We develop a novel family of algorithms for the online learning setting with regret against any data sequence bounded by the empirical Rademacher complexity of that sequence. To develop a general theory of when this type of adaptive regret bound is achievable we establish a connection to the theory of decoupling inequalities for martingales in Banach spaces. When the hypothesis class is a set of linear functions bounded in some norm, such a regret bound is achievable if and only if the norm satisfies certain decoupling inequalities for martingales. Donald Burkholder's celebrated geometric characterization of decoupling inequalities (1984) states that such an inequality holds if and only if there exists a special function called a Burkholder function satisfying certain restricted concavity properties. Our online learning algorithms are efficient in terms of queries to this function. We realize our general theory by giving novel efficient algorithms for classes including lp norms, Schatten p-norms, group norms, and reproducing kernel Hilbert spaces. The empirical Rademacher complexity regret bound implies --- when used in the i.i.d. setting --- a data-dependent complexity bound for excess risk after online-to-batch conversion. To showcase the power of the empirical Rademacher complexity regret bound, we derive improved rates for a supervised learning generalization of the online learning with low rank experts task and for the online matrix prediction task. In addition to obtaining tight data-dependent regret bounds, our algorithms enjoy improved efficiency over previous techniques based on Rademacher complexity, automatically work in the infinite horizon setting, and are scale-free. To obtain such adaptive methods, we introduce novel machinery, and the resulting algorithms are not based on the standard tools of online convex optimization.

This week's top Machine Learning stories, including computational models of drug effectiveness, stem cells, sentiment analysis, and more! Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning!

Educational, training institutions and eLearning content publishers must adapt themselves to the new technological landscape in order to keep their courses and learning materials relevant to today's learners. The development of educational mobile apps provides exciting new ways to develop educational courses that are both effective in reaching educational objectives for teachers and rewarding to the online learner. This presentation will serve as a guide for managers at learning organizations into ways to adapt courses for the multi-screen and multi-device app based environment that today's learners engage in. Mobile devices are outpacing traditional desktop environments when it comes to accessing the web. In fact, 60% of search queries are now done through mobile devices (source: SearchEngineLand).

Developing training programmes for a large group of sales or technical or services personnel is a challenging task as the programme is meant for a diverse group, and has to be engaging and meaningful for the participants. The programmes are mostly delivered at multiple locations, they have to be updated from time to time and at times, also require to be culturally sensitive to remain relevant as well as contemporary. Effective assessment strategy is also important to ensure the programmes meet the stated business objectives. In the digital era, there is a plethora of content available on the internet. A lot of it is free of cost via options such as MOOCs, Course Era, You Tube and others.

The other day, I went to meet someone in downtown Sydney, Australia. On my way, back on the local train, I looked at my mobile to check my emails and found a message asking me whether I would like to meet the person I had just connected with on my LinkedIn network. So, was this some form of artificial intelligence (AI) at play? We now live in a brave new world where AI is the next frontier. We keep hearing about bots, chatbots, teacherbots, digital assistants, machine learning, deep learning and many more such words and often wonder what do they mean.

For centuries, humans have been fretting over "technological unemployment" or the loss of jobs caused by technological change. Never has this sentiment been accentuated more than it is today, at the cusp of the next industrial revolution. With developments in artificial intelligence continuing at a chaotic pace, fears of robots ultimately replacing humans are increasing. TNW Conference won best European Event 2016 for our festival vibe. See what's in store for 2017.

The race is on to attract as much expertise in artificial intelligence as possible at tech companies large and small, and more than a few Silicon Valley giants are looking inward to convert tech talent they already possess into the AI resources they increasingly need. Facebook has its own AI course, which is oversubscribed, according to a new report by Wired, and which is led by one of the leading AI researchers in the world. Facebook's Larry Zitnick, who is a key leader at the social networking company's Artificial Intelligence Research Lab, as well as a Microsoft Research and CMU Robotics alum, teaches a class on deep learning for Facebook employees that draws over-capacity crowds. Zitnick's course sparks strong competition among engineers who already rank among the best in the world, each vying to come to grips with and excel at a field outside of their original purview, but one that few fail to recognize is the hottest in tech. On the other hand, AI and deep learning increasingly touch all aspects of the technology business, so experts with understanding of where the overlap might prove most useful in their own original discipline are also going to be very much in demand. There are external efforts underway to help create more of these polyglot deep learning pros, including at online educational firms like Udacity, but new talent isn't rolling in fast enough from outside sources, traditional and non-traditional alike.

For centuries, humans have been fretting over "technological unemployment" or the loss of jobs caused by technological change. Never has this sentiment been accentuated more than it is today, at the cusp of the next industrial revolution. With developments in artificial intelligence continuing at a chaotic pace, fears of robots ultimately replacing humans are increasing. However, while AI continues to master an increasing number of tasks, we're still decades away from human jobs going extinct. With AI finding its way into more and more domains, the demand for tech talent is growing.

For centuries, humans have been fretting over "technological unemployment" or the loss of jobs caused by technological change. Never has this sentiment been accentuated more than it is today, at the cusp of the next industrial revolution. With developments in artificial intelligence continuing at a chaotic pace, fears of robots ultimately replacing humans are increasing. We're inviting 250 to exhibit at TNW Conference and pitch on stage! However, while AI continues to master an increasing number of tasks, we're still decades away from human jobs going extinct.

For centuries, humans have been fretting over "technological unemployment" or the loss of jobs caused by technological change. Never has this sentiment been accentuated more than it is today, at the cusp of the next industrial revolution. With developments in artificial intelligence continuing at a chaotic pace, fears of robots ultimately replacing humans are increasing. We've teamed up with Product Hunt to offer you the chance to win an all expense paid trip to TNW Conference 2017! However, while AI continues to master an increasing number of tasks, we're still decades away from human jobs going extinct.