Lecture Summaries and Materials
Technical English 技術英語
Spring 20１8
Prof. David
Avis
Part 1. English technical writing (April 9,16,23, May 7,14)
TA: Yiling Dai
daiyiling10@hotmail.com
also check:
reports
and grading return to: course home page
Check this page often. All reading material
for the course and announcements are here.
Here is some general material that we will study:
How
to write mathematics
by Paul Halmos (A true classic and much copied!)
Writing
good scientific papers
by Wolfgang Schärtl, Mainz University
Mathematical
Writing
by
Donald E. Knuth, Tracy Larrabee,
and Paul M. Roberts
and this book: Writing Science by Joshua Schimel (2012)
April 9: Course overview. Self introduction (sorry,
no slides!).
We did a class exercise: "How to write good" which came from here,
last page. These are slides based on the Knuth-Larrabee-Roberts
paper above which we will study later.
Technical English vs English What is the difference? We
looked at some of Marco Cuturi's slides (Lecture 1).
April 16:
The structure of a scientific research paper. Slides were based on those
prepared by Sylvie
Noel. We talked a lot about titles and abstracts.
Here are some examples or titles and abstracts that we discussed in class. See if you can find the strong and weak points of each.
Report 1 due in
class April 23: Choose any computer scientist of
your liking and describe (about 200 words), as if writing an
abstract, his/her biggest contribution to the field. Be sure to
follow the rules for abstracts that we
discussed. Try to find a 'catchy' title for your abstract! Hand in your homework on a single A4 page.
-------------------------------Sample answer
Alan Turing: Can machines think?
Although references to thinking machines and artificial beings have
appeared in history as early as in ancient Greece (Talos of Crete,
bronze robot of Hephaestus), no rigorous definition for
machine intelligence existed before the work of Alan Turing
(1912-1954). In his breakthrough 1950 paper, which defined the field
of artificial intelligence, he presented what is now known as
the Turing test. Turing reasoned that if independent judges could
not tell the difference between the responses of a machine and a
human in a conversation then we should say that the machines is
intelligent. In the test, judges submit written questions and
receive written responses, not knowing whether they were given
by the human or a machine. In 2014 in Reading, England it was
claimed that a program called Goostman had passed the Turing test,
but this is disputed by most experts. Ever since it was proposed,
the Turing test has been both influential and criticized, making it
one of the most fundamental concepts in the history of AI.
April 23: Story telling. We went through Sylvie
Noel's slides (Slides3 Slides4) describing "sticky" stories and the structures OCAR and LDR. We also watched the video and did the Grammar #1 exercises from Michael Alley's web page.
Report 2 due in class on May 7: First review Slides3 Slides4 . Now examine the two papers: 'A method for obtaining digital signatures ...' by RSA and 'Deep Neural Learning' by Jaeger.
(a) Which story structure (OCAR or LDR) does the RSA paper use? Is it sticky?
(b) Which story structure (OCAR por LDR) does the Jaeger paper use? Is it sticky?
Use about 200 words for each answer.
Justify your answers by taking each letter in turn (either OCAR or LDR) and explain in a few sentences whether you think the paper satisfies the requirement of that letter.
If the paper is 'sticky' which ideas will the reader probably remember? If it is not 'sticky' what did the author do wrong?
May 7: We studied about how to send an effective email using material from Michael Alley: slides1 slides2
May 14: Differences between Japanese technical writing and English technical writing. Read Anthony Leggett's notes.