MIT AI Lab研究指南：如何进行科研

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"这篇文档来自麻省理工学院人工智能实验室，旨在阐述如何进行研究。它提供了一些在阅读、写作、编程等研究所需技能方面的启发性策略，并涵盖了理解与享受研究过程的方法（如方法论、主题和导师选择以及情感因素）。在麻省理工学院人工智能实验室(AI Lab)工作或学习的研究者们共同编写了这份名为"How to do Research"的工作论文316号，由David Chapman编辑，版本1.3，发布于1988年9月。该文档的目的是自负地尝试解释如何进行科学研究，提供了一些可能对提升研究效率有帮助的策略。文档首先介绍了进行研究的重要性，接着详细讨论了以下主题： 1. 阅读AI：这部分可能包含如何有效地阅读和理解AI领域的文献和技术报告。 2. 获取联系：建立学术网络，与同行交流，对于获取最新信息和合作至关重要。 3. 学习其他领域：研究者需要拓宽视野，了解跨学科的知识，这有助于创新思维。 4. 笔记本：保持良好的笔记习惯是跟踪思考过程和进展的关键。 5. 写作：清晰、准确的表达是研究成果的重要展示方式，如何有效写作是研究者必备的技能。 6. 演讲：公开演讲和报告能力有助于传播研究成果，增强影响力。 7. 编程：在AI领域，编程能力是实现研究想法的基础。 8. 导师：选择合适的导师可以引导研究方向，提供指导和支持。 9. 论文：如何规划和撰写高质量的学位论文。 10. 研究方法学：理解并应用适当的研究方法，确保研究的科学性和有效性。 11. 情感因素：研究过程中可能会遇到挫折，如何保持动力和积极心态是成功的关键。此工作论文强调了研究不仅仅是技术层面的探索，还包括个人成长和情感管理。虽然主要针对AI领域的研究，但其中的策略和建议对于任何领域的科研工作都具有普遍价值。由于这是内部流通的工作论文，其内容可能过于初步或详细，不适合正式引用，但它为那些希望在科研道路上取得成功的人提供了宝贵的经验分享。"

papers—but not until at least a year after the cool people know all about it.

Which means that the cool people have a year’s head start on working with new

ideas.

How do the cool people ﬁnd out about a new idea? Maybe they hear about it at

a conference; but much more likely, they got it through the Secret Paper Passing

Network. Here’s how it works. Jo Cool gets a good idea. She throws together a

half-assed implementation and it sort of works, so she writes a draft paper about

it. She wants to know whether the idea is any good, so she sends copies to ten

friends and asks them for comments on it. They think it’s cool, so as well as telling

Jo what’s wrong with it, they lend copies to their friends to Xerox. Their friends

lend copies to their friends, and so on. Jo revises it a bunch a few months later

and sends it to AAAI. Six months later, it ﬁrst appears in print in a cut-down ﬁve-

page version (all that the AAAI proceedings allow). Jo eventually gets around to

cleaning up the program and writes a longer revised version (based on the feedback

on the AAAI version) and sends it to the AI Journal. AIJ has almost two years

turn-around time, what with reviews and revisions and publication delay, so Jo’s

idea ﬁnally appears in a journal form three years after she had it—and almost

that long after the cool people ﬁrst found out about it. So cool p eople hardly ever

learn about their subﬁeld from published journal articles; those come out too late.

You, too, can be a cool people. Here are some heuristics for getting connected:

• There’s a bunch of electronic mailing lists that discuss AI subﬁelds like

connectionism or vision. Get yourself on the ones that seem interesting.

• Whenever you talk about an idea you’ve had with someone who knows the

ﬁeld, they are likely not to give an evaluation of your idea, but to say, “Have

you read X?” Not a test question, but a suggestion ab out something to read

that will probably be relevant. If you haven’t read X, get the full reference

from your interlocutor, or better yet, ask to borrow and Xerox his copy.

• When you read a paper that excites you, make ﬁve copies and give them to

people you think will be interested in it. They’ll probably return the favor.

• The lab has a number of on-going informal paper discussion groups on vari-

ous subﬁelds. These meet every week or two to discuss a paper that everyone

has read.

• Some people don’t mind if you read their desks. That is, read the papers that

they intend to read soon are heaped there and turn over pretty regularly.

You can look over them and see if there’s anything that looks interesting.

Be sure to ask before doing this; some people do mind. Try people who seem

friendly and connected.

• Similarly, some people don’t mind your browsing their ﬁling cabinets. There

are people in the lab who are into scholarship and whose cabinets are quite

comprehensive. This is often a faster and more reliable way to ﬁnd papers

than using the school library.

• Whenever you write something yourself, distribute copies of a draft of it

to people who are likely to be interested. (This has a potential problem:

plagiarism is rare in AI, but it does happen. You can put something like

“Please do not photocopy or quote” on the front page as a partial prophy-

lactic.) Most people don’t read most of the papers they’re given, so don’t

take it personally when only a few of the copies you distribute come back

with comments on them. If you go through several drafts—which for a jour-

nal article you should—few readers will read more than one of them. Your

advisor is expected to be an exception.

• When you ﬁnish a paper, send copies to everyone you think might be in-

terested. Don’t assume they’ll read it in the journal or proceedings sponta-

neously. Internal publication series (memos and technical reports) are even

less likely to be read.

• The more diﬀerent people you can get connected with, the better. Try to

swap papers with people from diﬀerent research groups, diﬀerent AI labs,

diﬀerent academic ﬁelds. Make yourself the bridge between two groups of

interesting people working on related problems who aren’t talking to each

other and suddenly reams of interesting papers will ﬂow across your desk.

• When a paper cites something that looks interesting, make a note of it.

Keep a log of interesting references. Go to the library every once in a while

and look the lot of them up. You can intensively work backward through a

“reference graph” of citations when you are hot on the trail of an interesting

topic. A reference graph is a web of citations: paper A cites papers B

and C, B cites C and D, C cites D, and so on. Papers that you notice

cited frequently are always worth reading. Reference graphs have weird

properties. One is that often there are two groups of people working on the

same topic who don’t know about each other. You may ﬁnd yourself close to

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_grace_

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