Facebook fights dangerous pseudo-science.https://t.co/dNX0JxwhqD https://t.co/dNX0JxwhqD
— Yann LeCun (@ylecun) March 7, 2019
Facebook fights dangerous pseudo-science.https://t.co/dNX0JxwhqD https://t.co/dNX0JxwhqD
— Yann LeCun (@ylecun) March 7, 2019
Why Swift for @TensorFlow? A really good read by the engineering team.
— Radek Osmulski (@radekosmulski) March 7, 2019
I am not too fond of static typing, but everything else checks out. This will be a lot of fun 🙂https://t.co/ibAKZ4kVVv
An embarrassment to @JAMA_current . Ed care to comment? @ehlJAMA https://t.co/oKFaWABzHv
— Frank Harrell (@f2harrell) March 7, 2019
Great review by @pacoid of the history of data science and machine learning products: https://t.co/g3Olo8etBF
— Peter Skomoroch (@peteskomoroch) March 7, 2019
Just announced: "https://t.co/GEOZuodrZj Embracing Swift for Deep Learning".
— Jeremy Howard (@jeremyphoward) March 6, 2019
I'll be joined by @clattner_llvm in teaching Swift for @TensorFlow as part of the next @fastdotai course, where we'll be starting to build fastai for Swift!https://t.co/4zSsWmykz9 pic.twitter.com/ummP4WnjyQ
If you read one article about the tech worker movement, read this one. Features comments from awesome AI Now compatriot @mer__edith, and in-depth reporting from @AASchapiro ✨ https://t.co/HTgt6PrFF8
— Kate Crawford (@katecrawford) March 6, 2019
Why generative models is a fundamentally misguided approach for representation learning. https://t.co/VQAv43F1nP
— Sherjil Ozair (@sherjilozair) March 6, 2019
Coral: Google's low-end neural net inference ASIC "Edge TPU" in the form of a USB stick for $80 or a RaspberryPi-like dev board for $150 (both available from Mouser). Runs MobileNet v2 at 100 fps.
— Yann LeCun (@ylecun) March 6, 2019
Nice feature rundown at Hackaday:... https://t.co/kmTzdQw7i7
The most common AI is Mechanical Turk https://t.co/l2AZw1EHk2
— Chris Albon (@chrisalbon) March 6, 2019
I taught a lot of stats to v. sharp grad students & post docs from other depts. And, hands down, the most prevalent false hope was that by learning more stats they could 1. know exactly which analytical approach was "right" and 2. eliminate uncertainty/error in their results.
— Jenny Bryan (@JennyBryan) March 5, 2019
"Most engineers and data scientists still do not realize the collective power that they have. Engineers and data scientists are in high demand, and they should be leveraging this to push companies to be more ethical." https://t.co/oVoO929jk9 pic.twitter.com/4Lu3ieR7bc
— Rachel Thomas (@math_rachel) March 4, 2019
Married share of 25-37 year old Americans in
— Conrad Hackett (@conradhackett) March 4, 2019
1968 83%
1982 67%
1989 62%
2001 57%
2018 46% pic.twitter.com/2K4XWsKOkR