This paper describes a new parameter-free online learning algorithm for changing environments. In comparing against algorithms with the same time complexity as ours, we obtain a strongly adaptive regret bound that is a factor of at least $\sqrt{\log(T)}$ better, where $T$ is the time horizon. Empirical results show that our algorithm outperforms state-of-the-art methods in learning with expert advice and metric learning scenarios.

In my 25 years of working with large datasets, from developing early machine learning algorithms for multimedia systems in the 1990s to optimizing the email marketing infrastructure at GSI Commerce in the 2000s and now applying machine learning to big data to find actionable insights in real time, I've seen the convergence of machine learning and marketing firsthand. This year, I'm excited to see how machine learning (ML), an artificial intelligence (AI) discipline geared toward the technological development of human knowledge, has impacted the marketing big data ecosystem. I'm also intrigued by how much room I see for growth in the future. Machine learning techniques are being used to solve many diverse problems, and we stand to benefit as we move towards a world of hyper-converged data, channels, content, and context -- having the right conversation at the right time with the right person in the right way. For us marketers, ML is about finding nuggets of "predictive" knowledge in the waves of structured and unstructured data.

Five years from now, company leaders will be looking back and wishing they developed an artificial intelligence strategy sooner, according to one of the veterans of the field. Andrew Ng, the cofounder of Coursera and the former machine learning chief at Chinese tech powerhouse Baidu, said that he thinks Fortune 500 businesses will find the rise of AI similar to the rise of the internet. Some top CEOs bemoan how their businesses were late to the party when it came to competing on the internet, and Ng said that the same thing will be true when it comes to AI. In his view, businesses are best off hiring a leader with deep knowledge of the field who can help build up an organization's knowledge and capabilities in a centralized way. That chief AI officer, as he described it, would be charged with helping to bring expertise in the field to the rest of the a company.

Editor's note: This tutorial series was started in September of 2014, with the 8 installments coming over the course of 2 years. I only mention this to put John's first paragraph into context, and to assure readers that this informative series of tutorials, including all of its code, is as relevant and up-to-date today as it was at the time it was written. This is great material, both for anyone taking Andrew Ng's MOOC and as a standalone resource. One of the pivotal moments in my professional development this year came when I discovered Coursera. I'd heard of the "MOOC" phenomenon but had not had the time to dive in and take a class.

Last week, Elon Musk dashed off 125 characters announcing a remarkably ambitious plan to send Amtrak to an early grave. "Just received verbal govt approval for The Boring Company to build an underground NY-Phil-Balt-DC Hyperloop. NY-DC in 29 mins," he proclaimed in a tweet. Sign up to get Backchannel's weekly newsletter. Yet something about this particular moonshot seemed off.

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments. New posts will be published here first, and previous posts are archived on the Udacity blog.

Conventionally, the questions on a test are assumed to be kept secret from test takers until the test. However, for tests that are taken on a large scale, particularly asynchronously, this is very hard to achieve. For example, TOEFL iBT and driver's license test questions are easily found online. This also appears likely to become an issue for Massive Open Online Courses (MOOCs, as offered for example by Coursera, Udacity, and edX). Specifically, the test result may not reflect the true ability of a test taker if questions are leaked beforehand. In this paper, we take the loss of confidentiality as a fact. Even so, not all hope is lost as the test taker can memorize only a limited set of questions' answers, and the tester can randomize which questions to let appear on the test. We model this as a Stackelberg game, where the tester commits to a mixed strategy and the follower responds. Informally, the goal of the tester is to best reveal the true ability of a test taker, while the test taker tries to maximize the test result (pass probability or score). We provide an exponential-size linear program formulation that computes the optimal test strategy, prove several NP-hardness results on computing optimal test strategies in general, and give efficient algorithms for special cases (scored tests and single-question tests). Experiments are also provided for those proposed algorithms to show their scalability and the increase of the tester's utility relative to that of the uniform-at-random strategy. The increase is quite significant when questions have some correlation---for example, when a test taker who can solve a harder question can always solve easier questions.

Robot Academy is an online platform that provides free-to-use undergraduate-level learning resources for robotics and robotic vision. The content was developed for two 6-week Massively Open Online Courses (MOOCs) that Corke taught in 2015 and 2016. This content is now available as individual lessons (over 200 videos, each less than 10 minutes long) or in masterclasses (collections of videos, around 1 hour in duration, previously a MOOC lecture). Unlike a MOOC, all lessons are available all the time. While the content is typically designed for undergraduate-level students, around 20% of the lessons require no more than general knowledge.

On 1st of August 2017, we will start a free online training program for Machine Learning, called Machine Learning Army Camp. The goal for me is to study and present 20 top books in Machine Learning, in 6 months, and to share my process with the community. We will integrate the knowledge from 20 books into a single big knowledge network, and you will get to see how it will grow over time. I show you here a network (click to see image) that I have built before for foundations in science in general, after having read around 80 books (philosophy of science, logic, math, computer science, machine learning, etc.). In ML Army Camp we will build one big network specifically for Machine Learning.

The future of work looks full of promise. Combining human brainpower with artificial intelligence, virtual reality and automatization will revolutionize how we work. "The future of work looks full of promise." Already, robotic enhancement is helping humans exceed their natural capabilities. AI is opening the door to real-time, personalized intelligent services, cutting waste and maximizing results.