如何做研究 how to do research

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做科研的方法

做科研的方法科学研究是人类认识和改造自然、推动社会发展的重要手段，而科研方法则是科学研究活动的基本规范和程序。

在当今科技飞速发展的时代，科研方法的选择和运用直接关系到科研工作的效率和质量。

下面将从科研目的、设计、实施、分析和传播等方面，详细探讨做科研的方法。

在确定科研目的时，研究者应明确自己的研究动机和目标，要有明确的研究问题和假设。

科研目的的明确性有助于研究者更好地确定研究方向，选择适当的研究方法和手段。

在科研设计阶段，研究者应充分了解前人的研究成果，找出研究的空白和不足之处，然后根据自己的研究目的和问题，合理选择研究方法和实施方案。

在实施研究过程中，研究者需要遵循科学的方法论原则，例如实验要求具有可重复性、实验方法应具备严谨性、观察数据要准确可信等。

科研人员还应该严格遵守伦理规范，确保研究过程中不伤害研究对象，不侵犯他人的合法权益。

在数据分析方面，科研者应该运用合适的数理统计方法，对实验数据进行分析，得出科学合理的结论。

要遵循数据分析的科学原则，注意排除干扰因素，避免主观臆断，确保数据分析结果的科学可靠性。

在研究成果的传播上，科研者应该以客观、真实的态度向学术界和社会公众传播研究成果，必要时可以通过发表论文、参加学术研讨会、进行学术交流等方式，推广自己的研究成果，促进科学知识的传播和应用。

科研方法是科学研究活动的灵魂和核心，科研者必须严格遵循科学的方法论原则，合理选择研究方法和手段，保证科研工作的严谨性和可靠性，促进科学研究活动的良性发展。

希望科研者能够深入研究和探讨科研方法，提高科研水平，为人类社会的发展贡献更多的科学力量。

How to do Research如何做研究(英文版)

