Bulletin #14 [2016-12-13]: Nouvelles, liens, emplois & évènements • News, links, jobs & events!

Nouvelles, liens, emplois & évènements • News, links, jobs & events
No. 14, 2016-12-13                                                wrangler/responsable: @prooffreader

Welcome to the 14th edition (December 13 2016) of the MTLDATA Bulletin. Don’t forget, you can also find us on the Web, Facebook, Twitter, LinkedIn and Meetup. English content is in blue, French in green.

Bienvenue à la quatorzième édition (le 13 décembre  2016) du Bulletin MTLDATA; n’oubliez pas, vous pouvez nous trouver aussi sur le Web, Facebook, Twitter, LinkedIn et Meetup. Le contenu en anglais est en bleu, celui en français est en vert.

Bulletin also published online http://mtldata.blogspot.ca ← site Web du Bulletin


Intel Security’s Jean-Nicolas Hould wrote a blog post called Tidy Data in Python, with examples explaining the tidy data approach popularized in a 2014 paper by R/RStudio demigod Hadley Wickham, and showing how to apply it to the PyData ecosystem with Pandas.

Since official U.S. election results don’t get released until December, Aviva Canada’s David Taylor (a MTLDATA organizer and wrangler of this bulletin) scraped data from the Politico and CNN websites to sate the hunger of the dataholics in Reddit’s /r/datasets. The data was subsequently used in analyses which were published in major media.

Nicolas Kruchten recently teamed up with Radio-Canada and Hacks/Hackers Montreal data journalist Roberto Rocha to analyze Car2go parking data. Article here, blog post here.

Quan Nguyen of Calcul-Québec and MTLDATA will be a mentor for Robert Wright of Princeton University’s upcoming Coursera MOOC, Buddhism and Modern Psychology. In this video, Wright interviews Kevin Kelly, founder and Executive Editor of Wired about his new book The Inevitable: Understanding the 12 Technological Forces that Will Shape Our Future.  It's interesting to hear discussions on the questions such as "Will Google monopolize AI?" and "How to foster an ethical AI".

Do you know someone making news in Montreal data science? Let us know at bulletin@mtldata.com!

Jean-Nicolas Hould de Intel Security a écrit un article intitulé Tidy Data en Python, avec des exemples expliquant l'approche publiée dans un article de 2014 par le célèbre guru de R / RStudio, Hadley Wickham, et montrant comment l'appliquer à l'écosystème PyData avec Pandas.

Puisque les résultats officiels des élections américaines ne sont pas publiés avant décembre, David Taylor d'Aviva Canada (aussi organisateur de MTLDATA et responsable de ce bulletin a fait du web scraping des données des sites Web de Politico et de CNN pour combler les désirs du subreddit /r/datasets. Les données ont ensuite été utilisées dans des analyses qui ont été publiées dans des principaux médias.

Nicolas Kruchten a récemment collaboré avec un journaliste de données Roberto Rocha de Radio-Canada et Hacks / Hackers Montréal pour analyser les données de stationnement Car2go. Article ici, blog ici.

Quan Nguyen, de Calcul-Québec et MTLDATA, sera un mentor pour un cours Coursera de Robert Wright,de l'Université de Princeton, Le Bouddhisme et la psychologie moderne. Dans cette vidéo, Wright interview Kevin Kelly, fondateur et rédacteur en chef de Wired à propos de son nouveau livre L'inévitable: Comprendre les 12 forces technologiques qui formeront notre avenir. Il est intéressant d'entendre les discussions sur les questions telles que “Est-ce que Google monopolisera l’IA?" Et "Comment favoriser une IA éthique".

Connaissez-vous quelqu'un qui fait des intéressant projects de données à Montréal? Dîtes-le-nous à bulletin@mtldata.com!


Upping the Ante:

The U.S. Election:
  • Intriguingly, a group of undergrads at Princeton were able to build a quick and dirty fake news classifier during a 36 hour hackathon. It is possible these Princeton students a set of once-in-a-generation geniuses. Or, perhaps, fake news is actually tractable as a problem using existing techniques Facebook already has in house.Facebook Must Really Suck At Machine Learning (Elad Gil, Elad Blog)
  • There was widespread complacency about Clinton’s chances in a way that wasn’t justified by a careful analysis of the data and the uncertainties surrounding it.Why FiveThirtyEight Gave Trump A Better Chance Than Almost Anyone Else (Nate Silver, FiveThirtyEight)

  • If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future.” What Artificial Intelligence Can and Can’t Do Right Now (Andrew Ng, Harvard Business Review)
  • At the end of the day, playing video games, while impressive, is really not that different from doing classification on synthetic data.Whence your reward function? (Hall Daumé III, Natural Language Processing Blog)
  • One of the disadvantages of machine learning as a discipline is the lack of reasonable confidence intervals on a given prediction. There are all kinds of reasons you might want such a thing, but I think machine learning and data science practitioners are so drunk with newfound powers, they forget where such a thing might be useful.” Predicting with confidence: the best machine learning idea you never heard of (Scott Locklin)

Deep Learning:

The Future:
  • What happens to ecommerce recommendations when a system might be able to infer things about your taste from your Instagram or Facebook photos, without needing tags or purchase history - when it can see your purchase history in your selfies?Cameras, ecommerce and machine learning (Benedict Evans, ben-evans.com)
  • As machine intelligence improves, the value of human prediction skills will decrease because machine prediction will provide a cheaper and better substitute for human prediction, just as machines did for arithmetic. However, this does not spell doom for human jobs, as many experts suggest. That’s because the value of human judgment skills will increase.The Simple Economics of Machine Intelligence (Joshua Gans, Digitopoly)

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