One of the key information pieces in improving energy efficiency of buildings is the appliance level breakdown of energy consumption. Energy disaggregation is the process of obtaining this breakdown from a building level aggregate data using computational techniques. Most of the current research focuses on residential buildings, obtaining this information from a single smart meter and often relying on high frequency data. This work is directed at commercial buildings equipped with building management and automation systems providing low frequency operational and contextual data. This paper presents a machine learning method to disaggregate energy consumption of the building using this operational data as input features. Experimental results on two publicly available datasets demonstrate the effectiveness of the approach, which surpasses existing methods. For all but one appliance of House 2 of the publicly available REDD dataset, improvements in normalized error in assigned power range between 20% (Lighting) and 220% (Stove). For another dataset from an educational facility in Singapore, disaggregation accuracy of 92% is reported for the facility's cooling system.

Establishing a safety standard for autonomous vehicles operating in open and dynamic environment is a challenge. As collisions are inevitable in over-constrained situations, we focus on deciding the liability for a hazard. Our insight is that hazards caused by malfunctions of autonomous vehicles result from loss of functional integrity. Design defects may leave it unnoticed, or the real-world may make integritypreserving motion infeasible. Guarantee of functional integrity in an observable way at run-time is indispensable for revealing defects by using formal root-cause analysis, and for supporting safety claims by dismissing unreasonable doubts about design defects. From a practitical standpoint, we attempt to formalize a verification problem that consists of a novel criterion for determining liability for hazard, a safety claim comprised of confirmed observable states, and assumptions underlying the safety claim. We propose a run-time scheme of monitoring events that may lead to violations of the assumptions and a precursor to root-causes leading to loss of functional integrity and consequent hazards. We formulate a means of preemptively detecting unsafe motions liable to be hazardous as satisfiability problem within the framework of an adversarial motion planning subject to assumptions on maneuverability of movers. A numerical study shows that the run-time scheme using non-linear programming (NLP) encoding is viable in a real-world setting.

We propose an alternative to the Turing test that removes the inherent asymmetry between humans and machines in Turing’s original imitation game. In this new test, both humans and machines judge each other. We argue that this makes the test more robust against simple deceptions. We also propose a small number of refinements to improve further the test. These refinements could be applied also to Turing’s original imitation game.

Recently a large attention has been devoted to the ethical issues arising around the design and the implementation of artificial agents. This is due to the fact that humans and machines more and more often need to collaborate to decide on actions to take or decisions to make. Such decisions should be not only correct and optimal from the point of view of the overall goal to be reached, but should also agree to some form of moral values which are aligned to the human ones. Examples of such scenarios can be seen in autonomous vehicles, medical diagnosis support systems, and many other domains, where humans and artificial intelligent systems cooperate. One of the main issues arising in this context regards ways to model and reason with moral values. In this paper we discuss the possible use of AI compact preference models as a promising approach to model, reason, and embed moral values in decision support systems.

Traditionally, the needs of vulnerable populations have been addressed by a plethora of public and private agencies that rely on donations of money, goods and services which they distribute based on their perception of what is needed and where. This approach, however, lacks a comprehensive understanding of the demand side as well as the ability to coordinate between various suppliers of goods and services, identify latent supply and predict future demand. To help address these issues, we have developed a knowledge-based platform that harnesses advances in several AI fields for efficient and effective provisioning of goods and services.

This week, more than 2,000 Google employees walked out of work to protest President Trump's immigration ban. Far from disciplining them for leaving their desks, CEO Sundar Pichai and co-founder Sergey Brin treated workers to impassioned speeches of support. "Proud, moved, and touched to be at a company that boldly stands for its people," Googler Sam Tse tweeted. While Pichai and Brin were no doubt speaking from personal conviction--Brin's family fled the former Soviet Union when he was a boy--they also had little choice but to back their employees. Trump's directive cut to the heart of Silicon Valley's treasured values of globalism and openness, values widely embraced by the workers themselves.

I consider connected insurance the most relevant insurtech trend: it is the more mature as of today and with the larger TAM. Connected insurance solutions are all the insurance approaches based on sensors for collecting data on the state of an insured risk and telematics for the remote transmission and the management of the data collected. Within the connected insurance landscape, the auto insurance use case is definitely the more mature. It is the first insurtech trend came to age: on one hand, telematics account for more than 17% of the auto insurance portfolio in Italy, on the other hand Metromile will be the first insurance newcomer to acquire an incumbent. The Connected Insurance Observatory will start in the first quarter of 2017 its North American operations.

The sixth and final chapter in the "Resident Evil" franchise has just come out in theatres, and you may as well start there. The films are best enjoyed out of sequence, with no prior knowledge of the plot. Who is this woman with the pair of shotguns and the tight pants? What's this undead beast that wants to eat her face? When did Washington, D.C., get turned to rubble?

The radiation level in the containment vessel of reactor 2 at the crippled Fukushima No. 1 power plant has reached a maximum of 530 sieverts per hour, the highest since the triple core meltdown in March 2011, Tokyo Electric Power Co. Holdings Inc. said. Tepco said on Thursday that the blazing radiation reading was taken near the entrance to the space just below the pressure vessel, which contains the reactor core. The high figure indicates that some of the melted fuel that escaped the pressure vessel is nearby. At 530 sieverts, a person could die from even brief exposure, highlighting the difficulties ahead as the government and Tepco grope their way toward dismantling all three reactors crippled by the March 2011 disaster. Tepco also announced that, based on its analysis of images taken by a remote-controlled camera, that there is a 2-meter hole in the metal grating under the pressure vessel in the reactor's primary containment vessel.

February 3, 2017 --The dark knight could be getting a gear upgrade, thanks to a three-person robotics team. Anyone who's seen bats wheeling and swooping at dusk as they hunt for insects knows their unparalleled maneuverability. This fact hasn't escaped the notice of roboticists, who frequently take inspiration from natural sources, but bat-wing complexity has proved challenging to mimic. On Wednesday, a team from the University of Illinois at Urbana-Champaign and the California Institute of Technology published a paper outlining the construction of one of the most bat-like flying robots yet. Bats put birds to shame in terms of agility and efficiency, say the researchers.