90% of Americans think AI will destroy at least half of all jobs – but 91% say they don't think it will impact theirs. (v/@Gallup)

Experts from IBM, Microsoft & more met @MIT to discuss how to best prepare for the jobs of the future: https://t.co/9fDbChHWpA#mitworkofthefuture pic.twitter.com/gh5lkrmnqU

— MIT CSAIL (@MIT_CSAIL) November 25, 2019

Much of what’s being sold as "AI" today is snake oil. It does not and cannot work. In a talk at MIT yesterday, I described why this happening, how we can recognize flawed AI claims, and push back. Here are my annotated slides: https://t.co/iCpyFw5urN pic.twitter.com/pmOTI3vq8p

— Arvind Narayanan (@random_walker) November 19, 2019

A decade that has revived the field of AI is ending with the #AIDebate

AI DEBATE: Yoshua Bengio | Gary Marcus

“The Best Way Forward For AI”

RSVP for the live streaming event at https://t.co/aYw409Dnsb

Pre-readings available: https://t.co/oNiGnbp1YF#DeepLearning #MontrealAI pic.twitter.com/sRnqcyyOyv

— MONTREAL.AI (@Montreal_AI) December 11, 2019

INFOGRAPHIC: How much data is generated every minute by companies like Google, Uber and Netflix.

(credit @Domotalk v/@fisher85M) #IoT #DataScience #AI #BigData #ArtificialIntelligence #Analytics pic.twitter.com/F9P5XhsQ9W

— MIT CSAIL (@MIT_CSAIL) December 6, 2019

Want to help build a new benchmark for AI, that aims to push machines toward a deeper understanding? If yes, please contribute a few questions at Google Form below, and please pass this along 😀 Suggestions for improvements welcome!
🙏 https://t.co/VxCYPYTIkh

— Gary Marcus (@GaryMarcus) November 11, 2019

We’re sharing a new benchmark called MLQA to help extend performance improvements in extractive question-answering (QA) to more languages. It contains thousands of QA instances in Arabic, German, Hindi, Spanish, Vietnamese, and Simplified Chinese. https://t.co/qGSfOc30Co pic.twitter.com/XIITMxpWt8

— AI at Meta (@AIatMeta) November 23, 2019

#NLProc does not have a standard benchmark for interpretability. I am stoked to announce ERASER: the first-ever effort on unifying and standardizing NLP tasks with the goal of interpretability. https://t.co/8l74WNiYzm

— Nazneen Rajani (@nazneenrajani) November 8, 2019

We’ve developed one of the first systems that uses only 3D point clouds and achieves higher precision in detecting 3D objects than prior work. Read more: https://t.co/64RCwWsrcN. And we’ve open-sourced this model here: https://t.co/fJl0hXR5nq #ICCV2019 pic.twitter.com/kJgWROcyn8

— AI at Meta (@AIatMeta) November 5, 2019

This week at #NeurIPS2019, NVIDIA researchers will be presenting a rendering framework called DIB-R, which produces #3D objects from 2D images. Learn how DIB-R can produce an object in less than 100 milliseconds using machine learning techniques.

— NVIDIA AI (@NVIDIAAI) December 9, 2019

More than 90 #medicalimaging products using #AI are now cleared for clinical use. See the inception startups leading the way. #RSNA19 #RSNAI https://t.co/mrtp49FMUp
https://t.co/J4R9p0rtoD

— NVIDIA AI (@NVIDIAAI) December 3, 2019

We’re pleased to release the Visual Task Adaptation Benchmark (VTAB), a diverse, realistic, and challenging protocol to measure progress towards universal visual representations. Learn all about it below. https://t.co/PbORwSFPAg

— Google AI (@GoogleAI) November 6, 2019

We are happy to announce the v2.0 release of the Google Research Football Environment. The most exciting feature of this release is the Game Server, which lets your agent compete online with other researchers' models. Visit https://t.co/2ow3q98ZOP and give it a try! https://t.co/JMB9EwvqNU

— Google AI (@GoogleAI) November 26, 2019