Few folks are aware of how much electricity their household consumes on a daily basis, according to the U.S. Energy Information Administration's Residential Energy Consumption Survey. Among smart meter owners in 2016, only 8 percent reported knowing that they had access to hourly or daily data, and just 4 percent said they'd viewed that data. That's all the more discouraging when you consider that always-on devices like DVRs and game consoles account for 23 percent of home energy usage -- a total of $40 billion annually nationwide. Boston-area startup Sense, which uses machine learning to provide real-time insights on electrical usage, is on a mission to effect change. It today revealed that energy management solutions firm Landis Gyr has joined Schneider Electric, Energy Impact Partners, Shell Ventures, Prelude Ventures, Capricorn Investment Group, and iRobot in bringing its series B round funding to $20 million.

This content is provided by Leidos and Amazon Web Services. Anyone who has ever dealt with federal cybersecurity has had those two options presented to them. Everyone agrees that when designing a new system, considering cybersecurity at the onset―or "baking it in"―is a good idea. Adding solutions after the fact, or "bolting it on," can help a system respond to new threats, but can also result in a complicated and messy cybersecurity strategy. Is it really just a binary question?

Amazon, Microsoft and Google are the forerunners among tech giants to leverage artificial intelligence to tackle cybersecurity threats and keep hackers at bay, who often pose as a real user to gain crucial data. Speaking to an international news agency, the chief security officers from each of the company unanimously agreed that AI and ML plays a crucial role in protecting their company'' multi-million dollar infrastructure by crunching large pool of data on a daily basis. While acknowledging that it is impossible to stop every intruder, Stephen Schmidt, Amazon CISO, maintained that the new tech remains largely beneficial for companies like that of Amazon which has to ensure online safety of millions of people across the world. Speaking about the ability of AI and ML in identifying hacker he said, "We will see an improved ability to identify threats earlier in the attack cycle and thereby reduce the total amount of damage and more quickly restore systems to a desirable state." Speaking on the large set of data that needs to be processed for monitoring unauthorised activities, Mark Risher, product management director at Google said,"The amount of data we need to look at to make sure whether this is you or an impostor keeps growing at a rate that is too large for humans to write rules one by one."

WASHINGTON - U.S. authorities are in the "advanced" stages of a criminal probe that could result in an indictment of Chinese technology giant Huawei, a report said Wednesday. The Wall Street Journal, citing anonymous sources, said the Justice Department is looking into allegations of theft involving trade secrets from Huawei's U.S. business partners, including a T-Mobile robotic device used to test smartphones. The Justice Department declined to comment on the report and Huawei did not respond to a request for comment. The move would further escalate tensions between the U.S. and China after the arrest last year in Canada of Huawei's chief financial officer Meng Wanzhou, who is the daughter of the company's founder and remains under house arrest, awaiting proceedings. The Meng case has inflamed U.S.-China and Canada-China relations.

Are you curious about the ways in which Artificial Intelligence, machine learning, and the range of cognitive technologies are helping improve Cybersecurity and respond better to emerging threats? Check out this infographic from Cognilytica that outlines some key stats as well as key ways in which AI is improving cybersecurity.

Russian president Vladimir Putin stated - "AI is the future and whoever becomes leader in AI will become the ruler of the world". Chinese president Xi Jinping declared that "China wants to be the world leader in AI by 2030". US White House administration voiced - "America has been the global leader in AI, and the Trump administration will ensure our great nation remains the global leader in AI". Similarly, National strategy for Artificial Intelligence, India published by NITI Aayog indicates its vision as "AI-for-All in India". These statements clearly indicates that the race for the supremacy in the field of Artificial Intelligence had already taken a great momentum and AI has managed to influence even main stream politics and the world leaders in a great way. On the other hand, many experts across the globe are already in a big hurry to proclaim which country is going to be the AI superpower and who is already ahead in the race.

NASA's InSight lander is leaning in for a better listen of Mars' underground tremors. The robotic explorer placed its seismometer on the surface at the end of last month, and is now getting even closer'for a better connection with Mars.' This will help its instruments pick up fainter signals that may otherwise have been missed. NASA's InSight lander is leaning in for a better listen of Mars' underground tremors. The robotic explorer placed its seismometer on the surface at the end of last month, and is now getting even closer'for a better connection with Mars.' Before and after images show its instrument at its lowest position yet Days prior, InSight leveled out its seismometer and adjusted the internal sensors ahead of lowering everything down toward the ground.

Detecting the emergence of abrupt property changes in time series is a challenging problem. Kernel two-sample test has been studied for this task which makes fewer assumptions on the distributions than traditional parametric approaches. However, selecting kernels is nontrivial in practice. Although kernel selection for two-sample test has been studied, the insufficient samples in change point detection problem hinders the success of those developed kernel selection algorithms. In this paper, we propose KL-CPD, a novel kernel learning framework for time series CPD that optimizes a lower bound of test power via an auxiliary generative model. With deep kernel parameterization, KL-CPD endows kernel two-sample test with the data-driven kernel to detect different types of change-points in real-world applications. The proposed approach significantly outperformed other state-of-the-art methods in our comparative evaluation of benchmark datasets and simulation studies. Detecting changes in the temporal evolution of a system (biological, physical, mechanical, etc.) in time series analysis has attracted considerable attention in machine learning and data mining for decades (Basseville et al., 1993; Brodsky & Darkhovsky, 2013). This task, commonly referred to as change-point detection (CPD) or anomaly detection in the literature, aims to predict significant changing points in a temporal sequence of observations.

This paper proposes a new reinforcement learning (RL) algorithm that enhances exploration by amplifying the imitation effect (AIE). This algorithm consists of self-imitation learning and random network distillation algorithms. We argue that these two algorithms complement each other and that combining these two algorithms can amplify the imitation effect for exploration. In addition, by adding an intrinsic penalty reward to the state that the RL agent frequently visits and using replay memory for learning the feature state when using an exploration bonus, the proposed approach leads to deep exploration and deviates from the current converged policy. We verified the exploration performance of the algorithm through experiments in a two-dimensional grid environment. In addition, we applied the algorithm to a simulated environment of unmanned combat aerial vehicle (UCAV) mission execution, and the empirical results show that AIE is very effective for finding the UCAV's shortest flight path to avoid an enemy's missiles.

A high-tech augmented reality ski helmet which includes GPS, a speedometer and the ability video call friends on the slopes is being tested in Austria. Former Israeli Air Force pilot Alon Getz helped design the new cutting-edge technology as part of his start-up company RideOn. He said: 'I'm a software engineer, doing a lot of computer-vision and Artificial Intelligence. I was working in the defence industry leading Augmented Reality projects for the military. 'I'm also a snowboarder who goes snowboarding almost every year in the Alps.