Hamming’s “You and Your Research” and Herbert Simon’s “The Scientist as Problem Solver” are good “How I do research” papers. Hamming’s paper was described in the other comments. Simon won both a Turing award and a Nobel prize.
Thanks for the links, but I didn’t care for Simon’s paper at all. I recall Hamming’s inspiring me for a few hours at least—perhaps just making up the time spent reading it :)
Simon’s writing style seems a little strange to me for what its worth...
There are few others who have worked with with him and described their impressions of how he worked. Those might be more readable, but Hamming’s lecture/paper is hard to beat in my opinion.
I attempted to summarize the three papers and incorporate a few other things a while ago, inspired in part by a post by Cal Newport of StudyHacks on the methods of Feynman and a few others. Incidentally, Cal has colloborated in the past with the author of the Holistic Learning ebook in the OP.
Yes, I got a very strange vibe reading that Simon paper, as funny as parts of it (like the concluding advice on how to make good use of your friends) were, and as seminal a figure as he has been in AI and related fields.
After thinking about it, I think the issue is that Simon is coming from the Good Old Fashioned AI point of view of messing around with random Lisp code without any kind of principled background such as statistical models, and this leads to a kind of subtle semantic drift on all sorts of points and vocabulary—a kind of Uncanny Valley effect. Just similar enough to disturb one.
Hamming’s “You and Your Research” and Herbert Simon’s “The Scientist as Problem Solver” are good “How I do research” papers. Hamming’s paper was described in the other comments. Simon won both a Turing award and a Nobel prize.
Simon’s paper is here: http://repository.cmu.edu/cgi/viewcontent.cgi?article=1425&context=psychology Hamming’s: http://www.cs.virginia.edu/~robins/YouAndYourResearch.html
Thanks for the links, but I didn’t care for Simon’s paper at all. I recall Hamming’s inspiring me for a few hours at least—perhaps just making up the time spent reading it :)
Simon’s writing style seems a little strange to me for what its worth...
There are few others who have worked with with him and described their impressions of how he worked. Those might be more readable, but Hamming’s lecture/paper is hard to beat in my opinion.
http://web.cs.dal.ca/~eem/gradResources/HerbertSimon.pdf http://www.isle.org/~langley/papers/has.essay.pdf
I attempted to summarize the three papers and incorporate a few other things a while ago, inspired in part by a post by Cal Newport of StudyHacks on the methods of Feynman and a few others. Incidentally, Cal has colloborated in the past with the author of the Holistic Learning ebook in the OP.
Cal’s post: http://calnewport.com/blog/2012/06/18/impact-algorithms-strategies-remarkable-people-use-to-accomplish-remarkable-things/ My summary of Simon’s Methods: https://sites.google.com/site/wattsd/simplesimon
The summary is still rough and incomplete, so the sources might be more interesting/useful.
Yes, I got a very strange vibe reading that Simon paper, as funny as parts of it (like the concluding advice on how to make good use of your friends) were, and as seminal a figure as he has been in AI and related fields.
After thinking about it, I think the issue is that Simon is coming from the Good Old Fashioned AI point of view of messing around with random Lisp code without any kind of principled background such as statistical models, and this leads to a kind of subtle semantic drift on all sorts of points and vocabulary—a kind of Uncanny Valley effect. Just similar enough to disturb one.
That’s some nice doing-research porn. I liked Cal’s summary. Too bad he PC comment wars over a joke about Feynman “chasing skirt”.
Also recommended: Heilmeier’s Catechism.
That’s some nice doing-research porn. I liked Cal’s summary. Too bad he PC comment wars over a joke about Feynman “chasing skirt”.
Also recommended: http://en.wikipedia.org/wiki/George_H._Heilmeier#Heilmeier.27s_Catechism.