A good overview as part of a course syllabus: AI Methodology - Theoretical aspects– Mathematical formalizations, properties, algorithms - Engineering aspects– The act of building (useful) machines - Empirical science– Experiments https://www.cs.cornell.edu/courses/cs4700/2013fa/slides/CS4700-Intro_part2_v... On Tue, Jul 24, 2018 at 11:12 PM, Jenny Davis <jennifer.davis@anu.edu.au> wrote:
Hi Emma and All,
I've been compiling an "AI for social scientists" list as a Google Doc. Editing is set to public so feel free to add! It's currently organized reverse chronologically and has lots of room to grow.
https://docs.google.com/document/d/1xAQlNqpoK8TUoB21i4XVze1zxkvgc Ke06evTfdqd7BA/edit?usp=sharing
[https://lh3.googleusercontent.com/ctAthH-HY-83A1XyPMxgu5yYr8_ ahLAHoC1ksfl2fi1PSlis4jlqTsKOJV3s1jmR3h8dQQ=w1200-h630-p]<ht tps://docs.google.com/document/d/1xAQlNqpoK8TUoB21i4XVze1zxkvgc Ke06evTfdqd7BA/edit?usp=sharing>
AI Reading List<https://docs.google.com/document/d/ 1xAQlNqpoK8TUoB21i4XVze1zxkvgcKe06evTfdqd7BA/edit?usp=sharing> docs.google.com AI Readings for Social Scientists Dillon Reisman, Jason Schultz, Kate Crawford, Meredith Whittaker. 2018 “AI Now Algorithmic Impact Assessments” https://ainowinstitute.org/aiareport2018.pdf Eubanks, Virginia. 2018. “Automating Inequality” https://us.macmillan.com/automatinginequality/ virginiaeu...
Best,
Jenny
Jenny L. Davis
Lecturer, School of Sociology
The Australian National University
Co-Editor: Cyborgology<https://thesocietypages.org/cyborgology/> < https://twitter.com/Jenny_L_Davis>
Twitter: @Jenny_L_Davis<https://twitter.com/Jenny_L_Davis>
________________________________ From: Air-L <air-l-bounces@listserv.aoir.org> on behalf of Emma Stamm < stamm@vt.edu> Sent: Wednesday, July 25, 2018 7:17:46 AM To: aoir list Subject: [Air-L] Seeking resources on A.I. and machine learning
Dear AoIRists,
I am researching artificial intelligence and machine learning within the framework of digital culture studies and philosophy of technoscience. Right now, I am looking for articles that reflect current developments in AI & ML programming that are suitable for non-STEM experts. Specifically, I am interested in pieces that illuminate the ways in which generalization and inductive/abductive reasoning are essential to algorithms that effectively “predict” the future. However, more wide-ranging, introductory pieces would also be helpful for me.
I am familiar with the work of Pedro Domingos, but do not know of many other sources that suit my needs. Any recommendations would be very welcome.
Thank you in advance!
Best,
Emma
-- *Emma Stamm* *PhD Student, ASPECT Virginia Tech <https://liberalarts.vt.edu/departments-and-schools/ alliance-for-social-political-ethical-and-cultural-thought.html>* *o-culus.com <http://o-culus.com> | @turing_tests* _______________________________________________ The Air-L@listserv.aoir.org mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/ listinfo.cgi/air-l-aoir.org
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