MASSACHUSETTS INSTITUTE OF TECHNOLOGYARTIFICIAL INTELLIGENCE LABORATORYAI Working Paper 316 October, 1988How to do ResearchAt the MIT AI Labbya whole bunch of current, former, and honoraryMIT AI Lab graduate studentsDavid Chapman, EditorVersion 1.3, September, 1988.AbstractThis 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 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.Contents1. Introduction2. Reading AI3. Getting connected4. Learning other fields5. Notebooks6. Writing7. Talks8. Programming9. Advisors10. The thesis11. Research methodology12. Emotional factorsEndnote1. IntroductionWhat 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.Section 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.Section 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.Section 5 is about keeping a research notebook.Section 6 is about writing papers and theses; about writing and using comments on drafts; and about getting published.Section 7 is about giving research talks.Section 8 is about programming. AI programming may be different from the sorts you're used to.Section 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.Section 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.Section 11 is on research methodology. This section mostly hasn't been written yet.Section 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 incompletions.] We appreciate contributions; send your ideas and comments to Zvona@.2. Reading AIMany researchers spend more than half their time reading. You can learn a lot more quickly from other people's work than from doing your own. This section talks about reading within AI; section 4 covers reading about other subjects.The time to start reading is now. Once you start seriously working on your thesis you'll have less time, and your reading will have to be more focused on the topic area. During your first two years, you'll mostly be doing class work and getting up to speed on AI in general. For this it suffices to read textbooks and published journal articles. (Later, you may read mostly drafts; see section 3.)The amount of stuff you need to have read to have a solid grounding in the field may seem intimidating, but since AI is still a small field, you can in a couple years read a substantial fraction of the significant papers that have been published. What's a little tricky is figuring out which ones those are. There are some bibliographies that are useful: for example, the syllabi of the graduate AI courses. The reading lists for the AI qualifying exams at other universities---particularly Stanford---are also useful, and give you a less parochial outlook. If you are interested in a specific subfield, go to a senior grad student in that subfield and ask him what are the ten most important papers and see if he'll lend you copies to Xerox. Recently there have been appearing a lot of good edited collections of papers from a subfield, published particularly by Morgan-Kauffman.The AI lab has three internal publication series, the Working Papers, Memos, and Technical Reports, in increasing order of formality. They are available on racks in the eighth floor play room. Go back through the last couple years of them and snag copies of any that look remotely interesting. Besides the fact that a lot of them are significant papers, it's politically very important to be current on what people in your lab are doing.There's a whole bunch of journals about AI, and you could spend all your time reading them. Fortunately, only a few are worth looking at. The principal journal for central-systems stuff is Artificial Intelligence, also referred to as ``the Journal of Artificial Intelligence'', or ``AIJ''. Most of the really important papers in AI eventually make it into AIJ, so it's worth scanning through back issues every year or so; but a lot of what it prints is really boring. Computational Intelligence is a new competitor that's worth checking out. Cognitive Science also prints a fair number of significant AI papers. Machine Learning is the main source on what it says. IEEE PAMI is probably the best established vision journal; two or three interesting papers per issue. The International Journal of Computer Vision (IJCV) is new and so far has been interesting. Papers in Robotics Research are mostly on dynamics; sometimes it also has a landmark AIish robotics paper.IEEE Robotics and Automation has occasional good papers.It's worth going to your computer science library ( MIT 's is on the first floor of Tech Square) every year or so and flipping through the last year's worth of AI technical reports from other universities and reading the ones that look interesting.Reading papers is a skill that takes practice. You can't afford to read in full all the papers that come to you. There are three phases to reading one. The first is to see if there's anything of interest in it at all. AI papers have abstracts, which are supposed to tell you what's in them, but frequently don't; so you have to jump about, reading a bit here or there, to find out what the authors actually did. The table of contents, conclusion section, and introduction are good places to look. If all else fails, you may have to actually flip through the whole thing. Once you've figured out what in general the paper is about and what the claimed contribution is, you can decide whether or not to go on to the second phase, which is to find the part of the paper that has the good stuff. Most fifteen page papers could profitably be rewritten as one-page papers; you need to look for the page that has the exciting stuff. Often this is hidden somewhere unlikely. What the author finds interesting about his work may not be interesting to you, and vice versa. Finally, you may go back and read the whole paper through if it seems worthwhile.Read with a question in mind. ``How can I use this?'' ``Does this really do what the author claims?'' ``What if...?'' Understanding what result has been presented is not the same as understanding the paper. Most of the understanding is in figuring out the motivations, the choices the authors made (many of them implicit), whether the assumptions and formalizations are realistic, what directions the work suggests, the problems lying just over the horizon, the patterns of difficulty that keep coming up in the author's research program, the political points the paper may be aimed at, and so forth.It's a good idea to tie your reading and programming together. If you are interested in an area and read a few papers about it, try implementing toy versions of the programs being described. This gives you a more concrete understanding.Most AI labs are sadly inbred and insular; people often mostly read and cite work done only at their own school. Other institutions have different ways of thinking about problems, and it is worth reading, taking seriously, and referencing their work, even if you think you know what's wrong with them.Often someone will hand you a book or paper and exclaim that you should read it because it's (a) the most brilliant thing ever written and/or (b) precisely applicable to your own research . Usually when you actually read it, you will find it not particularly brilliant and only vaguely applicable. This can be perplexing. ``Is there something wrong with me? Am I missing something?'' The truth, most often, is that reading the book or paper in question has, more or less by chance, made your friend think something useful about your research topic by catalyzing a line of thought that was already forming in their head.3. Getting connectedAfter the first year or two, you'll have some idea of what subfield you are going to be working in. At this point---or even earlier---it's important to get plugged into the Secret Paper Passing Network. This informal organization is where all the action in AI really is. Trend-setting work eventually turns into published 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 people 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 about 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 various 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 prophylactic.) 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 journal 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 interested. Don't assume they'll read it in the journal or proceedings spontaneously. 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 closure on searching a graph and suddenly find your way into another whole section. This happens when there are different schools or approaches. It's very valuable to understand as many approaches as possible---often more so than understanding one approach in greater depth.Hang out. Talk to people. Tell them what you're up to and ask what they're doing. (If you're shy about talking to other students about your ideas, say because you feel you haven't got any, then try talking to them about the really good---or unbelievably foolish---stuff you've been reading. This leads naturally into the topic of what one might do next.) There's an informal lunch group that meets in the seventh floor playroom around noon every day. People tend to work nights in our lab, and so go for dinner in loose groups. Invite yourself along.If you interact with outsiders much---giving demos or going to conferences---get a business card. Make it easy to remember your name.At some point you'll start going to scientific conferences. When you do, you will discover fact that almost all the papers presented at any conference are boring or silly. (There are interesting reasons for this that aren't relevant here.) Why go to them then? To meet people in the world outside your lab. Outside people can spread the news about your work, invite you to give talks, tell you about the atmosphere and personalities at a site, introduce you to people, help you find a summer job, and so forth. How to meet people? Walk up to someone whose paper you've liked, say ``I really liked your paper'', and ask a question.Get summer jobs away at other labs. This gives you a whole new pool of people to get connected with who probably have a different way of looking at things. One good way to get summer jobs at other labs is to ask senior grad students how. They're likely to have been places that you'd want to go and can probably help you