怎样阅读文献
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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 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 pr
ogram, 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 in
terested in an area and read a few papers about it, try implementing toy ver
sions of the programs being described. This gives you a more concrete unders
tanding.
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 r
eferencing 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) precis
will find it not particularly brilliant and only vaguely applicable. This ca
n 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 t
heir head.
A whole lot of people at MIT