Amazing work on generative adversarial networks by Tero Karras, Samuli Laine and Timo Aila of NVIDIA. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. #education #professionals #careers #artificialintelligence #deeplearning #datascience #machinelearning #ML #Algorithm #Python #R #professional #industry #bigdata #ai #community #workforce

The research paper is available : http://stylegan.xyz/paper

Video link : https://www.youtube.com/watch?v=kSLJriaOumA

Deep learning is good at finding patterns in reams of data, but can't explain how they're connected. Turing Award winner Yoshua Bengio wants to change that.
https://www.wired.com/story/ai-pioneer-algorithms-understand-why/
#DeepLearning #AI

Grandmaster level in StarCraft II using multi-agent reinforcement learning

#AI #artificialintelligence
#DeepLearning #ReinforcementLearning
#deepmind

Blog: https://deepmind.com/blog/article/AlphaStar-Grandmaster-level-in-StarCraft-II-using-multi-agent-reinforcement-learning

https://www.nature.com/articles/s41586-019-1724-z

Deep Learning for Computational Chemistry

Garrett B. Goh, Nathan Oken Hodas, Abhinav Vishnu

Published in Journal of Computational… 2017

DOI:10.1002/jcc.24764

Arxiv Free Download:
https://arxiv.org/abs/1701.04503

Paywall:
https://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.24764

#deeplearning #AI #artificialintelligence #chemistry #computationalchemistry

In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties that distinguish them from traditional machine learning algorithms used in cheminformatics.

By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including quantitative structure activity relationship, virtual screening, protein structure prediction, quantum chemistry, materials design, and property prediction.

In reviewing the performance of deep neural networks, we observed a consistent outperformance against non-neural networks state-of-the-art models across disparate research topics, and deep neural network-based models often exceeded the "glass ceiling" expectations of their respective tasks.

US National Security Commission on Artificial Intelligence

Interim Report for Congress, November 2019

#AI #ArtificialIntelligence #Security #NSCAI

https://www.nationaldefensemagazine.org/-/media/sites/magazine/03_linkedfiles/nscai-interim-report-for-congress.ashx?la=en

Intel unveils its first chips built for AI in the cloud

Intel launching two #AI-oriented chips such as #NNPT1000 & #NNPI1000, the first #ASICs designed explicitly for #AI in the #cloud & a next-gen #Movidius Vision Processing unit will significantly bolster performance of machines working on AI platforms. https://www.engadget.com/2019/11/12/intel-nervana-chips-for-ai-in-cloud/

https://t.me/ArtificialIntelligenceArticles

Neurons spike back

By Dominique Cardon, Jean-Philippe Cointet and Antoine Mazières.

2018

In the tumultuous history of AI, learning techniques using so-called "connectionist" neural networks have long been mocked and ostracized by the "symbolic" movement. This article retraces the history of artificial intelligence through the lens of the tension between symbolic and connectionist approaches.

From a social history of science and technology perspective, it seeks to highlight how researchers, relying on the availability of massive data and the multiplication of computing power have undertaken to reformulate the symbolic AI project by reviving the spirit of adaptive and inductive machines dating back from the era of cybernetics.

#artificialintelligence #AI #connectionists #symbolicAI #neuralnetworks #expertsystems #historyofAI

https://neurovenge.antonomase.fr/

This is an exhaustive list of Monte Carlo tree search papers from major conferences including NIPS, ICML, and AAAI. Some of them with publicly available implementations.

https://github.com/benedekrozemberczki/awesome-monte-carlo-tree-search-papers

#datascience #machinelearning #deeplearning #python #ai #analytics #datamining

Mathematics for Machine Learning

Free Download Printed Book Cambridge University Press
https://mml-book.github.io/


#artificialintelligence #AI #Mathematics #calculus #linearalgebra #deeplearning #machinelearning

How To Build Your Own MuZero AI Using Python (Part 1/3)
Blog by David Foster : https://medium.com/applied-data-science/how-to-build-your-own-muzero-in-python-f77d5718061a
#MachineLearning #DeepLearning #DataScience #ArtificialIntelligence #AI