6.844 Info
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AI and Ethics | AI and Ethics | ||
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First, a followup on an issue we didn't have time for last class: Consider this statement from the Naryanan paper | First, a followup on an issue we didn't have time for last class: Consider this statement from the Naryanan paper | ||
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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? | ||
Revision as of 20:17, 17 August 2020
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 ?@mit.edu |
|---|---|
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Week 1 -- September 11
Week 2 -- September 18
Week 3 -- October 2
Week 4 -- October 9
Week 5 -- October 16
Week 6 -- October 23
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?
