6.844 Info
From 6.034 Wiki
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?
Week 7 -- October 30
Week 8 -- November 6
Week 9 -- November 13
Week 10 -- November 20
Week 11 -- December 6
AI and Ethics
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?
One good, easy to read source of guidance on this is available from the Markkula Center:
https://www.scu.edu/ethics-in-technology-practice/overview-of-ethics-in-tech-practice/
In particular, read at least these two sections:
- 1) Overview of Ethics in Tech Practice
- 2) Framework For Ethical Decision Making
Then read about two recent projects that produced controversy for Google:
The Maven project --
https://foreignpolicy.com/2018/06/29/google-protest-wont-stop-pentagons-a-i-revolution/
A search engine for China --
https://theintercept.com/2018/10/12/google-search-engine-china-censorship/
Answer these questions about those projects and the articles.
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 how well is it matched to the actual content of the story?
c) Evaluate this claim from that article:
- Officials stress that partnering with commercial industry on AI is a national security priority, particularly as potential U.S. adversaries such as Russia and China ramp up investments in that area. China, in particular, is dedicating $150 billion to AI through 2030, Floyd said. Meanwhile, the Defense Department is spending $7.4 billion on AI, big data, and the cloud in fiscal 2017, according to Govini.
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.
