020) (view source)
(→We are no longer using this site. Please see the Canvas sites for 6.034 on Canvas and 6.844 on Canvas for information and materials.)
 
(137 intermediate revisions not shown.)
Line 1: Line 1:
-
6.S899
+
= '''Welcome to the 2020 Edition of 6.844'''=
-
=Week 1=  
+
=Overview=
-
''Steps Toward AI'' by Marvin Minsky.
+
6.844 was created in response to requests from grad students who wanted to take 6.034, but needed graduate level credit.
-
Available here
+
-
[https://courses.csail.mit.edu/6.803/pdf/steps.pdf]
+
-
A few comments to guide your reading.
+
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 [http://ai6034.mit.edu/wiki/images/32-155.png 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.
-
Keep in mind first that this paper was written in 1961, 57 (fifty seven!) years ago. I suspect that’s likely before most of you had even entered middle school.  
+
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.
-
As the guest editor’s comment indicates, this is very early on in the birth of the modern version of the field; Minsky had been invited to write an tutorial overview.
+
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.
-
As this is not a technical paper about an idea, technique or program, the standard format for writing about the paper doesn’t apply.  
+
More information about the class can be found [http://ai6034.mit.edu/wiki/images/6844_f20.pdf here].
-
Recall your job is to summarize the paper in one page. Do that, and try to comment on these things as well:
+
=Staff=
-
a) how many of the ideas Minsky mentions do you recognize as still in use?
 
-
b) does he do a good job of laying out the structure of the field?
+
{| border=1 cellspacing=0 cellpadding=3 style="font-size:90%;"
 +
! Prof. Randall Davis<br/> Instructor <br/> ''davis@mit.edu'' || tbd <br/>  Teaching Assistant <br/> ''theosech@mit.edu''
 +
|- valign=top
 +
| [[Image:Rdavis.jpg]] || [[Image:Theo_Sechopoulos.png]]
 +
|}
-
c) 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 that seem compelling to you.
 
 +
== We are no longer using this site. Please see the MIT Canvas sites for [https://canvas.mit.edu/courses/4358 6.034 on Canvas] and [https://canvas.mit.edu/courses/4571 6.844 on Canvas] for information and materials. ==
 +
 +
<!--
 +
=Week 1 -- September 11=
-
=Week 2 -- Sept 28th=
+
-
'''Note: there is no class on September 21'''. It's a student holiday.
+
The paper below is for discussion on <b>Friday, 11 September</b>:
-
The paper below is for discussion on Friday, 28 September (yes, right after the 6.034 quiz). We will discuss the evolution of ''rule-based expert systems''.  The discussion will be based on your comments and insights on the paper:  
+
"Steps Toward AI" by Marvin Minsky,
 +
available
 +
[https://courses.csail.mit.edu/6.803/pdf/steps.pdf 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 -- September 18=
 +
 
 +
'''Note: There is no class on Friday, 20 September'''. It's a student holiday.
 +
 
 +
The paper below is for discussion on <b>Friday, 27 September. </b>  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
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.
+
Scientific Hypothesis Formation" in <i>Artificial Intelligence </i>61, 2 (1993): 209-261.  
-
This is a rather large paper, but you have two weeks 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.
+
The paper is available [http://web.mit.edu/6.034/www/6.s966/dendral-history.pdf here].
-
The paper is available at
+
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.
-
  http://web.mit.edu/6.034/www/6.s966/dendral-history.pdf
+
   
 +
A reminder from the overview info about the course:
-
''Also:'' as several people found out, it's a very bad idea to wait until just before class to try to produce a hardcopy of your writeup. Printers can be hard to find and can be ornery. Plan ahead.
+
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:
-
=Week 3 -- October 5=
+
1.  What is the author trying to accomplish, i.e., what is the problem they are trying to solve? Why is it difficult?
-
''What Are Intelligence? And Why?'' by Randall Davis
+
-
Available here
+
-
[https://www.aaai.org/ojs/index.php/aimagazine/article/view/1356]
+
-
This is another overview of AI paper, rather than a technical examination of a particular technique or program. Instead 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.
+
2.  What technical methods is the author bringing to bear?
-
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 criticisms.
+
3. Did the work succeed? What does “succeed” mean in this case?
-
=Week 4 -- October 12 =
+
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.)
-
Friday's lecture is about deep neural nets, which have been strikingly successful in computer vision, speech understanding, and a range of other classification tasks. But there is also an interesting problem with them. Deep Neural Nets are Easily Fooled, which is capitalized because that's the title of a very interesting paper, available here:
+
-
https://ieeexplore.ieee.org/iel7/7293313/7298593/07298640.pdf
+
=Week 3 -- October 2=
-
[If you are off-campus, that link might not work, in which case use this one:]
+
This paper is for discussion on <b>Friday, 4 October</b>:
 +
"What Are Intelligence? And Why?" by Randall Davis,
 +
available 
 +
[https://www.aaai.org/ojs/index.php/aimagazine/article/view/1356 here].
-
http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Nguyen_Deep_Neural_Networks_2015_CVPR_paper.pdf
+
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.
-
You may also wish to look at this web page:
 
