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://t.me/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://t.me/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

Corona Virus Media Watch launched by UNESCO’s International Research Centre on Artificial Intelligence in Slovenia
http://videolectures.net/site/news/corona_live/
https://t.me/ArtificialIntelligenceArticles

Help us scale #COVID19 detection over the phone.

If you have a #COVID19 diagnosis or are healthy, consider recording a breathing sample anonymously at
https://www.breatheforscience.com/


We hope this data leads to techniques to help diagnosis of #COVID19 over the phone.

Gradient Boosting Neural Networks: GrowNet
Badirli et al.: https://arxiv.org/abs/2002.07971v1
#ArtificialIntelligence #GradientBoosting #NeuralNetworks

Backpropagation, Intuitions (Stanford CS231n)
Link: http://cs231n.github.io/optimization-2/