In biomedical science, a set of objects or persons can often be described by multiple distinct sets of features obtained from different data sources or modalities (called "multi-view data"). Classical machine learning methods ignore the multi-view structure of such data, limiting model interpretability and performance. The R package `mvs` provides methods that were designed specifically for dealing with multi-view data, based on the multi-view stacking (MVS) framework. MVS is a form of supervised (machine) learning used to train multi-view classification or prediction models. MVS works by training a learning algorithm on each view separately, estimating the predictive power of each view-specific model through cross-validation, and then using another learning algorithm to assign weights to the view-specific models based on their estimated predictions. MVS is a form of ensemble learning, dividing the large multi-view learning problem into smaller sub-problems. Most of these sub-problems can be solved in parallel, making it computationally attractive. Additionally, the number of features of the sub-problems is greatly reduced compared with the full multi-view learning problem. This makes MVS especially useful when the total number of features is larger than the number of observations (i.e., high-dimensional data). MVS can still be applied even if the sub-problems are themselves high-dimensional by adding suitable penalty terms to the learning algorithms. Furthermore, MVS can be used to automatically select the views which are most important for prediction. The R package `mvs` makes fitting MVS models, including such penalty terms, easily and openly accessible. `mvs` allows for the fitting of stacked models with any number of levels, with different penalty terms, different outcome distributions, and provides several options for missing data handling.

AI is getting even more traction lately because of recent innovations that have made headlines, Alexa's unexpected laughing notwithstanding. But AI has been a sound career choice for a while now because of the growing adoption of the technology across industries and the need for trained professionals to do the jobs created by this growth. Pundits predict that AI will create close to 2.3 million jobs by 2020. However, it is also forecasted that this technology will wipe out over 1.7 million jobs, resulting in about half a million new jobs worldwide. Moreover, AI offers many unique and viable career opportunities.

Artificial Intelligence (AI) made headlines recently when people started reporting that Alexa was laughing unexpectedly. Those news reports led to the usual jokes about computers taking over the world, but there's nothing funny about considering AI as a career field. Just the fact that nine out of ten Americans use AI services in one form or another every day proves that this is a viable career option. During a Simplilearn fireside chat, Anand Narayanan, Chief Product Officer at Simplilearn, and Ronald Van Loon, a Big Data expert and Simplilearn advisory board member, discussed the future of AI and machine learning as career fields. They delved into specific types of jobs available and the training required to get them.

Machine learning has joined artificial intelligence (AI) as the hottest technology topics of 2018. We asked our expert influencers to share their thoughts on the state of the industry: where it's going, and how and why companies should be adopting machine learning and AI. Joining us are Ronald van Loon, director of Adversitement; Dr. Manjeet Rege, associate professor of graduate programs in software at the University of St. Thomas; Jennifer Shin, founder, @8PathSolutions; Colin Sumter, Analyst, CrowdMole; Daniel Yarmoluk, Director of IIoT & Data Science, ATEK Access Technologies; and Dr. Craig Brown, CEO, STEM Resource Partners. What is driving the momentum and urgency for companies to adopt machine learning and AI? Dr. Manjeet Rege: It is no more a question of whether you want to adopt an AI strategy, it's a matter of how soon.

As the year winds down, questions tend to arise about what the big trends of the past year have been and what the year to come may hold. With those questions in mind, we asked eight key influencers in the world of big data and analytics -- Chris Penn, vice president of marketing technology at SHIFT Communications; Jim Kaskade, CEO of Janrain; IT consultant Duane Baker; Bill Jensen, CEO of The Jensen Group; William McKnight, president of McKnight Consulting Group; Ronald van Loon, director of Adversitement; Dr. Manjeet Rege, associate professor of graduate programs in software at the University of St. Thomas, and Bob E. Hayes Ph.D., founder of Business over Broadway -- to take a look back at 2017 and look ahead at what's to come in 2018. Here's what they had to say. Chris Penn: Of all the associated technologies with big data -- machine learning, IoT, et cetera -- the one that really stormed the barricades was blockchain. I expected much more focus on IoT this year, and blockchain stole the show.

For a company to shift towards becoming an intelligent company, it needs to have more than just the technology to enable the transformation. There is a need for significant changes in the way employees think about data and how it can be effectively processed and acted on. Following are business domains that van Loon and Monaci believe need to be considered. Design Thinking is one of the ways in which this change can be brought about. Design Thinking is part of a broad methodology that amalgamates elements of imagination, intuition, holistic reasoning, and logic to explore all the probable solutions for a given problem.

Nowadays, companies are besieged by information and the possibilities of new IT solutions. With the rapid advancements in information technology and high speed Internet, companies now have access to huge amounts of information, processing and sharing systems regarding customers, their demographics, and their online behavior across all touchpoints on the buyer's journey. The advantage of access to so much information is not just in the form of increased revenue and developing long-lasting customer relationships. It is also about developing sensitivity to warning signals, which would allow companies to prevent or mitigate disasters. So far, companies have improved their practices with respect to capturing greater amounts of data.

A major part of the strategy should include machine-learning (ML) solutions. The implementation of these solutions could change how these enterprises view customer value and internal operating model today. If you want to stay ahead of the game, then you cannot afford to wait for that to happen. Your digital business needs to move towards automation now while ML technology is developing rapidly. Machine learning algorithms learn from huge amounts of structured and unstructured data, e.g.