-
http://anhnguyen.me/project/fooling/
+
=Week 4 -- October 9=
 +
 
 +
The paper below is for discussion on <b>Friday, 11 October</b>.
 +
 
 +
You've recently been learning about deep neural nets, which have been strikingly successful in computer vision, speech understanding, and a range of other classification tasks. But there is also an interesting problem with them. Deep Neural Nets are Easily Fooled, which is capitalized because that's the title of a very interesting paper, available
 +
[https://ieeexplore.ieee.org/iel7/7293313/7298593/07298640.pdf here].
 +
 
 +
If you are off-campus, that link might not work, in which case use this  [http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Nguyen_Deep_Neural_Networks_2015_CVPR_paper.pdf one].
 +
 
 +
 
 +
You may also wish to look at  [http://anhnguyen.me/project/fooling/ this web page].
Keep in mind though that I've seen it as well, so just repeating what you see there will not be considered a good write up.
Keep in mind though that I've seen it as well, so just repeating what you see there will not be considered a good write up.
 +
As usual the point is ''not'' just to simply summarize the paper, but to think about what interesting ideas are in there, describe those, and then evaluate them. Make your own judgments, and tell me what you think and why.
-
As usual the point is ''not'' to simply summarize the paper, but to think about what interesting ideas are in there, describe those, and then evaluate them. Make your own judgments and tell me what you think and why.
 
 +
=Week 5 -- October 16=
-
=Week 5 -- October 19=
 
-
Given the other things going on this week, I have selected a more non-technical paper to read. It's still challenging and needs some thought, but it's also fun. It concerns a famous argument about the possibility of computers thinking. Read it over and explain how you react to the arguments. Please, ''don't'' just summarize the paper; write out your own response to it. Is it convincing? If not, why not?
+
Given the other things going on this week, I have selected a more non-technical paper to read. It's still challenging and needs some thought, but it's also fun. It concerns a famous argument about the possibility of computers thinking. Read it over and explain how you react to the arguments. As usual, ''don't'' just summarize the paper; summarize the issues but then describe your response to them. Are they convincing? If so, why? If not, why not?
-
https://ai6034.mit.edu/wiki/images/Searle.pdf
+
The paper is [https://ai6034.mit.edu/wiki/images/Searle.pdf here].
Note that there is no easy or obvious answer here, the idea is to take the argument seriously and think about how you might respond.
Note that there is no easy or obvious answer here, the idea is to take the argument seriously and think about how you might respond.
-
=Week 6 -- October 26=
+
=Week 6 -- October 23=
-
You'll be learning about genetic algorithms this week. The first of these papers introduces the subject generally and hence overlaps somewhat with the class lecture; that's ok, sometimes it's effective to hear about something twice from two different sources, it sticks better. The second paper describes an example of GAs applied to a real world problem.
+
This week's reading seems a nice way to continue our discussion of what we
 +
mean by "understand", whether talking about people or about
 +
programs. It appeared online the day before our last class and
 +
mentions the Chinese Room thought experiment (which is why it came to
 +
the attention of one of our class members, who sent me a pointer).
-
The papers:
 
-
https://onlinelibrary.wiley.com/doi/epdf/10.1002/cplx.6130010108
+
It discusses the efforts to design successively more challenging
 +
language understanding tasks, as a way of working toward the
 +
goal of programs that understand natural language. And hence more
 +
generally, understand.
-
https://ai6034.mit.edu/wiki/images/P79-hemberg.pdf
 
-
You should still hand in only one page of writeup. Use the first paper to learn about the technique. In writing about it, consider these issues:
+
One piece of background you'll need is a basic understanding of word
 +
embedding. This brief article summarizes a research paper (cited at
 +
the end). It introduces the idea, shows you one of the very well known
 +
examples, and explores how robust this technique is:
-
:think carefully about where the analogies to biology are informative and where they can be misleading.
+
[https://www.technologyreview.com/s/541356/king-man-woman-queen-the-marvelous-mathematics-of-computational-linguistics/]
-
:how might you improve the fitness function for the maze path problem?
+
-
In the tax evasion paper, what is the co-evolution that is going on? Does it work? (Don't worry too much about section 3.4).
 
