Without abundant data captured from diverse data streams by means of products/services, it can be difficult for companies to create a conducive environment for data scientists to train their machine learning algorithms. While giants like Google and Amazon don't face issues when it comes to gathering data, other companies often have limited access to the datasets they need. Acquiring data is often an expensive endeavor which most companies cannot afford. This is why companies and researchers are now relying on synthetic data to train their algorithms. Synthetic data is lab-generated, artificially manufactured information which is neither obtained by direct measurement nor captured through any means.

Smart security systems that can lock the doors, detect intruders and manage camera feeds while homeowners are away have the potential to reduce insurance premiums and improve individual safety. This value was highlighted in a World Bank Report that pointed to how AI represents'an enormous opportunity to design, build and operate the homes of tomorrow in intelligent ways'. These intelligent ways defined as reducing carbon emissions, lowering energy consumption, cutting costs, and improving health and safety. An example of this technology lies in Wallpad, a COMMAX smart home device that uses Fluent.ai's The device can control lights, thermostats, alarm systems, digital locks, cameras and, quite possibly, anything digital enough to connect to it efficiently.

A Japanese government-funded project to develop the world's first predictive maintenance system for industrial plants will employ artificial intelligence and advanced technology to store data in a secure and user-centric way. The IOTA Foundation, a German-based nonprofit organization behind the technology, said it has been chosen as a partner in the project funded by Japan's New Energy and Industrial Technology Development Organization, a national research and development agency operating under the Ministry of Economy, Trade and Industry. The project, which seeks to strengthen the durability of critical infrastructure, will optimize facility management systems deployed in power, industrial, petrochemicals and oil refining plants throughout Japan by digitizing maintenance data and using artificial intelligence to predict when checkups are needed, according to the foundation. Data for plants across Japan is currently stored manually, causing issues with integrity and sharing capability, it said. The project aims to shift data to a decentralized database using IOTA's distributed ledger service called the Tangle, while AI systems are expected to replace engineers amid Japan's shrinking labor force.

It was at the 21st Conference of Parties (COP21), when history was made as nearly the entire world signed the landmark Paris Climate Agreement to counter climate change. Since that 2015 agreement, COP21 has been the most significant business-focused event driving sustainable development and advancing the "green economy." Renewable energy leader Kaiserwetter Energy Asset Management of Germany was driven by the COP21 agreement and has since revolutionized investment in zero-emission energy. They accomplished this by utilizing SAP technology to create an IoT Platform, capable of early failure detection within wind turbines, inter alia. Established in 2012, Kaiserwetter serves investors, financing banks, and governments within the renewable energy field – offering specialized data analytics as part of their investment process throughout the entire investment lifecycle.

According to the goals of Europe's green deal missions, the continent strives for becoming carbon neutral by 2050. Since buildings are a major contributor to the overall consumption of energy, improving their energy efficiency can be a key to a more sustainable and greener Europe. On the way towards zero-emission buildings, several challenges have to be met: In modern energy systems, several energy sources have to be orchestrated to maintain a high security of supply, to guarantee a healthy environment for the building users, and both by using a minimum of conventional energy. Further, the modern building also hosts the electric filling station for one or several electric vehicles, requiring a significant amount of electrical power. Since the components of the building energy systems are integrating more and more sensors and embedded systems, buildings are becoming networked cyber-physical energy systems -- especially larger objects like airports, shopping malls or office buildings.

Time series modelling is essential for solving tasks such as predictive maintenance, quality control and optimisation. Deep learning is widely used for solving such problems. When managing complex manufacturing process with neural networks, engineers need to know why machine learning model made specific decision and what are possible outcomes of following model recommendation. In this paper we develop framework for capturing and explaining temporal dependencies in time series data using deep neural networks and test it on various synthetic and real world datasets.

Since the HAL9000 and Star Trek's M-5 Multitronic, the power and capabilities of AI have always been oversold by both Hollywood and Silicon Valley. Although we're still waiting on machines that can carry on an intelligent conversation, AI has been creeping into many objects in our everyday lives behind the scenes, making them more useful and proactive. People are most familiar with the intelligent assistants built into devices like the Amazon Echo, Google Nest Hub and Apple HomePod, but as I wrote more than three years ago, these rely on cloud backend services for most of their smarts, using local hardware primarily to recognize their wake word and listen for follow-up questions. The combination allows surprisingly sophisticated deep and machine learning models to run on embedded systems. Until recently, shoehorning AI software into a battery-powered device has required data scientists skilled in working with the constraints of an embedded SoC, but recent advances in AI development and automation frameworks, categorically termed TinyML, greatly expands the realm of smart devices.

Solar power stations in space that beam'emission-free electricity' down to Earth could soon be a reality thanks to a UK government funded project. Above the Earth there are no clouds and no day or night that could obstruct the sun's ray – making a space solar station a constant zero carbon power source. The UK government commissioned new research into the concept of space-based solar power (SBSP) stations as a way to meet the Earth's growing energy needs. The idea is that the stations would capture the Sun's energy that never makes it to Earth and use laser beams to safely send the energy back to Earth. It's an idea first conjured by science-fiction writer Isaac Asimov in 1941 in his science fiction short story Reason where it was revealed a station a mile across was used as an'energy converter' to gather sunlight and beam it across the solar system.

At least a quarter of the warming that the Earth is experiencing today is due to anthropogenic methane emissions. There are multiple satellites in orbit and planned for launch in the next few years which can detect and quantify these emissions; however, to attribute methane emissions to their sources on the ground, a comprehensive database of the locations and characteristics of emission sources worldwide is essential. In this work, we develop deep learning algorithms that leverage freely available high-resolution aerial imagery to automatically detect oil and gas infrastructure, one of the largest contributors to global methane emissions. We use the best algorithm, which we call OGNet, together with expert review to identify the locations of oil refineries and petroleum terminals in the U.S. We show that OGNet detects many facilities which are not present in four standard public datasets of oil and gas infrastructure. All detected facilities are associated with characteristics known to contribute to methane emissions, including the infrastructure type and the number of storage tanks.

It's no secret that Los Angeles is notorious for its traffic jams, typically ranking first in studies of the nation's traffic hot spots. Estimates suggest that Angelinos spend an extra 120 hours a year stuck in them. While a nightmare for drivers, the L.A. transportation system does have its advantages if you're devising a new system to quickly predict and potentially redirect that traffic. Researchers from across the U.S. Department of Energy's (DOE) Argonne National Laboratory set out to do just that under the umbrella of a larger project on the design and planning of mobility systems led by collaborators at DOE's Lawrence Berkeley National Laboratory (LBNL). Using an artificial intelligence (AI) technique called machine learning, the team leveraged Argonne's supercomputers to digest traffic patterns from nearly a year's worth of data taken from 11,160 sensors along the large California highway system.