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Massachusetts Institute of Technology Artificial Intelligence Laboratory AI Working Paper 316 October, 1988

How to do Research
At the MIT AI Lab
by

a whole bunch of current, former, and honorary MIT AI Lab graduate students David Chapman, Editor Version 1.3, September, 1988. Abstract

This document presumptuously purports to explain how to do research. We give heuristics that may be useful in picking up the specific skills needed for research (reading, writing, programming) and for understanding and enjoying the process itself (methodology, topic and advisor selection, and emotional factors). Copyright c 1987, 1988 by the authors. A. I. Laboratory Working Papers are produced for internal circulation, and may contain information that is, for example, too preliminary or too detailed for formal publication. It is not intended that they should be considered papers to which reference can be made in the literature.

Contents
1 Introduction 2 Reading AI 3 Getting connected 4 Learning other fields 5 Notebooks 6 Writing 7 Talks 8 Programming 9 Advisors 10 The thesis 11 Research methodology 12 Emotional factors 1 2 4 7 11 11 18 20 22 26 30 31

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Introduction

What is this? There’s no guaranteed recipe for success at research. This document collects a lot of informal rules-of-thumb advice that may help. Who’s it for? This document is written for new graduate students at the MIT AI Laboratory. However, it may be useful to many others doing research in AI at other institutions. People even in other fields have found parts of it useful. How do I use it? It’s too long to read in one sitting. It’s best to browse. Most people have found that it’s useful to flip through the whole thing to see what’s in it and then to refer back to sections when they are relevant to their current research problems. The document is divided roughly in halves. The first several sections talk about the concrete skills you need: reading, writing, programming, and so on. The later sections talk about the process of research: what it’s like, how to go at it, how to choose an advisor and topic, and how to handle it emotionally. Most readers have reported that these later sections are in the long run more useful and interesting than the earlier ones. • Section 2 is about getting grounded in AI by reading. It points at the most important journals and has some tips on how to read. • 3 is about becoming a member of the AI community: getting connected to a network of people who will keep you up to date on what’s happening and what you need to read. • 4 is about learning about fields related to AI. You’ll want to have a basic understanding of several of these and probably in-depth understanding of one or two. • 5 is about keeping a research notebook. • 6 is about writing papers and theses; about writing and using comments on drafts; and about getting published. • 7 is about giving research talks. 1

• 8 is about programming. AI programming may be different from the sorts you’re used to. • 9 is about the most important choice of your graduate career, that of your advisor. Different advisors have different styles; this section gives some heuristics for finding one who will suit you. An advisor is a resource you need to know how to use; this section tells you how. • 10 is about theses. Your thesis, or theses, will occupy most of your time during most of your graduate student career. The section gives advice on choosing a topic and avoiding wasting time. • 11 is on research methodology. This section mostly hasn’t been written yet. • 12 is perhaps the most important section: it’s about emotional factors in the process of research. It tells how to deal with failure, how to set goals, how to get unstuck, how to avoid insecurity, maintain self-esteem, and have fun in the process. This document is still in a state of development; we welcome contributions and comments. Some sections are very incomplete. [Annotations in brackets and italics indicate some of the major...
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