make the right connections.4. Learning other fieldsIt used to be the case that you could do AI without knowing anything except AI, and some people still seem to do that. But increasingly, good research requires that you know a lot about several related fields. Computational feasibility by itself doesn't provide enough constraint on what intelligence is about. Other related fields give other forms of constraint, for example experimental data, which you can get from psychology. More importantly, other fields give you new tools for thinking and new ways of looking at what intelligence is about. Another reason for learning other fields is that AI does not have its own standards of research excellence, but has borrowed from other fields. Mathematics takes theorems as progress; engineering asks whether an object works reliably; psychology demands repeatable experiments; philosophy rigorous arguments; and so forth. All these criteria are sometimes applied to work in AI, and adeptness with them is valuable in evaluating other people's work and in deepening and defending your own.Over the course of the six or so years it takes to get a PhD at MIT , you can get a really solid grounding in one or two non-AI fields, read widely in several more, and have at least some understanding of the lot of them. Here are some ways to learn about a field you don't know muchabout:Take a graduate course. This is solidest, but is often not an efficient way to go about it.Read a textbook. Not a bad approach, but textbooks are usually out of date, and generally have a high ratio of words to content.Find out what the best journal in the field is, maybe by talking to someone who knows about it. Then skim the last few years worth and follow the reference trees. This is usually the fastest way to get a feel of what is happening, but can give you a somewhat warped view.Find out who's most famous in the field and read their books.Hang out with grad students in the field.Go to talks. You can find announcements for them on departmental bulletin boards.Check out departments other than MIT 's. MIT will give you a very skewed view of, for example, linguistics or psychology. Compare the Harvard course catalog. Drop by the graduate office over there, read the bulletin boards, pick up any free literature.Now for the subjects related to AI you should know about.Computer science is the technology we work with. The introductory graduate courses you are required to take will almost certainly not give you an adequate understanding of it, so you'll have to learn a fair amount by reading beyond them. All the areas of computer science---theory, architectures, systems, languages, etc.---are relevant.Mathematics is probably the next most important thing to know. It's critical to work in vision and robotics; for central-systems work it usually isn't directly relevant, but it teaches you useful ways of thinking. You need to be able to read theorems, and an ability to prove them will impress most people in the field. Very few people can learn math on their own; you need a gun at your head inthe form of a course, and you need to do the problem sets, so being a listener is not enough. Take as much math as you can early, while you still can; other fields are more easily picked up later. Computer science is grounded in discrete mathematics: algebra, graph theory, and the like. Logic is very important if you are going to work on reasoning. It's not used that much at MIT , but at Stanford and elsewhere it is the dominant way of thinking about the mind, so you should learn enough of it that you can make and defend an opinion for yourself. One or two graduate courses in the MIT math department is probably enough. For work in perception and robotics, you need continuous as well as discrete math. A solid background in analysis, differential geometry and topology will provide often-needed skills. Some statistics and probability is just generally useful.Cognitive psychology mostly shares a worldview with AI, but practitioners have rather different goals and do experiments instead of writing programs. Everyone needs to know something about this stuff. Molly Potter teaches a good graduate intro course at MIT .Developmental psychology is vital if you are going to do learning work. It's also more generally useful, in that it gives you some idea about which things should be hard and easy for a human-level intelligence to do. It also suggests models for cognitive architecture. For example, work on child language acquisition puts substantial constraints on linguistic processing theories. Susan Carey teaches a good graduate intro course at MIT .``Softer'' sorts of psychology like psychoanalysis and social psychology have affected AI less, but have significant potential. They give you very different ways of thinking about what people are. Social ``sciences'' like sociology and anthropology can serve a similar role; it's useful to have a lot of perspectives. You're on your own for learning this stuff. Unfortunately, it's hard to sort out what's good from bad in these fields without a connection to a competent insider. Check out Harvard: it's easy for MIT students to cross-register for Harvard classes.Neuroscience tells us about human computational hardware. With the recent rise of computational neuroscience and connectionism, it's had a lot of influence on AI. MIT 's Brain and Behavioral Sciences department offers good courses on vision (Hildreth, Poggio, Richards, Ullman) motor control (Hollerbach, Bizzi) and general neuroscience (9.015, taught by a team of experts).Linguistics is vital if you are going to do natural language work. Besides that, it exposes a lot of constraint on cognition in general. Linguistics at MIT is dominated by the Chomsky school. You may or may not find this to your liking. Check out George Lakoff's recent book Women, Fire, and Dangerous Things as an example of an alternative research program.Engineering, especially electrical engineering, has been taken as a domain by a lot of AI research , especially at MIT . No accident; our lab puts a lot of stock in building programs that clearly do something, like analyzing a circuit. Knowing EE is also useful when it comes time to build a custom chip or debug the power supply on your Lisp machine.Physics can be a powerful influence for people interested in perception and robotics.Philosophy is the hidden framework in which all AI is done. Most work in AI takes implicit philosophical positions without knowing it. It's better to know what your positions are. Learning philosophy also teaches you to make and follow certain sorts of arguments that are used in a lot of AI papers. Philosophy can be divided up along at least two orthogonal axes. Philosophy is usually philosophy of something; philosophy of mind and language are most relevant to AI. Then there are schools. Very broadly, there are two very different superschools: analytic and Continental philosophy. Analytic philosophy of mind for the most part shares a world view with most people in AI. Continental philosophy has a very different way of seeing which takes some getting used to. It has been used by Dreyfus to argue that AI is impossible. More recently, a few researchers have seen it as compatible with AI and as providing an alternative approach to the problem. Philosophy at MIT is of the analytical sort, and of a school that has been heavily influenced by Chomsky's work in linguistics.This all seems like a lot to know about, and it is. There's a trap here: thinking ``if only I knew more X, this problem would be easy,'' for all X. There's always more to know that could be relevant. Eventually you have to sit down and solve the problem.5. NotebooksMost scientists keep a research notebook. You should too. You've probably been told this in every science class since fifth grade, but it's true. Different systems work for different people; experiment. You might keep it online or in a spiral notebook or on legal pads. You might want one for the lab and one for home.Record in your notebook ideas as they come up. Nobody except you is going to read it, so you can be random. Put in speculations, current problems in your work, possible solutions. Work through possible solutions there. Summarize for future reference interesting things you read.Read back over your notebook periodically. Some people make a monthly summary for easy reference.What you put in your notebook can often serve as the backbone of a paper. This makes life a lot easier. Conversely, you may find that writing skeletal papers---title, abstract, section headings,fragments of text---is a useful way of documenting what you are up to, even when you have no intention of ever making it into a real paper. (And you may change your mind later.)You may find useful Vera Johnson-Steiner's book Notebooks of the Mind, which, though mostly not literally about notebooks, describes the ways in which creative thought emerges from the accumulation of fragments of ideas.6. WritingThere's a lot of reasons to write.You are required to write one or two theses during your graduate student career: a PhD and maybe an MS, depending on your department.Writing a lot more than that gives you practice.Academia runs on publish-or-perish. In most fields and schools, this starts in earnest when you become a professor, but most graduate students in our lab publish before they graduate. Publishing and distributing papers is good politics and good publicity.Writing down your ideas is the best way to debug them. Usually you will find that what seemed perfectly clear in your head is in fact an incoherent mess on paper.If your work is to benefit anyone other than yourself, you must communicate it. This is a basic responsibility of research . If you write well more people will read your work!AI is too hard to do by yourself. You need constant feedback from other people. Comments on your papers are one of the most important forms of that.Anything worth doing is worth doing well.Read books about how to write. Strunk and White's Elements of Style gives the basic dos and don'ts. Claire Cook's The MLA's Line By Line (Houghton Mifflin) is about editing at the sentence level. Jacques Barzun's Simple and Direct: A Rhetoric for Writers (Harper and Row, 1985) is about composition.。

