ICYMI: The paper describes the initial COVID-19 open image data collection

https://www.profillic.com/paper/arxiv:2003.11597
@ArtificialIntelligenceArticles

Introduction to Reinforcement Learning
By DeepMind : https://youtube.com/watch?v=2pWv7GOvuf0&list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ
@ArtificialIntelligenceArticles
#ArtificialIntelligence #DeepLearning #ReinforcementLearning

NLP with Deep Learning
https://www.youtube.com/watch?v=9oTHFx0Gg3Q
@ArtificialIntelligenceArticles

COVID-19
This repository contains a curated list of resources for constructing mathematical models and doing analysis on COVID-19.
Adwait Naik, GitHub:
@ArtificialIntelligenceArticles https://github.com/addy1997/COVID-19
#AITaskForce #Covid19 #Covid19Resources

https://www.youtube.com/watch?v=V7CY68zH6ps
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 7 - Imitation Learning - YouTube

The Data Engineering team at GitHub is looking for a savvy Data Engineer to join our growing team. The hire will be responsible for expanding and optimizing our data and data pipeline architecture, as well as optimizing data flow and collection for cross functional teams. The ideal candidate is an experienced data pipeline builder and data wrangler who enjoys optimizing data systems and building them from the ground up. The Data Engineer will support our software developers, data analysts and data scientists on data initiatives and will ensure optimal data delivery architecture is consistent throughout ongoing projects. They must be self-directed and comfortable supporting the data needs of multiple teams, systems and products. The right candidate will be excited by the prospect of optimizing or even re-designing our company’s data architecture to support our next generation of products and data initiatives. If you have a passion for data and GitHub we'd love to talk to you. https://ai-jobs.net/job/1001-lead-data-engineer/

AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery
Wallach et al.: https://arxiv.org/abs/1510.02855
#MachineLearning #DeepLearning #Biomolecules

Data Science Interview Guide
https://towardsdatascience.com/data-science-interview-guide-4ee9f5dc778

https://t.iss.one/ArtificialIntelligenceArticles

NEED A JOB? LOOK HERE This is the most comprehensive list I’ve seen showing which companies are actively hiring, freezing hiring, or laying people off. #covid19 https://candor.co/hiring-freezes/

@ArtificialIntelligenceArticles

Advancing Self-Supervised and Semi-Supervised Learning with SimCLR

https://ai.googleblog.com/2020/04/advancing-self-supervised-and-semi.html

There are quotes from Yoshua Bengio, Samy Bengio, Rich Richard S. Sutton, Pieter Abbeel, Sergey Levine, David Cox, and me.
Some of my quotes: << “My money is on self-supervised learning,” he said, referring to computer systems that ingest huge amounts of unlabeled data and make sense of it all without supervision or reward. He is working on models that learn by observation, accumulating enough background knowledge that some sort of common sense can emerge. @ArtificialIntelligenceArticles
“Imagine that you give the machine a piece of input, a video clip, for example, and ask it to predict what happens next,” Dr. LeCun said in his office at New York University, decorated with stills from the movie “2001: A Space Odyssey.” “For the machine to train itself to do this, it has to develop some representation of the data. It has to understand that there are objects that are animate and others that are inanimate. The inanimate objects have predictable trajectories, the other ones don’t.”
After a self-supervised computer system “watches” millions of YouTube videos, he said, it will distill some representation of the world from them. Then, when the system is asked to perform a particular task, it can draw on that representation — in other words, it can teach itself.

https://www.nytimes.com/2020/04/08/technology/ai-computers-learning-supervised-unsupervised.html

https://t.iss.one/ArtificialIntelligenceArticles

Neural Voice Puppetry: Audio-driven Facial Reenactment
Thies et al.: https://arxiv.org/abs/1912.05566
#ArtificialIntelligence #DeepLearning #ReinforcementLearning

Friston said he always assumed his ideas about how neurons organize would be used to build more efficient neuromorphic computer chips—hardware that tries to mimic how the brain processes information much more closely than today’s standard computer chips do. The idea of trying to integrate biological neurons with semiconductors is not, Friston said, an idea he’d anticipated.
“But to my surprise and delight they have gone straight for the real thing,” he said of Cortical Labs’ use of real biological neurons. “What this group has been able to do is, to my mind, the right way forward to making these ideas work in practice.”

https://fortune.com/2020/03/30/startup-human-neurons-computer-chips/