CORD-19: A Kaggle challenge for the Natural Language Processing community.

Dataset of 29,000 scholarly article (including 13,000 with full text), about COVID-19 and related coronaviruses.
Help answers questions like "what do we know about COVID-19 risk factors?" by mining the literature.
@ArtificialIntelligenceArticles
Created by the Allen Institute for AI in partnership with the Chan-Zuckerberg Initiative, Georgetown University, Microsoft Research, and the National Library of Medicine.
https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge
https://news.1rj.ru/str/ArtificialIntelligenceArticles

White House urges researchers to use AI to analyze 29,000 coronavirus papers
Paresh Dave, Reuters: https://www.reuters.com/article/us-health-coronavirus-tech-research-idUSKBN2133E6
#ArtificialIntelligence #CoronaVirus #CoronaOutbreak #DeepLearning #MontrealAI

EfficientDet: Scalable and Efficient Object Detection
New scaling techniques:
Backbone: we employ the more advanced EfficientNets as our backbone networks.
BiFPN: we propose a new bi-directional feature network, named BiFPN, to enable easy and fast feature fusion. In addition to the bi-directional topology, we also propose a new fast normalized fusion that enables better fusion with negligible latency cost.
Scaling: we propose to use a single compound scaling factor to govern the network depth, width, and resolution for all backbone, feature network, and prediction networks.
Paper: https://arxiv.org/abs/1911.09070
Code: https://github.com/google/automl/tree/master/efficientdet

Application of Machine Learning in monitoring and fighting COVID-19
Big Data Helps Taiwan Fight Coronavirus: https://spectrum.ieee.org/the-human-os/biomedical/devices/big-data-helps-taiwan-fight-coronavirus
Satellites and AI Monitor Chinese Economy’s Reaction to Coronavirus: https://spectrum.ieee.org/view-from-the-valley/artificial-intelligence/machine-learning/satellites-and-ai-monitor-chinese-economys-reaction-to-coronavirus
Could Supercomputers and Rapid Treatment Trials Slow Down Coronavirus? https://spectrum.ieee.org/the-human-os/biomedical/devices/could-supercomputers-and-rapid-treatment-trials-slow-down-coronavirus
Is Coronavirus Speeding the Adoption of Driverless Technology? https://spectrum.ieee.org/cars-that-think/transportation/self-driving/will-coronavirus-speed-adoption-of-driverless-technology

NSERC Presents 2 Minutes with Geoffrey Hinton
https://www.youtube.com/watch?v=Z_JFUuSet4U

Deep Reinforcement Learning Course
https://simoninithomas.github.io/Deep_reinforcement_learning_Course/

This Artificial Intelligence solution developed by Coimbatore- based AES Technologies needs no swab test, kits or laboratories that take days to issue the results. A CT scan is all that it takes to determine whether you and positive or negative and this, in LESS THAN A MINUTE!


https://www.youtube.com/watch?v=fqyFWQNqWpI

COVID-19 Chatbot
"Training a chatbot who will be able to answer questions from the public about COVID-19"
Just ask any questions you have:
https://covid19.dialogue.co/?lng=en#/info
@ArtificialIntelligenceArticles
Thousands of questions are needed to train this chatbot well :)
H / T Sasha Lu
#Chatbot #covid19 #DeepLearning

The CORD-19 dataset on Kaggle contains over 44,000 scholarly articles, and Kaggler Daniel Wolffram has created several widgets that are helpful in navigating the current COVID-19 literature:
https://www.kaggle.com/danielwolffram/topic-modeling-finding-related-articles
https://news.1rj.ru/str/ArtificialIntelligenceArticles

I look for the good in people. Sometimes I get hurt for it, but it's rare and it's worth it. You may hear me say optimistic things that sound naive. I'm not naive. I've read too much history to be naive. I just think love wins out over the darker parts of human nature in the end.
@lexfridman

Coronavirus Datasets from Every Country with Confirmed Cases
@ArtificialIntelligenceArticles

https://lionbridge.ai/datasets/coronavirus-datasets-from-every-country/

Regarding the continuation of my Supersymmetric artificial neural network model that I began in ~2016, here are some discussions of mine on Physics forums in 2017, as well as other resources:

1. Forum question of mine that was discussed: "Is it possible to create a ‘Transverse Field Ising Spin’-compatible Super Hamiltonian?"

https://www.physicsoverflow.org/39603/possible-create-transverse-ising-compatible-hamiltonian

2. Forum question of mine that was discussed: "Can One Compose A ‘Transverse Field Ising Spin’-Compatible Super Hamiltonian?"

http://www.scienceforums.com/topic/30421-can-one-compose-a-%E2%80%98transverse-field-ising-spin%E2%80%99-compatible-super-hamiltonian/

3. The Supersymmetric artificial neural network model:

https://github.com/JordanMicahBennett/Supersymmetric-artificial-neural-network

4. Mitchell Porter's view on the Supersymmetric Artificial Neural Network etc (Section 5 "USE OF SUPERMATH IN MACHINE LEARNING?"):

https://www.researchgate.net/publication/332103958_2019_Applications_of_super-mathematics_to_machine_learning

Deep Multi-Agent Reinforcement Learning for Decentralized Continuous Cooperative Control
Christian Schroeder de Witt et al.: https://arxiv.org/abs/2003.06709
#MachineLearning #ArtificialIntelligence #ReinforcementLearning

CS472 Data science and AI for COVID-19
Zou et al.: https://sites.google.com/view/data-science-covid-19/
#ArtificialIntelligence #Covid19 #DataScience https://news.1rj.ru/str/ArtificialIntelligenceArticles

Lucid
A collection of infrastructure and tools for research in neural network interpretability : https://github.com/tensorflow/lucid
#Tensorflow #Interpretability #Visualization #MachineLearning #Colab

New open source dataset for abstractive summarisation

https://medium.com/curation-corporation/teaching-an-ai-to-abstract-a-new-dataset-for-abstractive-auto-summarisation-5227f546caa8

Representation Learning Through Latent Canonicalizations
Litany et al.: https://arxiv.org/abs/2002.11829
#ArtificialIntelligence #DeepLearning #RepresentationLearning

Unboxing the "Black Box": Learning Interpretable Deep Learning Features of Brain Aging
https://prism.ucalgary.ca/handle/1880/111255