[Air-L] Seeking resources on A.I. and machine learning

[Ken Latta]

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_v3.pdf




On Tue, Jul 24, 2018 at 11:12 PM, Jenny Davis <jennifer.davis at 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 at listserv.aoir.org> on behalf of Emma Stamm <
> stamm at 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*
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