In collaboration with @Jigsaw and in partnership w/ the FaceForensics video benchmark team, we are excited to release a large dataset of visual deepfakes to directly support deepfake detection efforts. Learn more and find the data at https://t.co/0faXdciuxC pic.twitter.com/m8vM3GGbdY

— Google AI (@GoogleAI) September 24, 2019

Generative models in Machine Learning are being used to create "deepfakes" that are increasingly realistic and difficult to detect. The presence of these faking technologies may significantly impact trust in the media and the democratic process.https://t.co/QXmCKELGQe

— CIFAR (@CIFAR_News) September 3, 2019

I'm really excited about the ongoing research in conversational AI.

Today Amazon released the largest topic-based conversational dataset to date: 10.7k conversations, 235k utterances, 4.7M words. All convos are linked to particular knowledge sources.https://t.co/xW72hRYo9e

— Chip Huyen (@chipro) September 18, 2019

“The biggest problem in AI? Machines have no common sense.” First in a terrific series of videos ⁦@bigthink⁩ launching https://t.co/C6XodPbRao https://t.co/SDCo1zKdnu

— Gary Marcus (@GaryMarcus) September 9, 2019

"Rebooting AI", by @GaryMarcus & Ernest Davis, is a delight. It makes a persuasive case for
humility in artificial intelligence research by reminding us of the extraordinary powers of human intelligence. This is cognitive science at its very best.https://t.co/suVtrbSRsX

— Paul Bloom (@paulbloomatyale) September 25, 2019

To help with animal conservation efforts, Oxford researchers developed a deep learning model that uses NVIDIA GPUs for both training and inference to classify individual chimpanzees and their sex with 93% accuracy. https://t.co/2WJq1nPfLG

— Nishant Goyal (@goyalnishant) September 25, 2019

We're honored to be among other amazing companies on the @Forbes #AI50 list of America's Most Promising AI Companies!

It's still *so* early, and we're working hard to make even more progress toward transforming the way disease is diagnosed and treated.https://t.co/6viCjLSSDU

— PathAI (@Path_AI) September 17, 2019

More progress! Harnessing the power of #AI to rapidly analyse cardiac #MRI images & identify patients at risk for #heart failure + revealing importance of #genetic factors https://t.co/d6NWGIWzOH #ArtificialIntelligence #research #health #MachineLearning @andi_staub @AINewsFeed pic.twitter.com/KQWDKsQwgu

— Prof. Sally Eaves (@sallyeaves) September 30, 2019

NVIDIA is partnering on the #SpeechBrain project with @MILAMontreal for accelerated development of #ASR and #NLP applications, providing flexibility through the Neural Modules toolkit. Learn how you can join and contribute: https://t.co/yMwpQ5GSWa https://t.co/Gqyqqkfc1p

— NVIDIA AI Developer (@NVIDIAAIDev) September 18, 2019

We’ve released datasets for scene decomposition & representation learning research: https://t.co/azecxAD6i5.

Each image comes with ground-truth masks & features for all objects. We hope to facilitate unsupervised learning in this area, building on models like MONet & IODINE. pic.twitter.com/gLAX902KWb

— Google DeepMind (@GoogleDeepMind) September 5, 2019