怎样阅读文献 Next: Getting connected Previous: Introduction Up: How To Do Research In the MIT AI Lab Reading AI Many researchers spend more than half their time reading. You can learn a lo t more quickly from other people's work than from doing your own. This secti on talks about reading within AI; section covers reading about other subject s. The time to start reading is now. Once you start seriously working on your t hesis you'll have less time, and your reading will have to be more focused o n 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 .) The amount of stuff you need to have read to have a solid grounding in the f ield 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 ha ve been published. What's a little tricky is figuring out which ones those a re. 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 le ss 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 impor tant papers and see if he'll lend you copies to Xerox. Recently there have b een appearing a lot of good edited collections of papers from a subfield, pu blished 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 yea rs of them and snag copies of any that look remotely interesting. Besides th e fact that a lot of them are significant papers, it's politically very impo rtant 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 tim e 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 re ally important papers in AI eventually make it into AIJ, so it's worth scann ing through back issues every year or so; but a lot of what it prints is rea lly boring. Computational Intelligence is a new competitor that's worth chec king out. Cognitive Science also prints a fair number of significant AI pape rs. Machine Learning is the main source on what it says. IEEE PAMI is probab ly 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; s ometimes it also has a landmark AIish robotics paper. IEEE Robotics and Auto mation has occasional good papers. It's worth going to your computer science library (MIT's is on the first flo or of Tech Square) every year or so and flipping through the last year's wor th 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 f ull 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 frequentl y 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, a nd 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 gener al the paper is about and what the claimed contribution is, you can decide w hether 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 ex citing stuff. Often this is hidden somewhere unlikely. What the author finds interesting about his work may not be interesting to you, and vice versa. F inally, you may go back and read the whole paper through if it seems worthwh ile. 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 understan ding is in figuring out the motivations, the choices the authors made (many of them implicit), whether the assumptions and formalizations are realist
