About pair programming, the invention of map reduce and the need for fault tolerance, and about knowing all the details.
All that and impressionism.
https://www.newyorker.com/magazine/2018/12/10/the-friendship-that-made-google-huge
Twitter bots had 'disproportionate' role spreading misinformation in 2016 election: study https://prismo.news/posts/121
Knitted by AI.
"Even debugged, the patterns were weird. Like, really, really nonhumanly weird."
"“Four repeats in to this oddball, daintily alien-looking 8-row lace pattern, and I have, improbably, begun to internalize it and get in to a rhythm like every other lace pattern.
I still have a lingering suspicion that I’m knitting a pattern that could someday communicate to an AI that I want to play a game of Global Thermonuclear War.”"
http://aiweirdness.com/post/173096796277/skyknit-when-knitters-teamed-up-with-a-neural
Sptoify announced its new Data Science Challenge
Spotify Sequential Skip Prediction Challenge is a part of #WSDM Cup 2019. The dataset comprises 130M Spotify listening sessions, and the task is to predict if a track is skipped. The challenge is live today, and runs until Jan 4.
https://www.crowdai.org/challenges/spotify-sequential-skip-prediction-challenge
Nobody wants to join?
"While many of the big tech companies have been hit by a change in public perception, Facebook seems uniquely tarred among young workers.
They’re not as enthusiastic about Facebook because they’re frustrated with what they see happening politically or socially. It’s privacy and political news, and concern that it’s going to be hard to correct these things from inside.
Defense companies have had this reputation for a long time. Social networks are just getting that."
https://www.nytimes.com/2018/11/15/technology/jobs-facebook-computer-science-students.html
"Fake videos can now be created using a technique called a generative adversarial network, or GAN.
Although the machine learning breakthroughs in computer graphics are impressive, researchers should be more cognizant of the ramifications of what they’re creating. “It is not clear that the positive implications outweigh the negative.”
“Deep fakes are amplifying the liar’s dividend. When nothing is true, the dishonest person will thrive by saying what’s true is fake.”
https://www.theguardian.com/technology/2018/nov/12/deep-fakes-fake-news-truth
Welcome to the MSc course Managing Big Data! #MBD
You can use Mastodon toots for class discussion, questions, or to contact your teacher. You can configure the privacy of each toot as it fits.
The Canvas page is:
https://canvas.utwente.nl/courses/2477
See this Canvas page for a link to the course materials online.
"“Social media has elevated misogyny to entirely new levels of violence and virulence. Men with anti-feminist ideas broadcast their views to more people than ever before – and spread conspiracy theories, lies and misinformation.”
Classical texts are being “distorted and stripped of context” online to lend gravitas to campaigns of misogyny and white supremacy."
"Who should AI kill in a driverless car crash? It depends who you ask.
Responses to those questions varied greatly around the world. In the [developing world], for instance, there was a strong preference to spare young people at the expense of old – a preference that was much weaker in the far east and the Islamic world. The same was true for the preference for sparing higher-status victims – those with jobs over those who are unemployed."
Computer Science geeft drie 10-en in één jaar https://www.utoday.nl/news/66105/computer-science-geeft-drie-10-en-in-een-jaar
"When the Nobel prize for physics [was announced], anyone wanting to read on one of the three winners would have drawn a blank on Wikipedia.
The physicist Donna Strickland was not deemed significant enough to merit her own page. When a Wikipedia user attempted to create a profile for her in March, it was denied by a moderator: “This submission’s references do not show that the subject qualifies for a Wikipedia article”.
After, Wikipedia scrambled to build a profile."
@doina
This is the kind of nonsense that inspired my colleagues at Scrapinghub to write ELI5:
https://github.com/TeamHG-Memex/eli5
With common linear models it can directly explain the scoring per-word and even highlight passages of text in a Jupyter notebook. It can also do black-box analysis. You can directly view the scores for a whole lexicon and pick up irrational biases from the dataset. I have used it.. would never now not-use it. :)
Amazon scraps secret AI recruiting tool that showed bias against women:
"Amazon’s system taught itself that male candidates were preferable. It penalized resumes that included the word “women’s,” as in “women’s chess club captain.”
Instead, the technology favored candidates who described themselves using verbs more commonly found on male engineers’ resumes, such as “executed” and “captured,” one person said."
'1984' through the eyes of a scientist https://www.utoday.nl/what-u-think/65965/1984-through-the-eyes-of-a-scientist
If anyone's interested in industrial experiences with #DataScience, see #DataFramed, DataCamp’s podcast:
https://www.datacamp.com/community/podcast
(Episode 23 is from Booking.com)
Assistant Professor in Computer Science
University of Twente, NL
Likes to mine, stress-test, and analyse complex networks.