Abstract-- We propose the use of Bayesian networks, which provide both a mean value and an uncertainty estimate as output, to enhance the safety of learned control policies under circumstances in which a test-time input differs significantly from the training set. Our algorithm combines reinforcement learning and end-to-end imitation learning to simultaneously learn a control policy as well as a threshold over the predictive uncertainty of the learned model, with no hand-tuning required. Corrective action, such as a return of control to the model predictive controller or human expert, is taken when the uncertainty threshold is exceeded. We demonstrate that our method is robust to uncertainty resulting from varying system dynamics as well as from partial state observability. As the deployment of deep neural networks as controllers for physical robotic systems becomes more prevalent, the issue of safety within artificial intelligence becomes an increasingly important concern. Recently the use of end-to-end imitation learning to develop neural network control policies has surged in popularity, due in large part to the ease with which deep models can learn complex dynamics and infer global state from local data while bypassing the need for significant parameter tuning. In contrast, traditional approaches to vision-based control rely on methods such image segmentation and object detection, classification, labeling, and filtering; often, these methods require significant engineering and tuning.

Recently, AAAI coordinated and The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) cosigned a statement with CRA, and the Thirty-First Conference on Innovative Applications of Artificial expressing concern about the proposed Intelligence (IAAI-19), will be held in Honolulu, Hawaii, USA, January tax bill and its ramifications for graduate 27 - February 1, 2019. The technical conference will continue its student stipends. Other organizational 3.5-day schedule, preceded by the workshop and tutorial programs.

The deluge of online and offline misinformation is overloading the exchange of ideas upon which democracies depend. Fake news, conspiracy theories, and deceptive social bots proliferate, facilitating the manipulation of public opinion. Countering misinformation while protecting freedom of speech will require collaboration across industry, journalism, and academia. The Workshop on Digital Misinformation — held in May 2017 in conjunction with the International Conference on Web and Social Media in Montréal, Québec, Canada — was intended to foster these efforts. The meeting brought together more than 100 stakeholders from academia, media, and tech companies to discuss the research challenges implicit in building a trustworthy Web. Below we outline the main findings from the discussion.

Machine learning is a notoriously complex subject, which usually requires a great deal of advanced math and software development skills. That's why it's so amazing that Azure Machine Learning Studio lets you train and deploy machine learning models without any coding, using a drag-and-drop interface. With this web-based software, you can create applications for predicting everything from customer churn rates, to image classifications, to compelling product recommendations. In this course, you will learn the basic concepts of machine learning, and then follow hands-on examples of choosing an algorithm, running data through a model, and deploying a trained model as a predictive web service.

WORLDWIDE revenue from AI will surge past US$46 billion in 2020, according to research firm IDC. In Asia-Pacific, this is projected to rise to US$6.8 billion by 2021. Though researchers have been working on AI decades, development has accelerated in the past few years thanks to three factors – the ubiquitous availability of data, the growing capabilities of cloud computing, and more powerful algorithms developed by AI researchers. Most recently, a team of Microsoft researchers have developed the first machine translation system that can translate sentences of news articles from Chinese to English with the same quality and accuracy as a person. Throughout history, the emergence of new technologies has been accompanied by dire warnings about human redundancy.

Word embeddings provide a dense representation of words and their relative meanings. They are an improvement over sparse representations used in simpler bag of word model representations. Word embeddings can be learned from text data and reused among projects. They can also be learned as part of fitting a neural network on text data. In this tutorial, you will discover how to use word embeddings for deep learning in Python with Keras.

Artificial intelligence has the potential to improve people's lives in profound ways -- from helping diagnose diseases and breaking down language barriers to making businesses more efficient. Google believes that AI will help tackle huge challenges like healthcare, environmental protection and other social and developmental problems, while also spurring innovation for businesses and developers. The opportunity is huge and not constrained by location – a company in Bangalore or Gurgaon could serve the whole world. In fact, a recent report by Accenture concluded that India, by embracing AI technologies could add nearly $1 trillion to its GDP by 2035. India already has some of the key ingredients to becoming a major force in leading the next generation of disruptive innovation in machine learning (ML): a tech-savvy talent pool, renowned universities, healthy levels of entrepreneurship and strong corporations.

Computer vision has become one of the largest computer science research communities. We have made tremendous progress in recent years over a wide range of areas, including object recognition, image understanding, video analysis, 3D reconstruction, etc. It has also become one of the largest computer science research community. However, despite the expansion of our field, the percentage of female faculty members and researchers both in academia and in industry is still relatively low. As a result, many female researchers working in computer vision may feel isolated and do not have a lot of opportunities to meet with other women. The half-day workshop on Women in Computer Vision is a gathering for both women and men working in computer vision.

There are tons of resources to help you learn the different aspects of R, and as a beginner this can be overwhelming. It's also a dynamic language and rapidly changing, so it's important to keep up with the latest tools and technologies. That's why R-bloggers and DataCamp have worked together to bring you a learning path for R. Each section points you to relevant resources and tools to get you started and keep you engaged to continue learning. Just like R, this learning path is a dynamic resource.

Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application. The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists. The review begins by covering fundamental concepts in ML and modern statistics such as the bias-variance tradeoff, overfitting, regularization, and generalization before moving on to more advanced topics in both supervised and unsupervised learning. Topics covered in the review include ensemble models, deep learning and neural networks, clustering and data visualization, energy-based models (including MaxEnt models and Restricted Boltzmann Machines), and variational methods. Throughout, we emphasize the many natural connections between ML and statistical physics. A notable aspect of the review is the use of Python notebooks to introduce modern ML/statistical packages to readers using physics-inspired datasets (the Ising Model and Monte-Carlo simulations of supersymmetric decays of proton-proton collisions). We conclude with an extended outlook discussing possible uses of machine learning for furthering our understanding of the physical world as well as open problems in ML where physicists maybe able to contribute. (Notebooks are available at https://physics.bu.edu/~pankajm/MLnotebooks.html )