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

Unconstrained Monotonic Neural Networks

Antoine Wehenkel and Gilles Louppe : https://arxiv.org/abs/1908.05164

#NeuralNetworks #MachineLearning #NeuralComputing

12 NLP Researchers, Practitioners & Innovators You Should Be Following
Check out this list of NLP researchers, practitioners and innovators you should be following, including academics, practitioners, developers, entrepreneurs, and more.
https://www.kdnuggets.com/2019/08/nlp-researchers-practitioners-innovators-should-follow.html

Conditional Neural Processes
Garnelo et al.: https://arxiv.org/abs/1807.01613
1. Repo: https://github.com/deepmind/neural-processes
2. NoteBook: https://github.com/deepmind/neural-processes/blob/master/conditional_neural_process.ipynb
#ArtificialIntelligence #MachineLearning

A guide to convolution arithmetic for deep learning
Vincent Dumoulin and Francesco Visin : https://arxiv.org/pdf/1603.07285.pdf
#artificialintelligence #deeplearning #machinelearning

CS231n Convolutional Neural Networks for Visual Recognition

https://cs231n.github.io/neural-networks-3/#loss

Sports Matches & Artificial Intelligence
https://www.youtube.com/watch?v=kaslJ-8piSE&feature=youtu.be