-
In addition to answering these specific questions, identify your own interesting issues and discuss them.
+
In a throwback to primary school, you'll also need to understand
 +
sentence diagramming. A quick intro (or refresher):
 +
[https://blog.ung.edu/press/a-linguists-tree-of-knowledge/]
-
=Week 7 -- November 2=
+
 
 +
Now imagine that a sentence is just a noun phrase followed by a verb
 +
phrase. In your writeup, write this sentence with parentheses that
 +
separate it into the noun phrase and the verb phrase:
 +
 
 +
''The tall person who owns the hammer that is too heavy for Sam to lift likes chocolate.''
 +
 
 +
 
 +
Notice how much simpler the sentence is to understand once it's
 +
divided up into its constituent parts.
 +
 
 +
 
 +
Notice how two words -- "person" and "likes" -- that are very far apart in
 +
the original sentence can now far more easily be seen as connected to
 +
one another.
 +
 
 +
 
 +
You'll need this to understand the part of the article that describes
 +
treelike representations of sentences and neural nets trained with
 +
non-sequential representations.
 +
 
 +
 
 +
With that background, the main article for this week is from Quanta Magazine:
 +
 
 +
 
 +
[https://www.quantamagazine.org/machines-beat-humans-on-a-reading-test-but-do-they-understand-20191017/]
 +
 
 +
 
 +
Points for your writeup (in addition to the sentence re-writing above):
 +
 
 +
1.  Explain the challenge tasks the paper describes in the efforts to reach a
 +
better level of understanding in software. Do these seem to be
 +
working? Why or why not?
 +
 
 +
 
 +
2.  These efforts are sometimes described as a cat and mouse game. How
 +
does that apply here?
 +
 
 +
 
 +
3.  Evaluate the claim the neural network building is now a well defined
 +
engineering practice, in the sense that the right architecture is
 +
easily determined, built and trained. If not, why not?
 +
 
 +
 
 +
4. Consider this quote from the article:
 +
 
 +
''The only problem is that perfect rulebooks don't exist, because natural language is far too complex and haphazard to be reduced to a rigid set of specifications. Take syntax, for example: the rules (and rules of thumb) that define how words group into meaningful sentences. The phrase "colorless green ideas sleep furiously" has perfect syntax, but any natural speaker knows it's nonsense. What prewritten rulebook could capture this "unwritten" fact about natural language -- or innumerable others?''
 +
 
 +
Presumably you understood the "colorless green..." sentence as meaningless in the literal
 +
sense (i.e., leave aside poetic interpretations). How did you do that?
 +
That is, how did you do that in a way that would allow you to do it for
 +
innumerable other such sentences? Do you have a rule book full of
 +
unwritten facts in your head? If not, how did you figure out that this
 +
sentence (and others like it) are problematic?  What do you know?
 +
 