(final)如何做研究(2013)

武Fra bibliotek大学计算机学院
哪里去找到Topic
通常情况：来自你的导师
– 导师往往是该领域的资深学者，对topic可能有较好的把握能力； – 研究领域本身丌存在“好”，“坏”乊分，只要做的足够深入，都能做出好的工作； – 特定时期，某些领域可能更加活跃，相对来说杰出成果出的更多
武汉大学计算机学院
通过讲获得对你研究的建议
通过讲说服别人对你研究的疑问讲是你研究升华的过程
武汉大学计算机学院
做好的研究，做美的研究
解决复杂的问题选用简单的方法写漂亮的论文
武汉大学计算机学院
思考的缺乏
Nicholas Carr, Atlantic Monthly July 2008
武汉大学计算机学院
45
武汉大学计算机学院
[ABSTRACT] In this paper, we extend the pioneering work of flying pigs [1]. Our improvement enables pigs to fly higher.
武汉大学计算机学院
[ABSTRACT] Recently, the flying pig problem has attracted significant attention [1, 2]. However, pigs in previous works are all flying very slow. In this paper, we introduce a technique so that pigs can fly an order-of-magnitude faster.
武汉大学计算机学院
研究中的写
写是思考的过程，写是整理思路，完善思路的过程有了好的idea，需要去写

