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We’ve studied gender and STEM for 25 years. The science doesn’t support the Google memo. - Recode
"We have been researching issues of gender and STEM for more than 25 years, and we can say flatly that there is no evidence that women’s biology makes them incapable of performing at the highest levels in any STEM fields."
damore  google  diversity  feminism  work  recode  science  2017 
yesterday by handcoding
5 Tips To Survive High School | Best Images Collections HD For Gadget windows Mac Android
5 Ideas To Endure Significant Faculty We’ve bought some tips to assist you survive higher university. GMM#510! Fantastic Legendary A lot more: http://youtu.be/KfrAT6I1x94 Ever have a #VacayGoneCrayCray? Go to http://ift.tt/1pFfNnz to enter for a chance to get $10k towards a journey redo, courtesy of Decision Inns. SUBSCRIBE for each day episodes: http://little bit.ly/subrl2 **** Past […]
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yesterday by wotek
Nautilus | Science Connected
Nautilus is a different kind of science magazine. It delivers deep, undiluted, narrative storytelling to bring science into the largest and most important conversations we are having today. It challenges the reader to consider the connecting tissue that runs through the sciences and connects them to philosophy, culture and art.
culture  magazine  science 
yesterday by kdredington
That Alien Message - Less Wrong
Followup to:  Einstein's Speed
Imagine a world much like this one, in which, thanks to gene-selection technologies, the average IQ is 140 (on our scale).  Poten
science  ai  singularity  scifi  bayes  math  complexity 
yesterday by geetarista
Detecting Tanks
There's a story that's passed around to illustrate the ways machine learning can pick up on features in your dataset that you didn't expect, and probably gained the most exposure through Yudkowsky using it in "Artificial Intelligence as a Positive and Negative Factor in Global Risk" (pdf, 2008):

Once upon a time, the US Army wanted to use neural networks to automatically detect camouflaged enemy tanks. The researchers trained a neural net on 50 photos of camouflaged tanks in trees, and 50 photos of trees without tanks. Using standard techniques for supervised learning, the researchers trained the neural network to a weighting that correctly loaded the training set—output "yes" for the 50 photos of camouflaged tanks, and output "no" for the 50 photos of forest. This did not ensure, or even imply, that new examples would be classified correctly. The neural network might have "learned" 100 special cases that would not generalize to any new problem. Wisely, the researchers had originally taken 200 photos, 100 photos of tanks and 100 photos of trees. They had used only 50 of each for the training set. The researchers ran the neural network on the remaining 100 photos, and without further training the neural network classified all remaining photos correctly. Success confirmed! The researchers handed the finished work to the Pentagon, which soon handed it back, complaining that in their own tests the neural network did no better than chance at discriminating photos.
It turned out that in the researchers' dataset, photos of camouflaged tanks had been taken on cloudy days, while photos of plain forest had been taken on sunny days. The neural network had learned to distinguish cloudy days from sunny days, instead of distinguishing camouflaged tanks from empty forest.
bias  machinelearning  ai  tanks  science  datascience  data 
yesterday by timcowlishaw

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