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

Buffalo University Comprehensive Lecture Slides for Machine Learning and Deep Learning

By Professor Sargur Srihari

Machine Learning:
https://cedar.buffalo.edu/~srihari/CSE574/

Deep Learning:
https://cedar.buffalo.edu/~srihari/CSE676/index.html

Probabilistic Graphical Models:
https://cedar.buffalo.edu/~srihari/CSE674/

Data Mining:
https://cedar.buffalo.edu/~srihari/CSE626/index.html

#machinelearning #deeplearning #datamining #AI #artificialintelligence

What a statement: $1,000,000 prize money at the Kaggle "Deepfake Detection Challenge" – Identifying videos with facial or voice manipulations.
@ArtificialIntelligenceArticles
"These content generation and modification technologies may affect the quality of public discourse and the safeguarding of human rights—especially given that deepfakes may be used maliciously as a source of misinformation, manipulation, harassment, and persuasion. Identifying manipulated media is a technically demanding and rapidly evolving challenge that requires collaborations across the entire tech industry and beyond.

AWS, Facebook, Microsoft, the Partnership on AI’s Media Integrity Steering Committee, and academics have come together to build the Deepfake Detection Challenge (DFDC)."

https://www.kaggle.com/c/deepfake-detection-challenge
#AI #deeplearning #deepfakes #kaggle https://t.me/ArtificialIntelligenceArticles

What is adversarial machine learning, and how is it used today?

-Generative modeling, security, model-based optimization, neuroscience, fairness, and more!

Here's a fantastic video overview by Ian Goodfellow.
http://videos.re-work.co/videos/1351-ian-goodfellow
#ML #adversarialML #AI #datascience

Some of the brightest minds in #AI express their hopes for 2020🔝

Good read https://blog.deeplearning.ai/blog/the-batch-happy-new-year-hopes-for-ai-in-2020-yann-lecun-kai-fu-lee-anima-anandkumar-richard-socher

Machine Learning Unlocks Library of The Human Brain. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #NeuroScience


http://thetartan.org/2019/11/11/scitech/brain-thoughts

Can an #AI deep neural network be trained to diagnose low blood sugar from the ECG signal?
https://go.nature.com/2NrqzxM

Fingerpicks are never pleasant and in some circumstances are particularly cumbersome. Taking fingerpick during the night certainly is unpleasant, especially for patients in paediatric age.

"Our innovation consisted in using artificial intelligence for automatic detecting hypoglycaemia via few ECG beats. This is relevant because ECG can be detected in any circumstance, including sleeping."
https://m.medicalxpress.com/news/2020-01-ai-glucose-ecg-fingerprick.html

Data used to train #AI can contain implicit racial, gender, or ideological biases. How can we champion processes to remove bias from AI?

https://www.anaconda.com/machine-learning-bias-fairness/

Neural network based finger counting technique
#deeplearning #machinelearning #ai
Abstract: https://www.ijser.org/researchpaper/Neural-Network-based-finger-counting-technique.pdf

How can we guide #AI to learn the way humans do? Research from last year looked at the brain's structure to find out. Read more in this

https://science.sciencemag.org/content/363/6428/692

How many gender subtypes exist in the brain?

Combining fMRI and behavioral data, researchers examined gender identity in cisgender & transgender individuals using machine learning. #AI identified at least nine dimensions of brain-gender variation.

https://neurosciencenews.com/machine-learning-gender-15717/amp/

ข้อมูลวีดีโอ หาก Model รู้เข้าใจระดับความลึกและรูปทรงจะสามารถ ทำ Augmented เติมเข้าไปในวีดีโอได้อย่างน่าสนใจ

https://www.youtube.com/watch?v=51CQObCd_K0&feature=youtu.be&fbclid=IwAR3UHcxiphy2OnhHpcKZSf4zYB-nW8PHyPHBgxcltw-8SCpi8z0sQ8mGtaw

Enzyme, a compiler plug-in for importing foreign code into systems like TensorFlow & PyTorch without having to rewrite it. v/@MIT_CSAIL

Paper: http://bit.ly/EnzymePDF

More: http://bit.ly/EnzymeML

#ML #MachineLearning #PyTorch #TensorFlowJS #NeurIPS #tensorflow #AI

#AI_Researchers is all you need!!!

https://t.me/ArtificialIntelligenceArticles