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AI in tweets – November and December 2019
We have collected some of the most interesting tweets about AI from the past couple of months.
NeurIPS
What I love about #NeurIPS is that all talks are made public available the same day at https://t.co/WD6I0o4emb. It helps those who could not attend (for immigration or financial reasons) or decided not to (to reduce their carbon footprint). Other conferences should emulate this!
— Chris Gorgolewski (@chrisgorgo) December 10, 2019
Indexing report on AI’s global impact
A new @IndexingAI report on #AI’s global impact is now live. #AIIndex2019 analyzes the technology’s broad impact on society, from national economies to autonomous vehicles. Read more on our blog: https://t.co/Ys8MBLZBmv pic.twitter.com/UAVYXyAedj
— Stanford HAI (@StanfordHAI) December 12, 2019
Discussion on the Impact of AI on the Future Job market
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
How to recognize flawed AI claims
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
AI debate
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
How much data is generated every minute
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
Building an AI benchmark for better understanding
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
Multillingual QA dataset
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
NLP Understanding Benchmark
#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
Detecting 3D objects
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
Producing 3D objects from 2D images
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 medical imaging products using AI getting approval for use
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
Visual Task Adaptation Benchmark
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
Google Research Football Environment
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
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