外文翻译-如何做研究

Advice to Young ResearchersA wise man can learn from anyone, even from a fool, but a fool can learn from no one, not even from a wise man.—Jewish proverb It is easier to give advice than to bear one’s own problems.—Euripides (480–406 BCE)Anew researcher in any discipline is often at sea without a compass. He may have demonstrated tremendous skill already as a graduate student in the ability to achieve new results in research, but is unaware of the ethical requirements of research or of many activities that can improve the quality of his work and protect that work to a degree from serious errors. I am sorry to say that too many supposedly seasoned researchers are unaware of these errors or choose to ignore them. For the engineer there is the additional requirement that his research should have some connection to the real world, particularly on how one finds research problems in it. The present essay is not intended as a survivor’s manual. It has loftier goals for those young creative researchers who, to steal a phrase from Faulkner [1], will not only survive but prevail, whether in industry, government, or academia. The present article is adapted from part of an earlier article.A BASIC RULEWork hard and do good work! There is no substitute for this nor compensating factors. Also, to do really good work, you must be a little crazy. As the Romans said: nullum magnum ingenium sine mixtura dementiae fuit (there has not been great genius without an admixture of insanity), which need not imply that if you are a bit crazy, you will become a great genius, but it helps. But genius is not enough. My own career is certainly proof that persistence and hard work can compensate for lack of genius, unless one accepts Edison’s definition of genius as one percent inspiration and ninetynine percent perspiration, although my own ratio is certainly much poorer than Edison’s.RESEARCHBe focused! It is important, certainly, that your research have breadth. If you are just starting out, breadth will get you better job offers, or present you with a greater range of areas in which to obtain research funding, but proven depth is more likely to get you fame. If you are a young academician, both will help you get tenure. It is important that there be at least one area where you have real depth, although it may take a long time to develop.Put not thy trust in drawings! Drawings are very helpful in research. They excite our visual perception of the problem at hand, give us new insights, and are excellent ancillary devices for communication and teaching as well as mnemonics for important results. But drawings are also fraught with dangers, since it is often very difficult to interpret signs correctly from drawings. The misinterpretations can sometimes be very subtle. When my calculation almost “works”except for a sign inconsistency that I think I can fix later, that isusually the time for me to start doing the algebra microscopically. A word to the wise…Put not thy trust in others! Never trust a published formula. Always rederive it yourself. Not only do published formulas sometimes contain errors (mea culpa), but we often understand the range of application of a formula only by deriving it ourselves and seeing then the hidden assumptions. If an article is not particularly well written, be prepared that there may be communication problems in the results as well. Always save your notes.Be real! The best research often comes from real problems. I think that much academic research is, well, academic. If you are a young academician, form close ties with a local company or with a government facility. You may find that your best ideas, even general theoretical ideas, arise from their needs. (My most important research has often been research and development.) These contacts may also become a good source of research funding, summer salaries, and jobs for your students.Don’t always be practical! Sometimes impractical research can be very enlightening about the foundations of one’s field and even about the nature of real applications, but don’t let impractical research become your dominant research theme.A wise man can learn from anyone. Listen carefully to others, especially, to the questions of people struggling to understand your work. Sometimes their difficulties stem from situations not considered in your work or contain the germ of an important new idea. This has been the case for me in both Physics and Engineering. Keep in mind that the famous cameraman Gregg Toland volunteered his services on Citizen Kane to the untried director Orson Welles, because, he said, beginners had always provided him with the best new ideas.It is better to be right than “practical.”Wrong work is not “practical,”just because you can describe it simply without equations. Rigorous mathematical work is not “only of theoretical interest,”just because it requires a lot of equations. Do not let yourself be influenced by the kind of person who favors simplicity over correctness.Pay attention to small details! Sometimes small items of no obvious consequence can lead to important research. Be especially watchful of steps that you must gloss over in a research paper, which you think are intuitively obvious, but which you don’t know how to show. They may be a sign of hidden gold for future (or current) research. Also, they may indicate that your earlier intuitive assumptions weren’t quite right.Develop intuition! We avoid mistakes by having good intuition. We develop intuition by making mistakes.Not all work is valuable. Just because a work is correct doesn’t mean that it is valuable. It must be really useful or increase our understanding or pose a new problem as well. In a word, it must be truly worthwhile. Regrettably, valueless research sometimes (often?) gets published.Be a dilettante! It is worthwhile to approach some research as a dilettante, that is, to do the work on your own, on your own schedule and not be tied to contract orgraduate-student timetables. Don’t necessarily give your most original ideas to a Ph.D. student, unless you are certain that the student is at least as smart and as imaginative as you, because then the work will be done on the student’s timetable and at the student’s level of competence and creativity. To make the computer simulations a master’s thesis after the theory has been worked out completely is another thing. Research cannot all bepart of the business of being a professor. Some of it must be a truly joyous personal activity not easily given to a subordinate. If we forget this, then we risk making research just a job.Be unreasonable sometimes! Being unrelentingly reasonable or politically correct in life (and in research) makes Jack a dull boy. Sometimes ethics forces us to go out on a limb, and our quest for truth forces us to explore territory that others would rather we avoid. Shakespeare’s Polonius in Hamlet definitely had something to say here.Knowledge is infinite; humans are finite. While it is good to study just to acquire knowledge, keep in mind that there is no limit to the amount of background one can acquire on a particular topic. Don’t wait until you have complete knowledge of a topic before you begin to develop your own ideas. Usually, the idea comes first. We often learn what things we really need to learn as we do research. Sometimes it is even more efficient simply to “reinvent the wheel.”Learning too much about a topic can make us unoriginal, because we will get stuck in the rut of previous work, even our own.The most important research is often about finding questions, not about finding answers. As engineers or scientists, not only must we find the answers to important questions, we must find the important questions too. Computers and simulation can only be part of research. As Picasso once said, “Computers are useless. They can only give you answers.”(Los ordenatores son inútiles. Sólo pueden darte respuestas.) That statement is not altogether true for us, but there is much truth in it nonetheless.Check your work! This doesn’t mean only not finding errors when you reread your derivation or your computer program. Make certain that your new equations agree with trusted known results in special cases. In derivations, if you are able, derive your result in more than one way. In your computer output, check intermediate results. Check any properties your results should have. In simulation, test for simple models for which you know the answers or can easily calculate them by hand. Never just say: “I coded my equations, and this is what I got.”In a batch estimation problem, for example, do your estimate errors decrease inversely as the square root of N, as N, the number of measurements, becomes large? Are two-thirds of the estimation errors smaller than the theoretical one sigma in magnitude?Have courage! Do not be afraid to examine a topic, just because a respected colleague thinks such work is silly or that the problem has been settled. On the other hand, if you discover nothing important, move on. Most of my own early efforts in Astronautics did not lead to a publication.Carpe diem! Seize the day, said the Roman poet Horace (Quintus Horatius Flaccus, 65–8 BCE). Do not procrastinate in your work. Above all, do not procrastinate when it comes to working independently. When one arrives at a faculty as a new assistant professor or even post-doctoral fellow, it is natural to want to be helpful to your new colleagues, or to become a secondary contributor to their work, which you may rightly regard as better than anything that you can do. Your rôle, however, is not to be effectively another graduate student for senior faculty, even if you have much to learn from them. Participate in their work, make yourself useful. It will not hurt your chances for tenure that some highly respected member of the faculty acknowledges a debt to you. But you must also give your department a real reason to give you tenure. You must take the initiative in doing independent research. Don’t procrastinate. You can find a million excuses to put this off.Remember the Nike commercial and just do it! If you are working in industry, and your boss asks you to do something, but you think you have a better idea, do first what he asks—you don’t want to get a reputation of being uncooperative, which could get you fired or at least a poor raise—and then work out your own approach as well, but only if it can be done quickly. And don’t show off afterward.Keep it simple. Not all research is equal, and not all of it is valuable. The best research discovers simple things, which are often the hardest to find. It is easy simply to extend an existing piece of work to treat a small variation of a model or of a set of assumptions, or which consists mostly of simulations. Such work can be valuable, but the simple things are often the most important. Above all, avoid making a career out of publishing endless variations of your dissertation or some other research work. Your dissertation may contain unmined gold, but recognize when the vein has run out. Don’t expend enormous effort just to develop a new methodology which is five percent better than an existing one. No one will care except you.给年轻研究者的建议智者能从任何人那里学到东西，即使是傻瓜；但傻瓜在谁那儿也学不到知识，哪怕是在智者那里。

