The BEAD sensor device analyzes and learns the daily use cycle, energy consumption, user behavior and occupancy changes in all kinds of buildings, and then provides feedback to its automation systems, connecting them to the real-time operation of the building in order to optimize marketing, operations, and energy efficiency. Energy consumption in buildings currently accounts for over 40% of all energy consumed in Europe and the US. This makes for the largest share of the total energy consumption, ahead of transport and industrial production. Europe alone wastes over EUR 43 billion worth of energy in commercial buildings annually. The reason for this is that traditional automation technologies operate on fixed schedules and standard assumptions of occupancy in commercial and residential buildings. But in reality, only about one-third of these assumptions are true.

Recently I was honoured to be invited by Huawei to the CeBIT 2018 conference in Hannover, to be part of their KOL program (Key Opinion Leaders Program). The KOL program is a program where Huawei gives the opportunity to a select group of domain experts and media people to get an understanding of their solutions and technology, and how it can make a difference on our society. I knew Huawei from the smartphones we see popping up, and an initial contact from about 6 years ago, talking to one of the HR people, asking to come to work for them. At that time, I thought, What do I have to look for in China, working for a telecom operator? Well that was the image I had from Huawei.

Rapidly-exploring Random Tree star (RRT*) has recently gained immense popularity in the motion planning community as it provides a probabilistically complete and asymptotically optimal solution without requiring the complete information of the obstacle space. In spite of all of its advantages, RRT* converges to an optimal solution very slowly. Hence to improve the convergence rate, its bidirectional variants were introduced, the Bi-directional RRT* (B-RRT*) and Intelligent Bi-directional RRT* (IB-RRT*). However, as both variants perform pure exploration, they tend to suffer in highly cluttered environments. In order to overcome these limitations, we introduce a new concept of potentially guided bidirectional trees in our proposed Potentially Guided Intelligent Bi-directional RRT* (PIB-RRT*) and Potentially Guided Bi-directional RRT* (PB-RRT*). The proposed algorithms greatly improve the convergence rate and have a more efficient memory utilization. Theoretical and experimental evaluation of the proposed algorithms have been made and compared to the latest state of the art motion planning algorithms under different challenging environmental conditions and have proven their remarkable improvement in efficiency and convergence rate.

Ready or not, autonomous robots are leaving laboratories to be tested in real-world contexts. With more and more people living in cities, these technologies offer ways to cope with ageing populations and poorly maintained infrastructures, while promoting safer transport, productive manufacturing and secure energy supplies. Urban "living labs" are one way scientists are trying to understand how autonomous robots – or Robotics and Autonomous Systems (RAS), to give them their full title – will affect our everyday lives. Autonomous robots are interconnected, interactive, cognitive and physical tools, which can perceive their environments, reason about events, make or revise plans and control their own actions. These technologies are designed to draw on big data and connect with the Internet of Things, to make our lives easier by increasing accuracy and efficiency.

IBM yesterday announced that several new European clients have selected IBM Watson Internet of Things (IoT) technologies. New contracts signed with Spanish electricity grid operator Red Eléctrica de España, Italian elderly care provider Cooperativa Sole, Dutch telecommunications operator Tele2 and Israeli manufacturer of smart air conditioning Electra Group are examples of IBM's commitment to transforming business and improving operations with the power of Artificial Intelligence (AI)-enabled, IBM Cloud-based Internet of Things (IoT) technologies. Red Eléctrica de España (http://www.ree.es/en), the sole transmission agent and operator of the national electricity system in Spain has selected IBM Watson IoT technologies as part of its Intelligent Asset Management initiative project. The electricity system operator's objective is to plan and optimize the maintenance and replacement activities of their strategic transmission assets (like substations and transmission lines) with the support of the IBM Watson IoT platform. With an IoT analytics solution that is designed specifically to help Energy and Utilities improve asset maintenance and utilization, a team from IBM Global Business Services (GBS) has begun implementation of the platform and is working in unison with REE to develop asset degradation models and design optimization plans specific to the electricity system operator's requirements.

New research has revealed that the utilities industry, which has struggled to innovate for some time, is now ready to adopt Artificial Intelligence (AI) to bring much needed efficiencies to the sector. A recent poll, commissioned by field service management company Oneserve, found that 37 per cent of utilities companies in the UK have plans to integrate AI over the next five years while a quarter (25%) have already integrated the technology into their systems. The UK senior decision makers in the utilities industry cited monetary savings and customer retention as the two main driving factors for AI adoption. The research found that on average utilities companies lose £78,585 each, per year as a result of machine and system downtime. The inability to spot internal technical faults (41%) was given as the main reason for such huge loses followed by using old machinery and legacy systems (27%) and a lack of training which lead to miss-use by staff (20%).

Artificial intelligence is now so smart that silicon brains frequently outthink people. Computers operate self-driving cars, pick friends' faces out of photos on Facebook, and are learning to take on jobs typically entrusted only to human experts. Researchers from the University of Wisconsin–Madison and Oak Ridge National Laboratory have trained computers to quickly and consistently detect and analyze microscopic radiation damage to materials under consideration for nuclear reactors. And the computers bested humans in this arduous task. "Machine learning has great potential to transform the current, human-involved approach of image analysis in microscopy," says Wei Li, who earned his master's degree in materials science and engineering this year from UW–Madison.

Drones have become a mainstream product for businesses and consumers over the past five years, as functionality has expanded and pricing has dropped below $999. Companies like DJI, Yuneec, and Parrot have achieved significant market growth in the space, with affordable, relatively easy-to-use drones capable of capturing high-resolution (even 4K-class) content that would have previously required highly specialized equipment. Today I wanted to take a look at Redwood City-based startup Skydio's new R1 drone--a product that I believe has the potential to disrupt the crowded $5 billion drone market. Positioned essentially as a "self-driving camera," the R1 is embedded with 13 cameras. While this makes the R1 the definitive "follow me" drone, is it worth forking over the $2,499 asking price?

In this special guest feature, Marc Alacqua, CEO and founding partner of Signafire, discusses a useful approach to data – known as data fusion – which is essentially alchemy-squared, turning not just one but multiple raw materials in to something greater than the sum of their parts. It goes beyond older methods of big data analysis, like data integration, in which large data sets are simply thrown together in one environment. Marc is a decorated combat veteran of the U.S. Army Special Operations Forces. For his service during Operation Iraqi Freedom, he was cited for "exceptionally conspicuous gallantry" and awarded two Bronze Star Medals and the Army Commendation Medal for Valor. A 20-year veteran and Lieutenant Colonel, Marc has extensive command experience in both combat and peace time, having commanded airborne and light infantry as well as special operations units.

Customer churn is a major headache for most companies and threatens to put the brakes on the red-hot growth of the pay-as-you-go (PAYGo) solar sector. With over 1 million units sold in the last 5 years and over 50,000 units installed each month, the PAYGo model makes solar affordable for end-users and provides sufficient margin for providers to scale last-mile distribution. However, for the model to succeed PAYGo operators must retain customers and build a base of loyal and engaged customers. Our project with Zola Electric (formerly Off Grid Electric) demonstrates that machine learning can help them do so. PAYGo operators make money from installments and/or fees as end-consumers pay off solar assets over 1 to 3 years.