6.844 Info
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| - | ! Prof. Randall Davis<br/> Instructor <br/> ''davis@mit.edu'' || tbd <br/> Teaching Assistant <br/> '' | + | ! Prof. Randall Davis<br/> Instructor <br/> ''davis@mit.edu'' || tbd <br/> Teaching Assistant <br/> ''theosech@mit.edu'' |
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| - | | [[Image:Rdavis.jpg]] || [[Image: | + | | [[Image:Rdavis.jpg]] || [[Image:Theo_Sechopoulos.png]] |
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| + | == 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. == | ||
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=Week 1 -- September 11= | =Week 1 -- September 11= | ||
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| - | The paper below is for discussion on <b>Friday, | + | The paper below is for discussion on <b>Friday, 11 September</b>: |
"Steps Toward AI" by Marvin Minsky, | "Steps Toward AI" by Marvin Minsky, | ||
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| + | =Week 2 -- September 18= | ||
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'''Note: There is no class on Friday, 20 September'''. It's a student holiday. | '''Note: There is no class on Friday, 20 September'''. It's a student holiday. | ||
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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.) | ||
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=Week 3 -- October 2= | =Week 3 -- October 2= | ||
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This paper is for discussion on <b>Friday, 4 October</b>: | This paper is for discussion on <b>Friday, 4 October</b>: | ||
"What Are Intelligence? And Why?" by Randall Davis, | "What Are Intelligence? And Why?" by Randall Davis, | ||
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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. | 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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=Week 4 -- October 9= | =Week 4 -- October 9= | ||
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The paper below is for discussion on <b>Friday, 11 October</b>. | The paper below is for discussion on <b>Friday, 11 October</b>. | ||
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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'' 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. | ||
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=Week 5 -- October 16= | =Week 5 -- October 16= | ||
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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? | 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? | ||
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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. | ||
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| - | =Week 6 -- October | + | |
| + | =Week 6 -- October 23= | ||
This week's reading seems a nice way to continue our discussion of what we | This week's reading seems a nice way to continue our discussion of what we | ||
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innumerable other such sentences? Do you have a rule book full of | 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 | 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? | + | sentence (and others like it) are problematic? What do you know? |
| - | + | =Week 7 -- October 30= | |
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| - | =Week 7 -- | + | |
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? | ||
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| - | + | =Week 8 -- November 6= | |
| - | =Week 8 -- November | + | |
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. | 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. | ||
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| - | + | =Week 9 -- November 13= | |
| - | =Week 9 -- November | + | |
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? | ||
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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 | + | |
| + | =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. | ||
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| - | + | =Week 11 -- December 4= | |
| - | =Week 11 -- December | + | |
AI and Ethics | AI and Ethics | ||
| - | + | Our final class will follow up on algorithmic fairness and look at a related issue that has been increasing in importance -- ethics in AI. | |
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The justified implication here is of bias against African-Americans. But go a step deeper than that and ask some appropriate followup questions. To get you started: does the paper say who found the European names more easily associated with pleasant terms? Make the obvious guess and then formulate a followup question or two to see whether you can put this in a broader context, and indicate what interesting insights the answers to your questions might reveal. [Yes, it's Prof. Davis being vague again, but it's a good exercise in going past the obvious inference.] | The justified implication here is of bias against African-Americans. But go a step deeper than that and ask some appropriate followup questions. To get you started: does the paper say who found the European names more easily associated with pleasant terms? Make the obvious guess and then formulate a followup question or two to see whether you can put this in a broader context, and indicate what interesting insights the answers to your questions might reveal. [Yes, it's Prof. Davis being vague again, but it's a good exercise in going past the obvious inference.] | ||
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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? | 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? | ||
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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. | 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. | ||
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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 |
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