研究思路和方法

研究思路和方法在进行研究时，科学合理的研究思路和方法是非常重要的。

一个好的研究思路和方法能够帮助研究者更好地开展研究工作，提高研究效率，确保研究结果的可靠性和科学性。

因此，本文将就研究思路和方法进行一些探讨和总结。

首先，研究思路的构建是研究工作的基础。

研究思路应当清晰明确，能够准确地指导研究者进行研究工作。

在构建研究思路时，研究者可以先对研究课题进行深入的了解和分析，明确研究的目的和意义，明确研究的范围和内容，确定研究的重点和难点，从而确定研究的方向和思路。

在构建研究思路时，研究者还应当充分考虑到研究的实际情况和条件，确保研究思路的可行性和有效性。

其次，研究方法的选择是研究工作的关键。

研究方法应当科学合理，能够有效地解决研究课题，获取准确的研究结果。

在选择研究方法时，研究者可以根据研究的具体内容和要求，选择合适的研究方法，如实证研究方法、理论研究方法、实验研究方法等。

在选择研究方法时，研究者还应当充分考虑到研究的实际情况和条件，确保研究方法的可操作性和有效性。

最后，研究思路和方法的实施是研究工作的关键。

研究思路和方法的实施需要研究者具备一定的科研能力和实践能力，能够独立开展研究工作。

在实施研究思路和方法时，研究者应当严格按照研究思路和方法的要求，认真细致地进行研究工作，确保研究过程的科学性和规范性。

在实施研究思路和方法时，研究者还应当及时总结经验，不断改进和完善研究思路和方法，提高研究工作的质量和水平。

总之，研究思路和方法是研究工作的重要组成部分，对于研究工作的开展和结果的取得起着至关重要的作用。

因此，研究者应当充分重视研究思路和方法的构建和选择，努力提高研究思路和方法的科学性和有效性，确保研究工作的顺利进行和取得良好的研究成果。

research method 注意事项

一、如何做研究1. 日志（diary writing/memo）在研究的整个过程中，为了了解研究的进程，研究者每天记录研究的具体进展及反思性分析。

记日志时，要自己固定一个记录的格式或体例。

在每篇日志之前都要清楚地记下日期、地点、参加人员、记录者，还可以有主题或者小标题。

为了便于以后的分析和整理，建议研究者采用一些固定的标注符号。

例如：对于研究进展的描述可以用大写字母D (Description)表示，对于自己的评论可以用大写字母C (Comment)表示，对于反思性分析可以用大写字母R（Reflections）等标注。

2.录像和录音(video and audio)通过录像，可以记录到真实生动的信息，捕捉到很多细节，如表情、对话、动作及现场的环境等，为研究者提供观察依据和素材。

如，要调查大学新生报到有多少同学是独自报到的，可以采用录像的方法记录报到现场的情景。

与录像相比，录音更易于操作，能完整地记录有声资料。

可以用来记录访谈、发言的声音资料。

3. 访谈（Interview）访谈是指访谈者向被访谈者作面对面的直接调查，是通过口头交流的方式获取有关资料的方法。

采用访谈的方式可以使研究者了解受访者的内心活动，真实地想法和观点。

访谈的主要类型有三种:有结构访谈（Structured Interview）；无结构访谈（Unstructured Interview）；半结构访谈（Semi-structured Interview）。

有结构访谈：指按照有完整结构的问卷形式而进行的正式访谈，既有问题也有答案供被访谈者选择。

它的好处是访谈结果便于统计分析，易于比较不同的答案。

但是这种访谈缺乏弹性，访谈者和被访谈者均不能根据双方的情况灵活地改变问题和答案。

无结构访谈：访谈者按照一个粗线条的提纲进行访谈，对于提问的方式、所提的问题、回答的方式、访谈记录的方式等没有统一的要求，访谈者可根据具体情况灵活处理。

同时也允许被访谈者提出问题。

如何做科学研究英文 PPT

Locating Resources/Gathering Information & Materials
• Ask yourself:
– What do I know about my topic? – What additional information would help me? – How can I use different sources of information
research?
Developing the hypothesis
• A hypothesis is an assertion subject to verification, testing or proof、
– A hypothesis should state precisely what will be tested、 Formulating a hypothesis will help you design your experiment so that you'll know what to measure or what data will be needed、
(experts, books, articles, puter databases) to gather the information I need? – Where will I conduct this research? – What resources are available to me--time, equipment, people, money, facilities, etc、?
• Read literatures as much as possible • Identify questions • Construct hypothesis • Conduct a pilot experiment • Write a proposal

研究的步骤和计划模板

研究的步骤和计划模板
一、研究步骤
1. 确定研究主题和目标：明确研究的问题和目的，为后续的研究工作提供方向。

2. 文献综述：查阅相关文献，了解研究领域的现状和已有研究成果，为研究提供理论依据。

3. 确定研究方法：根据研究目标和主题，选择合适的研究方法，如问卷调查、访谈、实验等。

4. 收集数据：按照研究方法的要求，收集相关数据，为后续的分析提供基础。

5. 数据分析：对收集到的数据进行整理、分析和解释，得出研究结论。

6. 撰写研究报告：将研究过程、结果和结论整理成研究报告，供他人参考和使用。

二、研究计划模板
1. 研究背景：介绍研究背景和意义，说明研究的必要性和重要性。

2. 研究目的：明确研究的目的和目标，为后续的研究工作提供方向。

3. 研究问题：提出研究的问题和假设，为后续的研究提供依据。

4. 研究方法：说明研究的方法和技术路线，包括数据收集、分析和解释的方法。

5. 研究步骤：详细描述研究的步骤和时间安排，包括每个步骤的具体任务和时间节点。

6. 预期结果：预测研究的结果和可能的影响，为后续的研究提供参考。

7. 研究意义：阐述研究的理论和实践意义，说明研究的价值和贡献。

8. 参考文献：列出参考文献，为研究提供理论依据和支持。

以上是研究的步骤和计划模板，希望对您有所帮助。

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∙麻省理工学院人工智能实验室AI Working Paper 316 1988年10月来自MIT人工智能实验室：如何做研究？作者：人工智能实验室全体研究生编辑：David Chapman版本：1.3时间：1988年9月译者：柳泉波北京师范大学信息学院2000级博士生摘要本文的主旨是解释如何做研究。

