Carla: Open-source simulator for autonomous driving research
Github:https://github.com/carla-simulator/carla
Paper:http://proceedings.mlr.press/v78/dosovitskiy17a/dosovitskiy17a.pdf

TensorFlow Quantum

https://www.tensorflow.org/quantum/

Blog on "Architecture Comparison of AlexNet, VGGNet, ResNet, Inception, DenseNet" in Towards Data Science.

https://towardsdatascience.com/architecture-comparison-of-alexnet-vggnet-resnet-inception-densenet-beb8b116866d

Demographic Bias in Biometrics: A Survey on an Emerging Challenge
The main contributions of this article are:
an overview of the topic of algorithmic bias in the context of biometrics,
a comprehensive survey of the existing literature on biometric bias estimation and mitigation,
a discussion of the pertinent technical and social matters, and
an outline of the remaining challenges and future work items, both from technological and social points of view.
https://arxiv.org/pdf/2003.02488.pdf

Yann LeCun

A 14 year old girl born without a left hemisphere exhibits normal language abilities.

This would suggest that language acquisition abilities are not due to some sort of specifically pre-wired circuits in the left hemisphere (where language-related areas are normally found).

https://www.sciencedirect.com/science/article/pii/S0010945220300678

Optimizing JPEG Quantization for Classification Networks. http://arxiv.org/abs/2003.02874

Accelerator-aware Neural Network Design using AutoML. http://arxiv.org/abs/2003.02838

Guided Generative Adversarial Neural Network for Representation Learning and High Fidelit... http://arxiv.org/abs/2003.02836

Machine Learning: A New Approach to Drug Discovery | Daphne Koller | WiDS 2020
https://www.youtube.com/watch?v=V6bSlPNwrKo

Daphne Koller: Machine Learning: A New Approach to Drug Discovery
https://www.youtube.com/watch?v=xz1RWYvHi8g

Better, faster, and cheaper drug discovery with machine learning by AstraZeneca
https://www.youtube.com/watch?v=qH3z5GwccxE

#CarnegieMellon's Delphi research group is extending the forecast of influenza-like illness in anticipation of #COVID19 with the use of statistical #machinelearning.

Learn more →

https://www.ml.cmu.edu/news/news-archive/2020/march/using-machine-learning-cdc-forecasting-flu-covid19-illness.html

https://news.1rj.ru/str/ArtificialIntelligenceArticles

DeepMind, Google’s London-based AI research unit, has published predictions of the structure of proteins associated with SARS-CoV-2, in the hope that they help scientists understand how the new coronavirus functions, and allow for more precise investigation into potential treatments.

The company used its AlphaFold system, which applies machine learning techniques to estimate the physical structure of proteins, to generate the predictions, which it has published without the normal, time consuming, review or verification process for such work.
https://deepmind.com/research/open-source/computational-predictions-of-protein-structures-associated-with-COVID-19
“Knowing a protein’s structure provides an important resource for understanding how it functions, but experiments to determine the structure can take months or longer, and some prove to be intractable,” the researchers wrote in a post accompanying the publication.

“For this reason, researchers have been developing computational methods to predict protein structure from the amino acid sequence.”

https://www.theguardian.com/world/live/2020/mar/05/coronavirus-live-updates-italy-germany-pandemic-europe-uk-us-australia-india-update-latest-news

paper :

https://www.nature.com/articles/s41586-019-1923-7.epdf

Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data
Article: https://eng.uber.com/generative-teaching-networks/

The creativity of citizen scientists could help researchers design proteins that may be able to fight the SARS-CoV-2 virus.
Researchers are calling on citizen scientists to play a free online game called Foldit, in which they help design and identify proteins that may be able to bind to and neutralize the SARS-CoV-2 spike protein that it uses to invade host cells. The scientists hope that players’ creations will yield insights that will allow them to create an effective antiviral therapy for COVID-19. Other researchers are asking citizens for help in a more passive way. The Scientist spoke with Brian Koepnick, who works on Foldit at the University of Washington Institute for Protein Design, about this project.
The Scientist: What is Foldit? How does it work?
Brian Koepnick: Foldit is a free, online game that anyone in the world can download and run on their Mac, Linux, or Windows PC. The main drive of Foldit is our science puzzles. These are weekly challenges that we refresh every week . . . that are directly related to research we’re doing here in the lab at the Institute for Protein Design or in our other labs. Foldit players can participate in the science puzzles. . . [which] are constructed in such a way that competing players who develop high-scoring solutions make meaningful research contributions.
Continue reading in the article link
https://www.the-scientist.com/news-opinion/scientists-use-online-game-to-research-covid-19-treatment-67230
@ArtificialIntelligenceArticles

"In a new paper a team of researchers from Insilico Medicine present a new model called GENTRL (https://github.com/insilicomedicine/GENTRL) for molecule generation. This algorithm, given a protein target, has generated 6 viable compounds in 21 days, and after 25 more days of synthesis and testing, 4 passed the preliminary tests; the most potent one was tested on live mice, and its predicted biological and chemical properties were confirmed."



https://www.nature.com/articles/s41587-019-0224-x

AI Can Detect Coronavirus Infections Far Faster Than Humans
https://www.freethink.com/articles/detect-coronavirus
https://news.1rj.ru/str/ArtificialIntelligenceArticles