 +
 
 +
=Week 7 -- October 30=
You've been learning about near miss as an interesting model of learning. One of the issues in this technique is the origin of the near misses -- where do they come from? How do we know what near misses to supply?
You've been learning about near miss as an interesting model of learning. One of the issues in this technique is the origin of the near misses -- where do they come from? How do we know what near misses to supply?
-
This paper offers a real use of near-miss learning to inform a sketch recognition system that works from descriptions of shapes. The difficult part is getting those descriptions and getting them to be correct.
+
This paper, mentioned briefly in the 6.034 lecture, offers a real use of near-miss learning to inform a sketch recognition system that works from descriptions of shapes. The difficult part is getting those descriptions and getting them to be correct.
https://ai6034.mit.edu/wiki/images/HammondDavisIUI06.pdf
https://ai6034.mit.edu/wiki/images/HammondDavisIUI06.pdf
-
We've read a several overview papers thus far in the course; this one is a description of a particular system and a particular approach to learning. That makes the framework that I supplied at the beginning of the term more relevant:
+
We've read aseveral overview papers thus far in the course; this one is a description of a particular system and a particular approach to learning. The framework that I supplied at the beginning of the term even more relevant in this context:
Line 112: Line 217:
2. What technical methods is the author bringing to bear?   
2. What technical methods is the author bringing to bear?   
-
3. Did the work succeed? What does “succeed” mean in this case?
+
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.)
+
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.)
Line 120: Line 225:
 +
=Week 8 -- November 6=
-
=Week 8 -- November 9=
+
In last Monday's lecture I gave an overview a core topic in AI -- representation -- that has been somewhat overshadowed recently as a result of the interest in statistical models (e.g., neural nets, SVMs, etc.) It's important to understand how knowledge might be represented explicitly in a program, rather than indirectly (e.g., in the weights of several million neurons). The 6.034 lecture reviewed several of the key representations that have been developed.
-
This Wednesday Professor Winston will be giving a overview of a core topic in AI -- representation -- that has been somewhat overshadowed recently as a result of the interest in statistical models (e.g., neural nets, SVMs, etc.) It's important to understand how knowledge might be represented explicitly in a program, rather than indirectly (e.g., in the weights of several million neurons). His lecture will review several of the key representations that have been developed.
+
The paper for this Friday is an overview of the topic of representation in general, trying to consider what a representation, ''is'' by thinking of the fundamental roles it plays.  
-
The paper for Friday is an overview of the topic of representation in general, trying to consider what a representation ''is'' by thinking of the fundamental roles it plays. The paper lays out 5 such roles -- consider each of them and explain how it does (or does not) help you understand the concept of knowledge representation. The paper assumes a familiarity with traditional knowledge representations of the sort Prof. Winston will describe on Wednesday, so pay attention.
+
The paper lays out 5 such roles -- for each of them:
 +
1) explain what the role is
 +
 +
2) explain how it does (or does not) help make clear to you the concept of knowledge representation. Well reasoned critiques are welcome here. Is there another role that should have been used to explore the concept?
https://ai6034.mit.edu/wiki/images/Whatkr.pdf
https://ai6034.mit.edu/wiki/images/Whatkr.pdf
Line 134: Line 243:
-
=Week 9 -- November 16=
+
=Week 9 -- November 13=
In 6.034 we have seen several models of learning (nearest neighbor, neural nets, SVMs, etc.). All of them share the property that they require large numbers of examples in order to be effective. Yet human learning seems nothing like that. It's remarkable how much we manage to learn from just a few examples, sometimes just one. How might this work?
In 6.034 we have seen several models of learning (nearest neighbor, neural nets, SVMs, etc.). All of them share the property that they require large numbers of examples in order to be effective. Yet human learning seems nothing like that. It's remarkable how much we manage to learn from just a few examples, sometimes just one. How might this work?
 +
 +
This paper for this week explores that topic:
https://ai6034.mit.edu/wiki/images/LakeDec2015.pdf
https://ai6034.mit.edu/wiki/images/LakeDec2015.pdf
Line 144: Line 255:
:A central challenge is to explain these two aspects of human-level concept learning: How do people learn new concepts from just one or a few examples? And how do people learn such abstract, rich, and flexible representations? An even greater challenge arises when putting them together: How can learning succeed from such sparse data yet also produce such rich representations? For any theory learning (4, 14–16), fitting a more complicated model requires more data, not less, in order to achieve some measure of good generalization, usually the difference in performance between new and old examples. Nonetheless, people seem to navigate this trade-off with remarkable agility, learning rich concepts that generalize well from sparse data.
:A central challenge is to explain these two aspects of human-level concept learning: How do people learn new concepts from just one or a few examples? And how do people learn such abstract, rich, and flexible representations? An even greater challenge arises when putting them together: How can learning succeed from such sparse data yet also produce such rich representations? For any theory learning (4, 14–16), fitting a more complicated model requires more data, not less, in order to achieve some measure of good generalization, usually the difference in performance between new and old examples. Nonetheless, people seem to navigate this trade-off with remarkable agility, learning rich concepts that generalize well from sparse data.
-
The paper for this week explores this notion, proposes a mechanism, and demonstrates its performance on a number of problems. It's one of the more challenging papers we'll cover. Don't get lost in the math (there isn't much) or the mechanism (probabilistic programming); instead stay try to determine ''why this works''. Why is the program successful? As usual there is no one right answer here, but see what you can come up with.
+
The paper explores this notion, proposes a mechanism, and demonstrates its performance on a number of problems. It's one of the more challenging papers we'll cover. Don't get lost in the math (there isn't much) or the mechanism (probabilistic programming); instead try to determine ''why this works''. Why is the program successful? As usual there is no one right answer here, but see what you can come up with.
-
A old text describing the Spanish conquest of the Aztecs and Incas provides an obscure hint at one line of thought: "The Conquistadors on their Spanish Horses were seen as Centaurs, so at one were they with their horses during the conquest of Mexico and Peru."
+
A old text describing the Spanish conquest of the Aztecs and Incas provides an obscure hint at one line of thought: "The Conquistadors on their Spanish Horses were seen as Centaurs, so at one were they with their horses during the conquest of Mexico and Peru." (If you're unfamiliar with the term centaur, check wikipedia.]
Note also that the very last page of the pdf has pointers to supplemental material. This is becoming more commonplace, as authors are asked to include more information to support their claims. Sometimes the supplemental material is raw data and code, sometimes it's pointers to additional papers/memos. Be sure to check out the material available so you'll know to do this in the future.
Note also that the very last page of the pdf has pointers to supplemental material. This is becoming more commonplace, as authors are asked to include more information to support their claims. Sometimes the supplemental material is raw data and code, sometimes it's pointers to additional papers/memos. Be sure to check out the material available so you'll know to do this in the future.
-
=Week 10 -- November 30=
+
=Week 10 -- November 20=
With the skyrocketing interest in AI and machine learning has come a recognition that the models we create can be biased, sometimes by accident and at times even despite our best efforts. Given the application of these models to real world issues -- like being approved or turned down for a lown, or being grsnted parole or not -- these systems can have significant real world consequences.
With the skyrocketing interest in AI and machine learning has come a recognition that the models we create can be biased, sometimes by accident and at times even despite our best efforts. Given the application of these models to real world issues -- like being approved or turned down for a lown, or being grsnted parole or not -- these systems can have significant real world consequences.
Line 195: Line 306:
-
=Week 11 -- December 7=
+
=Week 11 -- December 4=
 +
 