我们提供的这些建议，对做研究本身（阅读、写作和程序设计），理解研究过程以及开始热爱研究（方法论、选题、选导师和情感因素），都是极具价值的。

不像正式论文那样，会列出所有的参考文献。

1. 简介这是什么？并没有什么神丹妙药可以保证在研究中取得成功，本文只是列举了一些可能会有所帮助的非正式意见。

目标读者是谁？本文档主要是为MIT人工智能实验室新入学的研究生而写，但对于其他机构的人工智能研究者也很有价值。

即使不是人工智能领域的研究者，也可以从中发现对自己有价值的部分。

如何使用？要精读完本文，太长了一些，最好是采用浏览的方式。

很多人觉得下面的方法很有效：先快速通读一遍，然后选取其中与自己当前研究项目有关的部分仔细研究。

本文档被粗略地分为两部分。

第一部分涉及研究者所需具备的各种技能：阅读，写作和程序设计，等等。

第二部分讨论研究过程本身：研究究竟是怎么回事，如何做研究，如何选题和选导师，如何考虑研究中的情感因素。

很多读者反映，从长远看，第二部分比第一部分更有价值，也更让人感兴趣。

? 小节2 如何通过阅读打好AI研究的基础。

列举了重要的AI期刊，并给出了一些阅读的诀窍。

? 小节3 如何成为AI研究领域的一员：与相关人员保持联系，他们可以使你保持对研究前沿的跟踪，知道应该读什么材料。

? 小节4 学习AI相关领域的知识。

对几个领域都有基本的理解，对于一个或者两个领域要精通。

? 小节5 如何做研究笔记。

? 小节6 如何写期刊论文和毕业论文。

如何为草稿写评审意见，如何利用别人的评审意见。

如何发表论文。

? 小节7 如何做研究报告。

? 小节8 是有关程序设计的。

AI程序设计与平常大家习惯的程序设计有所不同。

? 小节9 有关研究生涯最重要的问题，如何选导师。

不同的导师具有不同的风格，本节的意见有助于你找到合适的导师。

导师是你必须了解如何利用的资源。

? 小节10 关于毕业论文。

毕业论文将占据研究生生涯的大部分时间，本部分涉及如何选题，以及如何避免浪费时间。

? 小节11 有关研究方法论，尚未完成。

? 小节12 或许是最重要的一节：涉及研究过程中的情感因素，包括如何面对失败，如何设定目标，如何避免不安全感，保持自信，享受快乐。

2. 阅读很多研究人员花一半的时间阅读文献。

从别人的工作中可以很快地学到很多东西。

本节讨论的是AI中的阅读，在第四小节将论述其他主题相关的阅读。

阅读文献，始于今日。

一旦你开始写作论文，就没有多少时间了，那时的阅读主要集中于论文主题相关的文献。

在研究生的头两年，大部分的时间要用于做课程作业和打基础。

此时，阅读课本和出版的期刊文章就可以了。

（以后，你将主要阅读文章的草稿，参看小节三）。

在本领域打下坚实的基础所需要的阅读量，是令人望而却步的。

但既然AI只是一个很小的研究领域，因此你仍然可以花几年的时间阅读本领域已出版的数量众多论文中最本质的那部分。

一个有用的小技巧是首先找出那些最本质的论文。

此时可以参考一些有用的书目：例如研究生课程表，其他学校（主要是斯坦福大学）研究生录取程序的建议阅读列表，这些可以让你有一些初步的印象。

如果你对AI的某个子领域感兴趣，向该领域的高年级研究生请教本领域最重要的十篇论文是什么，如果可以，借过来复印。

最近，出现了很多精心编辑的有关某个子领域的论文集，尤其是MorganKauffman出版的。

AI实验室有三种内部出版物系列：Working Papers，Memos和Technical Reports，正式的程度依次增加，在八层的架子上可以找到。

回顾最近几年的出版物，将那些非常感兴趣的复制下来。

这不仅是由于其中很多都是意义重大的论文，对于了解实验室成员的工作进展也是很重要的。

有关AI的期刊有很多，幸运的是，只有一部分是值得看的。

最核心的期刊是Artificial I ntelligence，也有写作"the Journal of Artificial Intelligence"或者"AIJ"的。

