COMPSCI 282BR - Interpretability and Explainability in Machine Learning
Instructor : Hima Lakkaraju - https://interpretable-ml-class.github.io
#interpretability #artificialintelligence #machinelearning

A Guide for Ethical Data Science
A collaboration between the Royal Statistical Society (RSS) and the Institute and Faculty of Actuaries (IFoA) : https://www.statslife.org.uk/news/4292-rss-and-ifoa-publish-new-ethical-guidance-on-data-science
#datascience #ethics #society

How Does Huawei Rise to Core AI Challenges?
https://www.cio.com/article/3444197/how-does-huawei-rise-to-core-ai-challenges.html

"I'm sorry Dave, I'm afraid I can't do that" Deep Q-learning from forbidden action. https://arxiv.org/abs/1910.02078

Mapping (Dis-)Information Flow about the MH17 Plane Crash. https://arxiv.org/abs/1910.01363

Perturbations are not Enough: Generating Adversarial Examples with Spatial Distortions. https://arxiv.org/abs/1910.01329

"At least 40% of startups in Europe that claim to use AI are lying" - Verge

Read Article: https://www.theverge.com/2019/3/5/18251326/ai-startups-europe-fake-40-percent-mmc-report
Read this interesting 150 page report by MMC group: https://www.mmcventures.com/wp-content/uploads/2019/02/The-State-of-AI-2019-Divergence.pdf

WHAT’S STATE OF THE ART IN AUTOML IN 2019?
https://www.topbots.com/automl-state-of-the-art-2019/

Accelerating Federated Learning via Momentum Gradient Descent
Liu et al.: https://arxiv.org/abs/1910.03197
#ArtificialIntelligence #FederatedLearning #MachineLearning

TorchBeast: A PyTorch Platform for Distributed RL
Kuttler et al.: https://arxiv.org/abs/1910.03552
#DeepLearning #OpenAIGym #ReinforcementLearning

Spectral Inference Networks: Unifying Deep and Spectral Learning
Pfau et al. : https://arxiv.org/abs/1806.02215
#MachineLearning #DeepLearning #ArtificialIntelligence

Image Deduplicator
Tanuj Jain, Christopher Lennan, Zubin John and Dat Tran : https://github.com/idealo/imagededup
#DeepLearning #MachineLearning #Tensorflow

New book @ArtificialIntelligenceArticles

This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.
@ArtificialIntelligenceArticles

Top book by stuart Russell

A leading artificial intelligence researcher lays out a new approach to AI that will enable us to coexist successfully with increasingly intelligent machines

In the popular imagination, superhuman artificial intelligence is an approaching tidal wave that threatens not just jobs and human relationships, but civilization itself. Conflict between humans and machines is seen as inevitable and its outcome all too predictable.

In this groundbreaking book, distinguished AI researcher Stuart Russell argues that this scenario can be avoided, but only if we rethink AI from the ground up. Russell begins by exploring the idea of intelligence in humans and in machines. He describes the near-term benefits we can expect, from intelligent personal assistants to vastly accelerated scientific research, and outlines the AI breakthroughs that still have to happen before we reach superhuman AI. He also spells out the ways humans are already finding to misuse AI, from lethal autonomous weapons to viral sabotage.

If the predicted breakthroughs occur and superhuman AI emerges, we will have created entities far more powerful than ourselves. How can we ensure they never, ever, have power over us? Russell suggests that we can rebuild AI on a new foundation, according to which machines are designed to be inherently uncertain about the human preferences they are required to satisfy. Such machines would be humble, altruistic, and committed to pursue our objectives, not theirs. This new foundation would allow us to create machines that are provably deferential and provably beneficial.

In a 2014 editorial co-authored with Stephen Hawking, Russell wrote, "Success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last." Solving the problem of control over AI is not just possible; it is the key that unlocks a future of unlimited promise.

https://www.amazon.com/Human-Compatible-Artificial-Intelligence-Problem-ebook/dp/B07N5J5FTS

Stuart Russell discusses his newest book on the AI Alignment Podcast, Human Compatible: Artificial Intelligence and the Problem of Control.
Stuart Russell discusses his newest book on the AI Alignment Podcast, Human Compatible: Artificial Intelligence and the Problem of Control.
https://futureoflife.org/2019/10/08/ai-alignment-podcast-human-compatible-artificial-intelligence-and-the-problem-of-control-with-stuart-russell/?cn-reloaded=1