Welcome SUPERGLUE from Facebook AI, DeepMind, University of Washington and New York University.
It comprises new ways to test creative approaches on a range of difficult NLP tasks and serves a series of benchmark tasks to measure the performance of modern, high performance language-understanding AI.

Made on the premise that deep learning models for conversational AI have “hit a ceiling” and need greater challenges .

Read https://arxiv.org/pdf/1905.00537.pdf

Neural Blind Deconvolution Using Deep Priors

https://arxiv.org/abs/1908.02197v1

Join the team working to make AI education accessible to the entire world


AI is the new electricity. Millions of AI engineers will be required to transform industries with artificial intelligence and we’re building the education platform to train them. deeplearning.ai wants to provide a world-class education to people around the globe so that we can all benefit from an AI-powered future.

deeplearning.ai is looking for a Full Stack Engineer with strong computer science fundamentals with a passion for improving learner's experiences. The ideal candidate will thrive in an early development stage of a leading educational environment focusing on Machine Learning related topics.

As a Full Stack Engineer you will be responsible for building and delivering high quality infrastructure and support to the technical content deeplearning.ai is providing. Our team is growing fast, and we are looking for a strong engineer to develop our educational products. In this role, you will work alongside a team of talented content creators as well as our outside partners, to build various layers of the infrastructure of world renowned AI driven education.
Here’s what you’ll do:

Develop a learner-centered design, (for both backend and frontend) ensuring reliability and scalability to deliver the best experience for the deeplearning.ai learner
Maintain quality and ensure responsiveness and scalability of the developed application
Design and develop internal tools to help our teams iterate quickly
Maintain a high-quality code base
Help develop backend infrastructure for grading tools and network training
Design UI interface and interaction flow of the learner-centered design
Evaluate usability and visual consistency of existing designs
Tackle complex user interaction problems and build simple, logical, and effective solutions
Here are the skills you should have:

Broad and solid CS foundation knowledge, including data structures & algorithms, OS, Computer Networks and databases
3+ years of software development
Proficiency in Python and NodeJS, React
3+ years of experience with general backend (Linux, Databases: Sequel, Application servers) and cloud infrastructure
Proficient in AWS (ec2, VPC, batch, lambda, cloudwatch)
Familiarity with Dockers and Jupyter Notebook
Strong ability to convert ideas to running code
Bachelor degree in CS or related technical field is required
The following would also be helpful, but isn't required:

Machine Learning knowledge
Familiarity with Serverless Computing
By working with us you will:

Be a part of a world-class technical team working alongside with offices in different parts of the world
Have the opportunity to consolidate a quickly growing startup
Have access to state of the art infrastructure and technology
Have access to top-level training, weekly technical reading groups lead by Andrew Ng and other senior engineers, and the opportunity to try high impact ideas
We hope you will fit well with our team’s culture:

Strong work ethic: All of us believe in our work’s ability to change human lives. Consequently we work not just smart, but also hard.
Growth mindset: We are eager to teach you new skills and invest in your continual development. But learning is hard work, so this is something we hope you’ll want to do.
Good team member: We care and watch out for each other. We’re humble individually, and go after big goals together.
Flexibility: You should be flexible in your tasks and do whatever is needed, ranging from lower-level tasks such as coordinating complicated schedules, to high-level work such as thinking through corporate strategy.
This is a full-time position based in or around Palo Alto, California. You must already have, or be able to obtain, authorization to work in the United States.


https://jobs.lever.co/landing/fe181d69-cbd0-4a33-b224-a6d466f9e767/apply

Superstition in the Network: Deep Reinforcement Learning Plays Deceptive Games
Bontrager et al.: https://arxiv.org/abs/1908.04436
#reinforcementlearning #deeplearning #artificialintelligence

“Semantic Image Synthesis with Spatially-Adaptive Normalization”
GauGAN, in a lighthearted nod to the post-Impressionist painter Paul Gauguin, turns doodles into stunning, photorealistic landscapes.
Park et al., 2019 : https://arxiv.org/abs/1903.07291
Code: https://github.com/NVlabs/SPADE
WebSite: https://nvlabs.github.io/SPADE/
#ArtificialIntelligence #DeepLearning #GenerativeDesign #PyTorch

Phyre: a benchmark for physical reasoning.

Think of it as the games Incredible Machine or Crayon Physics for AI systems.
https://phyre.ai/

Speak better with Artificial Intelligence - Automatic speech recognition (ASR) systems from google AI and ALSTDI
work, Project Euphonia for slurred speech and those with accents.
It is a speech-to-text transcription service for people with speaking impairments.
71% of the improvement comes from only five minutes of training data.

Read at https://arxiv.org/pdf/1907.13511.pdf

A day at the beach: Deep learning for a child
https://buff.ly/2H8hYwP

PHYRE: A New Benchmark for Physical Reasoning
By: Anton Bakhtin, Laurens van der Maaten, Justin Johnson, Laura Gustafson and Ross Girshick. Facebook AI Research : https://research.fb.com/publications/phyre-a-new-benchmark-for-physical-reasoning/
Demo: https://player.phyre.ai
#DeepLearning #Physics #ReinforcementLearning

Does the brain do backpropagation? CAN Public Lecture - Geoffrey Hinton

One of the best recent talks of Prof. geoffrey hinton
online on computation in the brain. Intriguingly, the proposed relation between the neuron firing rate and the error signal looks quite similar to the Euler-Lagrange equation of motion in Physics.

https://www.youtube.com/watch?v=qIEfJ6OBGj8

@ArtificialIntelligenceArticles

Visualizing and Measuring the Geometry of BERT
Coenen et al.: https://arxiv.org/abs/1906.02715
#BERT #NaturalLanguageProcessing #UnsupervisedLearning

An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents

Such et al.: https://arxiv.org/abs/1812.07069

Code: https://github.com/uber-research/atari-model-zoo

Blog: https://eng.uber.com/atari-zoo-deep-reinforcement-learning/

#ArtificialIntelligence #DeepLearning #ReinforcementLearning

Deep Kernel Learning for Clustering
Wu et al.: https://arxiv.org/pdf/1908.03515v1.pdf
#DeepLearning #MachineLearning #NeuralNetworks

"One-shot Face Reenactment"
Zhang et al.: https://arxiv.org/abs/1908.03251
Project: https://wywu.github.io/projects/ReenactGAN/OneShotReenact.html
GitHub: https://github.com/bj80heyue/One_Shot_Face_Reenactment
#ArtificialIntelligence #DeepLearning #MachineLearning

Creative improvisation is at the root of jazz and science.

https://www.openculture.com/2016/07/the-secret-link-between-jazz-and-physics-how-einstein-coltrane-shared-improvisation-and-intuition-in-common.html

Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real
Nachum et al.: https://arxiv.org/abs/1908.05224
#Robotics #ArtificialIntelligence #MachineLearning

Something really really cool!🙂
#weekend_read
Paper-Title: Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real #GoogleAI
Link to the paper: https://arxiv.org/pdf/1908.05224.pdf
Link to the videos: https://sites.google.com/view/manipulation-via-locomotion
TL;DR: They have presented successful zero-shot transfer of policies trained in simulation to perform difficult locomotion and manipulation via locomotion tasks. The key to their method is the imposition of hierarchy, which introduces modularity into the domain randomization process and enables the learning of increasingly complex behaviours.

Best Resources for Getting Started With GANs
https://machinelearningmastery.com/resources-for-getting-started-with-generative-adversarial-networks/

Pytorch Implementation of Autoregressive Language Model
https://github.com/lyeoni/pretraining-for-language-understanding