In this article we present an unsupervised low-frequency method aimed at detecting and disaggregating the power used by Cumulative Water Heaters (CWH) in residential homes. Our model circumvents the inherent difficulty of unsupervised signal disaggregation by using both the shape of a power spike and its time of occurrence to identify the contribution of CWH reliably. Indeed, many CHWs in France are configured to turn on automatically during off-peak hours only, and we are able to use this domain knowledge to aid peak identification despite the low sampling frequency. In order to test our model, we equipped a home with sensors to record the ground-truth consumption of a water heater. We then apply the model to a larger dataset of energy consumption of Hello Watt users consisting of one month of consumption data for 5k homes at 30-minute resolution. In this dataset we successfully identified CWHs in the majority of cases where consumers declared using them. The remaining part is likely due to possible misconfiguration of CWHs, since triggering them during off-peak hours requires specific wiring in the electrical panel of the house. Our model, despite its simplicity, offers promising applications: detection of mis-configured CWHs on off-peak contracts and slow performance degradation.

Non-degradable plastic waste stays for decades on land and in water, jeopardizing our environment; yet our modern lifestyle and current technologies are impossible to sustain without plastics. Bio-synthesized and biodegradable alternatives such as the polymer family of polyhydroxyalkanoates (PHAs) have the potential to replace large portions of the world's plastic supply with cradle-to-cradle materials, but their chemical complexity and diversity limit traditional resource-intensive experimentation. In this work, we develop multitask deep neural network property predictors using available experimental data for a diverse set of nearly 23000 homo- and copolymer chemistries. Using the predictors, we identify 14 PHA-based bioplastics from a search space of almost 1.4 million candidates which could serve as potential replacements for seven petroleum-based commodity plastics that account for 75% of the world's yearly plastic production. We discuss possible synthesis routes for these identified promising materials. The developed multitask polymer property predictors are made available as a part of the Polymer Genome project at https://PolymerGenome.org.

Building and construction sectors are major contributors to both waste and emissions globally, and achieving growth sustainably is becoming more and more important for companies around the world. As projects are increasingly complex and expectations from different stakeholders higher, achieving ambitious sustainability goals is challenging without the use of data and modern technology. At the Data Innovation Summit 2021, Janne Liuttu, Chief Data Scientist at Ramboll will be sharing how AI is enabling Ramboll to build sustainable cities and society where people and nature flourish. In our discussion, he walks us through AI's role in reducing waste and carbon emissions, concrete solutions for creating sustainable cities and societies at Ramboll and the challenges of applying AI in the building and construction sectors. Hyperight: Hi Janne, it's our pleasure to welcome you as a speaker to the Data Innovation Summit 2021.

In Texas, Texas A&M AgriLife Research scientists are using artificial intelligence to set a new world record for producing algae as a reliable, economic source for biofuel that can be used as an alternative fuel source for jet aircraft and other transportation needs. Joshua Yuan, AgriLife Research scientist, professor and chair of Synthetic Biology and Renewable Products in the Texas A&M College of Agriculture and Life Sciences Department of Plant Pathology and Microbiology, is leading the research project. "The commercialization of algal biofuel has been hindered by the relatively low yield and high harvesting cost," Yuan said. "The limited light penetration and poor cultivation dynamics both contributed to the low yield." Overcoming these challenges could enable viable algal biofuels to reduce carbon emissions, mitigate climate change, alleviate petroleum dependency and transform the bioeconomy, Yuan said.

While the Fukushima event led to a particularly strong change in energy policies in Germany, resulting in the so-called Energiewende, or energy transition, the trend toward renewables is visible worldwide. Here, we outline how major challenges of the energy transition have led to a strong need for essential contributions from the computer science community to maintain stability and security of supply, particularly for the electric power grid. As a result, the new discipline of Energy Informatics has emerged which is addressing this highly interdisciplinary and dynamic field of research and development. In tomorrow's energy system, electric power will be provided mainly by photo-voltaic modules on rooftops and in larger field installations, and by wind power plants, onshore as well as offshore. Being weather-dependent, this energy supply is inherently volatile and only partially controllable.

