Abstraction is a fundamental part when learning behavioral models of systems. Usually the process of abstraction is manually defined by domain experts. This paper presents a method to perform automatic abstraction for network protocols. In particular a weakly supervised clustering algorithm is used to build an abstraction with a small vocabulary size for the widely used TLS protocol. To show the effectiveness of the proposed method we compare the resultant abstract messages to a manually constructed (reference) abstraction. With a small amount of side-information in the form of a few labeled examples this method finds an abstraction that matches the reference abstraction perfectly.

Learning the minimum/maximum mean among a finite set of distributions is a fundamental sub-task in planning, game tree search and reinforcement learning. We formalize this learning task as the problem of sequentially testing how the minimum mean among a finite set of distributions compares to a given threshold. We develop refined non-asymptotic lower bounds, which show that optimality mandates very different sampling behavior for a low vs high true minimum. We show that Thompson Sampling and the intuitive Lower Confidence Bounds policy each nail only one of these cases. We develop a novel approach that we call Murphy Sampling. Even though it entertains exclusively low true minima, we prove that MS is optimal for both possibilities. We then design advanced self-normalized deviation inequalities, fueling more aggressive stopping rules. We complement our theoretical guarantees by experiments showing that MS works best in practice.

From a first-principles perspective, the task of feeding eight billion people boils down to converting energy from the sun into chemical energy in our bodies. Traditionally, solar energy is converted by photosynthesis into carbohydrates in plants (i.e., biomass), which are either eaten by the vegans amongst us, or fed to animals, for those with a carnivorous preference. Today, the process of feeding humanity is extremely inefficient. If we could radically reinvent what we eat, and how we create that food, what might you imagine that "future of food" would look like? The average American meal travels over 1,500 miles from farm to table.

Artificial intelligence (AI) shifts the computing paradigm from rule-based programming to an outcome-based approach. It allows processes to operate at scale, reducing the number of human processing errors, and inventing new ways of solving problems. AlphaGo inspired Go players to try new strategies after experts had been using the same opening moves for 3,000 years. As adoption increases, AI will enable organizations to unlock the "last mile" that traditional automation could not address. But as more enterprises entrust AI to make decisions on their behalf, governance becomes super critical.

Industry 4.0 signifies the promise of a new Industrial Revolution--one that marries advanced production and operations techniques with smart digital technologies to create a digital enterprise that would not only be interconnected and autonomous but could communicate, analyze, and use data to drive further intelligent action back in the physical world. It represents the ways in which smart, connected technology would become embedded within organizations, people, and assets, and is marked by the emergence of capabilities such as robotics, analytics, artificial intelligence and cognitive technologies, nanotechnology, quantum computing, wearables, the Internet of Things, additive manufacturing, and advanced materials. While its roots are in manufacturing, Industry 4.0 is about more than simply production. Smart, connected technologies can transform how parts and products are designed, made, used, and maintained. They can also transform organizations themselves: how they make sense of information and act upon it to achieve operational excellence and continually improve the consumer/partner experience.

In this article, which is Part 2 of a series tracing the concept of artificial intelligence from its inception, we pick up the story with Alan Turing, who is considered by many to be the "father" of computer science. As we shall see, Alan Turing has a credible right to be called not only the father of computer science but one of the earliest pioneers in artificial intelligence (or machine intelligence, as he would have called it). For this reason, I devote considerable attention to his brief but remarkable career, including some biographical details. I repeat my disclaimer that I am not a professional historian. Instead, I hope this series of articles inspires others to further study this fascinating history, as well as providing insight into what "artificial intelligence" actually means.

Can robots really save the NHS? Last week Jeremy Hunt, the secretary of state for health and social care doubled down on ambitious plans for the deployment of artificial intelligence software across the NHS, with a particular focus on cancer detection. The idea that software programmes could spot cancer earlier than trained radiologists may seem outlandish, yet for people working with healthcare start-ups in Britain, Mr Hunt's plan seems not just feasible but perhaps not ambitious enough. Rather than just looking at what technologies the NHS could adopt from the outside to improve care, it should be looking to develop its own services to provide to the world. We all know there is no technological...

Beyond traditional industrial automation and advanced robots, new generations of more capable autonomous systems are appearing in environments ranging from autonomous vehicles on roads to automated check-outs in grocery stores. Much of this progress has been driven by improvements in systems and components, including mechanics, sensors and software. AI has made especially large strides in recent years, as machine-learning algorithms have become more sophisticated and made use of huge increases in computing power and of the exponential growth in data available to train them. Spectacular breakthroughs are making headlines, many involving beyond-human capabilities in computer vision, natural language processing, and complex games such as Go. These technologies are already generating value in various products and services, and companies across sectors use them in an array of processes to personalize product recommendations, find anomalies in production, identify fraudulent transactions, and more.

There's a lot we can learn from the octopus. Scientists working in the field of soft robotics have been devising smart materials, smart skins and artificial muscles which move and work in novel ways – all taking a cue from how octopuses move. And by better understanding octopuses, engineers could soon transform the technologies available to people with disabilities. "Some of our smart skins are based on the skin of the octopus and the cephalopods," says engineer Jonathan Rossiter, head of the soft robotics group at Bristol Robotics Laboratory. They can change colour, change texture and radically morph their shapes.

BSA project @ Speaker twitter handle 6. HIMSS Europe GmbH 6 Change of professionals • We need more technical profiles • We need to reconvert part of the actual professionals • We need to invest in training the actual professionals toward more technical competence 7. @ Speaker twitter handle Thank you!