AI领域真正具备价值的论文最终都会投往AIJ，因此值得浏览每一年每一期的AIJ；但是该期刊也有很多论文让人心烦。

Computational Intelligence是另外一本值得一看的期刊。

Cogn itive Science也出版很多意义重大的AI论文。

Machine Learning是机器学习领域最重要的资源。

IEEE PAMI（Pattern Analysis and Machine Intelligence）是最好的有关视觉的期刊，每期都有两三篇有价值的论文。

International Journal of Computer Vision（IJ CV）是最新创办的，到目前为止还是有价值的。

Robotics Research的文章主要是关于动力学的，有时候也有划时代的智能机器人论文。

IEEE Robotics and Automation偶尔有好文章。

每年都应该去所在学校的计算机科学图书馆（在MIT的Tech Square的一层），翻阅其他院校出版的AI技术报告，并选出自己感兴趣的仔细加以阅读。

阅读论文是需要练习的技能。

不可能完整地阅读所有的论文。

阅读论文可分为三个阶段：第一阶段是看论文中是否有感兴趣的东西。

AI论文含有摘要，其中可能有内容的介绍，但是也有可能没有或者总结得不好，因此需要你跳读，这看一点那看一点，了解作者究竟做了些什么。

内容目录（the table of contents）、结论部分（conclusion）和简介（int roduction）是三个重点。

如果这些方法都不行，就只好顺序快速浏览了。

一旦搞清楚了论文的大概和创新点，就可以决定是否需要进行第二阶段了。

在第二阶段，要找出论文真正具有内容的部分。

很多15页的论文可以重写为一页左右的篇幅；因此需要你寻找那些真正激动人心的地方，这经常隐藏于某个地方。

论文作者从其工作中所发现的感兴趣的地方，未必是你感兴趣的，反之亦然。

最后，如果觉得该论文确实有价值，返回去通篇精读。

读论文时要牢记一个问题，我应该如何利用该论文？真的像作者宣称的那样么？“”“”“……”如果会发生什么？。

理解论文得到了什么结论并不等同于理解了该论文。

理解论文，就要了解论文的目的，作者所作的选择（很多都是隐含的），假设和形式化是否可行，论文指出了怎样的方向，论文所涉及领域都有哪些问题，作者的研究中持续出现的难点模式是什么，论文所表达的策略观点是什么，诸如此类。

将阅读与程序设计联系在一起是很有帮助的。

如果你对某个领域感兴趣，在阅读了一些论“”文后，试试实现论文中所描述的程序的玩具版本。

这无疑会加深理解。

可悲的是，很多AI实验室天生就是孤僻的，里面的成员主要阅读和引用自己学校实验室的工作。

要知道，其他的机构具有不同的思考问题的方式，值得去阅读，严肃对待，并引用它们的工作，即使你认为自己明晓他们的错误所在。

经常会有人递给你一本书或者一篇论文并告诉你应该读读，因为其中有很闪光的地方且/或可以应用到你的研究工作中。

但等你阅读完了，你发现没什么特别闪光的地方，仅仅是勉强可用而已。

于是，困惑就来了，我哪不对啊？我漏掉什么了吗？。

实际上，这是因“”为你的朋友在阅读书或论文时，在头脑中早已形成的一些想法的催化下，看出了其中对你的研究课题有价值的地方。

3. 建立关系————一两年后，对自己准备从事的子领域已经有了一些想法。

此时或者再早一点加入Secret Paper Passing Network是很重要的。

这个非正式的组织是人工智能真正在做什么的反映。

引导潮流的工作最终会变成正式发表的论文，但是至少在牛人完全明白一年之后，也就是说，牛人对新思想的工作至少领先一年。

牛人如何发现新思路的？可能是听自于某次会议，但是最可能来自于Secret Paper Passing Network。

下面是该网络工作的大致情况。

Jo Cool有了一个好想法。

她将尚不完整的实现与其他一些工作融合在一起，写了一份草稿论文。

她想知道这个想法究竟怎么样，因此她将论文的拷贝发送给十位朋友并请他们进行评论。

朋友们觉得这个想法很棒，同时也指出了其中的错误之处，然后这些朋友又把论文拷贝给他们各自的一些朋友，如此继续。

几个月后，Jo对之进行了大量修订，并送交给AAAI。

六个月后，该论文以五页的篇幅正式发表（这是AAAI会议录允许的篇幅）。

最后Jo开始整理相关的程序，并写了一个更长的论文（基于在AAAI发表论文得到的反馈）。

然后送交给AI期刊。

AI期刊要花大约两年的时间，对论文评审，包括作者对论文修改所花费的时间，以及相应的出版延迟。

因此，理想情况下，Jo的思想最终发表在期刊上需要大约三年时间。

所以牛人很少能从本领域出版的期刊文章中学到什么东西，来得太迟了。

你，也可以成为一个牛人。

下面是建立学术关系网的一些诀窍：? 有很多讨论某个AI子领域（如连接主义或者视觉）的邮件列表，选择自己感兴趣的列表加入。

? 当与很熟悉本领域的人讨论自己的思想时，他们很可能不直接评价你的想法，而是说：“”你读过某某吗？这并不是一个设问，而是建议你去阅读某份文献，它很可能与你的想法有关系。

如果你还没有读过该文献，从跟你交谈的高手那里得到该文献的详细信息，或者直接从他那里借一份拷贝下来。

? 当你读到某份让你感到很兴奋的论文，复印五份送交给对之感兴趣的其他五个人。

他们可能会反馈回来很好的建议。

? 本实验室有很多针对不同子领域的非正式（持续发展的）论文讨论组，他们每星期或每两星期聚会一次，对大家阅读完的论文进行讨论。

? 有些人并不介意别人去翻看他们的书桌，也就是说，去翻阅他们堆在书桌上的不久要阅读或者经常翻阅的论文。

你可以去翻翻看，有没有自己感兴趣的。

当然了，首先要得到主人的许可，要知道有些人确实反感别人翻自己的东西。

去试试那些平易近人的人。

? 同样，有些人也并不介意你翻看他们的文件柜。

实验室中可是有很多学问精深的人，他们的文件柜里也是有好多宝贝。

与利用学校图书馆相比，这通常是更快更可靠的寻找论文的方式。

? 只要自己写下了些东西，将草稿的拷贝分发给那些可能感兴趣的人。

（这也有一个潜在领域的剽窃很少，但也确实有。