Lecture Summaries and Materials

Technical English       技術英語       Spring 2017

Prof. David Avis                          Part 1. English technical writing (April 10,17,24, May 1,8)

TA:      Stef van der Struijk (stefstruijk@ii.ist.i.kyoto-u.ac.jp)

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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 10:  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 17: 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 three example abstracts found by Stef, ranked from best to worst.  See if you can find the strong and weak points of each.
Exercise due in class April 24:  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 24: 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.

Exercise due in class on May 8: 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 1: We studied about how to send an effective email using material from Michael Alley: slides1 slides2

May 8: Differences between Japanese technical writing and English technical writing. Read Anthony Leggett's notes.