With its complex rules, fine print and lengthy processes, it's little wonder that the $1.2tn insurance industry has a poor reputation for trust and customer service. In a recent global survey from accounting firm EY, consumers ranked insurance below banks, car manufacturers, online shopping sites and supermarkets for trustworthiness. A newcomer to the field, New York City-based Lemonade hopes to reverse that reputation by using technology and behavioral science to create a faster and more transparent service. The company is working with Dan Ariely, a professor of psychology and behavioral economics at Duke University, to take antagonism out of its relationship with customers. Lemonade set out to create algorithms that make it easy and quick to sign up and approve claims – in minutes rather than days.

The BBC One nature show Spy in the Wild wanted to get up close and personal with some crocodiles and monitor lizards. Instead of setting up hidden cameras, though, its producers got in touch with the École Polytechnique Fédérale de Lausanne's famous robotics division. Apparently, they saw Pleurobot, the robotic life-like salamander EPFL made, and wanted machines that can blend in with real reptiles. The team ended up building two remote-controlled robots representing the two species with cameras for eyes. They look like the real deal and move, well, almost like them, as well.

Disruptive technologies are now dictating our future, as new innovations increasingly blur the lines between physical, digital and biological realms. Robots are already in our operating rooms and fast-food restaurants; we can now use 3D imaging and stem-cell extraction to grow human bones from a patient's own cells; and 3D printing is creating a circular economy in which we can use and then reuse raw materials. This tsunami of technological innovation will continue to change profoundly how we live and work, and how our societies operate. In what is now called the fourth Industrial Revolution, technologies that are coming of age--including robotics, nanotechnology, virtual reality, 3D printing, the Internet of Things, artificial intelligence, and advanced biology--will converge. And as these technologies continue to be developed and widely adopted, they will bring about radical shifts in all disciplines, industries and economies, and in the way that individuals, companies and societies produce, distribute, consume and dispose of goods and services.

We alluded to the possibility of Deep Learning and IoT previously where we said that Deep learning algorithms play an important role in IoT analytics because Machine data is sparse and / or has a temporal element to it. Devices may behave differently at different conditions. Hence, capturing all scenarios for data pre-processing/training stage of an algorithm is difficult. Deep learning algorithms can help to mitigate these risks by enabling algorithms learn on their own. This concept of machines learning on their own can be extended to machines teaching other machines.

Scientists have built artificial cells that are so life-like, they've tricked natural cells into thinking they're communicating with one of their own. This twist on the classic Turing test means that not only can our robots fool humans into thinking they're one of us - scientists can now make artificial cells that act so real, living organisms can't tell the difference. "We have been interested in the divide between living and nonliving chemical systems for quite some time now, but it was never really clear where this divide fell," one of the team, Sheref S. Mansy from the University of Trento, Italy, told ResearchGate. "[I]t is absolutely possible to make artificial cells that can chemically communicate with bacteria." Proposed more than 60 years ago by British computer scientist Alan Turing, the Turing test is designed to evaluate the intelligence of a machine by asking one simple question - can it trick a human into thinking they're having a conversation with another human?

A soft robotic sleeve placed around the heart in a pig model of acute heart failure. The actuators embedded in the sleeve support heart function by mimicking the outer heart muscles that induce the heart to beat. An international team of scientists has developed a soft robotic sleeve that can be implanted on the external surface of the heart to restore blood circulation in pigs (and possibly humans in the future) whose hearts have stopped beating. The device is a silicon-based system with two layers of actuators: one that squeezes circumferentially and one that squeezes diagonally, both designed to mimic the movement of healthy hearts when they beat. Heart failure affects 41 million people worldwide.

The Industrial Revolution was when humans first overcame the limitations of muscle power. Often referred to also as the First Machine Age, humans during this period were largely complements to the machines. The Second Machine Age, which we are into currently, is mainly about complementing our mental faculties many times, using digital technologies. It is not too clear though whether humans will complement machines during this era or will be replaced altogether. Examples of both can be seen.

Humanoid robots, with cultural awareness and a good bedside manner, could help solve the crisis over care for the elderly, academics say. An international team is working on a £2m project to develop versatile robots to help look after older people in care homes or sheltered accommodation. The robots will offer support with everyday tasks, like taking tablets, as well as offering companionship. Academics say they could alleviate pressures on care homes and hospitals. Researchers from Middlesex University and the University of Bedfordshire will assist in building personal social robots, known as Pepper Robots, which can be pre-programmed to suit the person they are helping. It is hoped culturally sensitive robots will be developed within three years.

We start with a description of Lacan's work that we then take into our analytics methodology. In a first investigation, a Lacan-motivated template of the Poe story is fitted to the data. A segmentation of the storyline is used in order to map out the diachrony. Based on this, it will be shown how synchronous aspects, potentially related to Lacanian registers, can be sought. This demonstrates the effectiveness of an approach based on a model template of the storyline narrative. In a second and more comprehensive investigation, we develop an approach for revealing, that is, uncovering, Lacanian register relationships. Objectives of this work include the wide and general application of our methodology. This methodology is strongly based on the "letting the data speak" Correspondence Analysis analytics platform of Jean-Paul Benz\'ecri, that is also the geometric data analysis, both qualitative and quantitative analytics, developed by Pierre Bourdieu.

Embedded Intelligence, German Research Center for Artificial Intelligence, Kaiserslautern, Germany, {vitor.fortes,paul.lukowicz}@dfki.de Abstract Traditional activity recognition systems work on the basis of training, taking a fixed set of sensors into account. In this article, we focus on the question how pattern recognition can leverage new information sources without any, or with minimal user input. Thus, we present an approach for opportunistic activity recognition, where ubiquitous sensors lead to dynamically changing input spaces. Our method is a variation of well-established principles of machine learning, relying on unsupervised clustering to discover structure in data and inferring cluster labels from a small number of labeled dates in a semi-supervised manner. Elaborating the challenges, evaluations of over 3000 sensor combinations from three multiuser experiments are presented in detail and show the potential benefit of our approach. Keywords: Opportunistic Activity Recognition, Unsupervised Learning, Semi-supervised Learning, Classifier Adaptation 1. Introduction Today, state-of-the-art approaches to activity and context recognition typically assume fixed, narrowly defined system configurations dedicated to often also narrowly defined tasks. Such systems can only work when sensors are known in the training phase and they cannot adapt to new sensors in their environment. In turn, sensors are evermore present in our life, although not always available. When moving around, a person may face highly instrumented environments and places with little or no intelligent infrastructure. Concerning on-body sensing, a user may carry a varying collection of sensor enabled devices (mobile phone, watch, headset, etc.) on different, dynamically varying body locations (different pockets, wrist, bag). Thus, in order to realize their full potential, systems need to take advantage of devices that just "happen" to be in the environment, taking into account their current placement and relevance. In our previous work, we investigated how on-body position and orientation of on-body sensors can be inferred [1, 2], how position shifts can be tolerated [3], and how one sensor can replace another [4]. Preprint submitted to Computational Intelligence and Neuroscience March 15, 2018 integration. More precisely, this means to answer the question how can a new sensor's data be integrated in an existing activity recognition system at runtime in order to improve this recognition process. Extending a system that used n sensors to one that uses (n 1) has many challenges. For instance, training data is expensive, and thus we cannot expect the new (n 1) data to be labeled.