Germany is gearing up to lay down the ethical foundations for self-driving cars, banning A.I. from making decisions that could harm one group of people over another. The country's transport minister has outlined the basis for future legal guidelines for driverless vehicles, rules that echo Isaac Asimov's three laws of robotics, which manufacturers will be expected to work towards ahead of formal legalization. Dobrindt has created an ethics commission to work out the specifics in terms of regulation, but the above rules will serve as a starting point for future laws. The third rule may seem to suggest that the manufacturer cannot depend on the driver stepping in during an emergency, but Dobrindt indicated that drivers will be expected to have a basic awareness at all times. In practice, this will likely mean sleeping at the wheel is forbidden, but reading a book is allowed.

This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.

We have covered Manopt before several times ( tag Manopt). It now comes to Python. From the paper: Further successful applications of optimization on manifolds include matrix completion tasks (Vandereycken, 2013; Boumal and Absil, 2015), robust PCA (Podosinnikova et al., 2014), dimension reduction for independent component analysis (ICA) (Theis et al., 2009), kernel ICA (Shen et al., 2007) and similarity learning (Shalit et al., 2012). Many more applications to machine learning and other elds exist. While a full survey on the usefulness of these methods is well beyond the scope of this manuscript, we highlight that at the time of writing, a search for the term \manifold optimization" on the IEEE Xplore Digital Library lists 1065 results; the Manopt toolbox itself is referenced in 90 papers indexed by Google Scholar.

Songbirds provide a model system that neuroscientists use to understand how the brain learns and controls speech and similar skills. Much like infants learning to speak from their parents, songbirds learn their song from a tutor and practice it millions of times before reaching maturity. Also like humans, songbirds have evolved special brain regions for learning and producing their vocalizations. These newly-evolved brain regions in songbirds, known as the song system, are found within broader brain areas shared by birds and humans across evolution. So by studying how the song system works, we can learn about our own brains.

Cyber criminals are quite adept at stealing data, money and privacy. No network is off limits as they exploit any point of weakness they find in businesses, homes, institutions, automobiles, utility networks and other portals. And their tactics evolve faster than security professionals can manage them. The question is, can we leverage technologies such as artificial intelligence (AI) and cognitive computing to win the war against cyber criminals? Cybersecurity experts shared their thoughts on this topic.

The Draper Satellite Image Chronology Competition (Chronos) ran on Kaggle from April to June 2016. This competition, which was novel in a number of ways, challenged Kagglers to put order to time and space. That is, given a dataset of satellite images taken over the span of five days, the 424 brave competitors were required to determine their correct order. The challenge, which Draper hosted in order to contribute to a deeper understanding of how to process and analyze images, was a first for Kaggle--it allowed hand annotation as long as processes used were replicable. While the winners of the competition used a mixture of machine learning, human intuition, and brute force, Damien Soukhavong (Laurae), a Competitions and Discussion Expert on Kaggle, explains in this interview how factors like the limited number of training samples which deterred others from using pure machine learning methods appealed to him.

The focus of this talk is scalable machine learning using the H2O R and Python packages. H2O is an open source distributed machine learning platform designed for big data, with the added benefit that it's easy to use on a laptop (in addition to a multi-node Hadoop or Spark cluster). The core machine learning algorithms of H2O are implemented in high-performance Java; however, fully featured APIs are available in R, Python, Scala, REST/JSON and also through a web interface. Since H2O's algorithm implementations are distributed, this allows the software to scale to very large datasets that may not fit into RAM on a single machine. H2O currently features distributed implementations of generalized linear models, gradient boosting machines, random forest, deep neural nets, dimensionality reduction methods (PCA, GLRM), clustering algorithms (K-means), and anomaly detection methods, among others.

We are on a mission to build smart AI to tackle complex social and collaboration problems. Our team of Aalto researchers consists of world-class experts in network optimization and social network analysis. Building upon years of research and expertise, our ultimate goal is to develop technology that helps individuals cope with complex communication and collaboration problems that arise in social settings - in business environments as well as in everyday life. The solutions we envision take the form of chat-bots, i.e., electronic personal assistants that integrate gracefully into modern collaboration platforms (such as Slack, Skype for Business, or Facebook Messenger).

Software agents are nothing new, but we're now entering a new wave of innovation relating to bots and artificial intelligence that has the potential to impact our lives at home and at work, and change how businesses operate. New applications for'intelligent' automation are springing up everywhere, with the potential to affect all of the processes and engagements that make organisations work… but where does it make sense to prioritise your efforts? From work automation to augmentation, what approaches are right for your use cases today? On July 21 Neil Ward-Dutton, Angela Ashenden and Craig Wentworth of MWD Advisors presented a free webinar that gave a hype-free overview of this fast-moving space. If you've got a free membership account you can watch the replay below.