 +
AI and Ethics
-
Our final class will folow up on algorithmic fairness and look at a related issue that has been increasing in importance -- ethics in AI.
+
Our final class will follow up on algorithmic fairness and look at a related issue that has been increasing in importance -- ethics in AI.
Line 208: Line 321:
-
For the ethical issues study, we'll want to consider issues like, What does it mean to do ethical research in AI? What for that matter does it mean to do ethical research in any technology?
+
For the ethics of AI we'll want to start with issues like, What does it mean to do ethical research in AI? What for that matter does it mean to do ethical research in any technology?
Line 241: Line 354:
a) What actually is project Maven? Do you consider it unethical?  Why or why not (feel free to use the framework in the Markkula Center materials).
a) What actually is project Maven? Do you consider it unethical?  Why or why not (feel free to use the framework in the Markkula Center materials).
-
b) What is the image at the beginning of the Foreign Policy story and what does it suggest about the publication's view of the work?
+
b) What is the image at the beginning of the Foreign Policy story and how well is it matched to the actual content of the story?  
c) Evaluate this claim from that article:
c) Evaluate this claim from that article:
Line 248: Line 361:
-
d) It's easy to criticize Google's efforts to build a censored search engine for China, as there are numerous problems with it. But take the other side -- what possible benefits might come from it? (Serious answers only, please. "It'll make Google a lot of money" is not a serious answer, even if true.) In all ethical issues it's important (ethically!) to be able to see both sides of an issue.  Ethics questions typically involve careful tradeoffs and balancing acts. You have to be able to see both sides in other to judge the tradeoffs.
+
d) It's easy to criticize Google's efforts to build a censored search engine for China, as there are numerous problems with it. But take the other side -- what possible benefits might come from it? (Serious answers only, please. "It'll make Google a lot of money" is not a serious answer, even if true.) In all ethical issues it's important (ethically!) to be able to see both sides of an issue.  Ethics questions typically involve careful trade-offs and balancing acts. You have to be able to see both sides in order to judge the trade-offs.
 +
-->

Current revision

Contents

Welcome to the 2020 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. 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 		tbd
Teaching Assistant
theosech@mit.edu
Image:Rdavis.jpg Image:Theo_Sechopoulos.png


We are no longer using this site. Please see the MIT Canvas sites for 6.034 on Canvas and 6.844 on Canvas for information and materials.

Retrieved from "https://ai6034.mit.edu/wiki/index.php?title=6.844_Info"
Personal tools