In these notes we describe heuristics to predict computational-to-statistical gaps in certain statistical problems. These are regimes in which the underlying statistical problem is information-theoretically possible although no efficient algorithm exists, rendering the problem essentially unsolvable for large instances. The methods we describe here are based on mature, albeit non-rigorous, tools from statistical physics. These notes are based on a lecture series given by the authors at the Courant Institute of Mathematical Sciences in New York City, on May 16th, 2017.

We hope you'll join us on May 9th at 10:00 AM ET in Duke's Trent Semans Center Great Hall for the first Duke Forge Health Data Science Symposium: Health Science and the Four Fs: Foibles, Frontiers, Fit-for-Purpose, and the Future of a Learning Health System. For those who are unable to attend in person, a remote videoconference option will be available. During the 3-hour workshop moderated by Forge Director Robert M. Califf, MD, our panel of experts will explore the intersection of classical statistics, machine learning, and the full spectrum of knowledge and tools needed to engage in actionable health data science. The symposium is free but registration is required. A detailed agenda is available on the Forge website.

Lucas asserted "... technology-enhanced teaching and learning can dramatically improve the quality and success of higher education ..." His Figure 1 and Figure 2, in outlining traditional versus technology-enhanced courses, suggested traditional teaching methods deliver a low-quality result, while professional (Hollywood) production methods deliver a high-quality result, with, again, no evidence provided. The idea of universities as "content producers" giving students "content" consisting of "course materials and exercises" gave me an analogous idea. Families give food and clothing to their children, but families are inefficient and can involve bloated administrations (parents). Just as parents do more than feed (they try to create an environment where their children can develop and thrive), universities likewise try to create a learning environment for students. Indispensable elements include laboratory work, fieldwork, real essays marked by real scholars (not against a list of bullet points), and project work.

Earlier I wrote about How to Reinvent Yout Career With AI Skills.This isn't a time to relax and think what should you learn next? Build skills around what's one of the most significant technologies of the coming decade –and that is Artificial Intelligence. Despite recent growth in interest, AI is a skill possessed by relatively few people. Many of the roles, needed skills and business titles of the future are unknown to us. Talent is no longer same as it used to be five years before.

The ability to manipulate and understand data is increasingly critical to discovery and innovation. As a result, we see the emergence of a new field--data science--that focuses on the processes and systems that enable us to extract knowledge or insight from data in various forms and translate it into action. In practice, data science has evolved as an interdisciplinary field that integrates approaches from such data-analysis fields as statistics, data mining, and predictive analytics and incorporates advances in scalable computing and data management. But as a discipline, data science is only in its infancy. The challenge of developing data science in a way that achieves its full potential raises important questions for the research and education community: How can we evolve the field of data science so it supports the increasing role of data in all spheres? How do we train a workforce of professionals who can use data to its best advantage? What should we teach them? What can government agencies do to help maximize the potential of data science to drive discovery and address current and future needs for a workforce with data science expertise?

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.