Energy giant Saudi Aramco says its 2021 net profit soared by more than 120 percent due to higher crude oil prices, as global economic growth recovered from a pandemic induced downturn. The announcement came on Sunday hours after Yemen's Houthi rebels – against whom Saudi Arabia leads a military coalition – targeted several locations, including Aramco facilities, in cross-border armed drone attacks. Aramco, Saudi Arabia's cash cow, did not say if the attacks caused any damage. "Aramco's net income increased by 124 percent to $110bn in 2021, compared to $49bn in 2020," the company said in a statement. Aramco achieved a net income of $88.2bn in 2019 before the coronavirus pandemic hit global markets, resulting in huge losses for the oil and aviation sectors, among others.

Recent progress in Graph Neural Networks (GNNs) for modeling atomic simulations has the potential to revolutionize catalyst discovery, which is a key step in making progress towards the energy breakthroughs needed to combat climate change. However, the GNNs that have proven most effective for this task are memory intensive as they model higher-order interactions in the graphs such as those between triplets or quadruplets of atoms, making it challenging to scale these models. In this paper, we introduce Graph Parallelism, a method to distribute input graphs across multiple GPUs, enabling us to train very large GNNs with hundreds of millions or billions of parameters. We empirically evaluate our method by scaling up the number of parameters of the recently proposed DimeNet++ and GemNet models by over an order of magnitude. On the large-scale Open Catalyst 2020 (OC20) dataset, these graph-parallelized models lead to relative improvements of 1) 15% on the force MAE metric for the S2EF task and 2) 21% on the AFbT metric for the IS2RS task, establishing new state-of-the-art results.

Artificial intelligence (AI) systems today are already transforming industries and becoming an indispensable part of our daily lives. Such systems, which leverage machines to process and analyse large amounts of data, have vastly changed how humans work and play, and are being used today in many sectors, from banking to energy to agriculture. But AI systems can be energy-intensive, and there is a pressing need for those working in the field of AI to address the potentially large environmental impacts. This is especially as demand for data and intelligent devices continues to proliferate. Singapore has committed itself to environmental sustainability, underlined by its ratification of the Paris Agreement and recent plans to reach net-zero by or around mid-century.

"We call it single bottom-line sustainability, where I look at the single bottom line of all those elements, and I start attaching sustainability to it," Glickman says. "And I start looking at changes of value and then I can build a business case for change." As companies set sustainability goals--to be carbon neutral by 2050, for example--they're tackling complex challenges: regulations change, supply chains are complicated, especially during the current pandemic, and integrating new technologies into legacy systems is almost always a hurdle, technologically and culturally. Glickman suggests an incremental approach--he calls it micro change, embracing the fact that sustainability isn't a one-and-done paradigm shift. "These are things that can be done in a six-week period, eight-week period, that have tangible proof of concepts that can be measured, that can be done at different levels." Looking at current infrastructure investments, particularly in North America and Europe, as well as the increasing interest of stakeholders, the sustainability bar is expected to rise. "For the next three years you will see a lot of investment. You will see countries or businesses that want to be leading because they see an advantage," says Glickman. "Then you will see others have to move along in that direction also." This episode of Business Lab is produced in partnership with Infosys. Laurel: From MIT Technology Review, I'm Laurel Ruma, and this is Business Lab. The show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace. Our topic today is sustainability, but on a global scale, from factories to supply chains to sustainable development goals for all the countries in the world. It's possible to design for sustainability, get a return on investment, and help fight climate change. My guest is Corey Glickman, who is the vice president and head of the sustainability and design business at Infosys. Corey is an expert in strategic design, digital transformation, customer experience strategy, and the use of visualization applied to the development of innovative products, processes, and services.

Artificial intelligence is one of the most revolutionary inventions in recent history. It is an invention that has changed our lives tremendously and allowed us to do things we never could before. While Artificial Intelligence has become more advanced, people are still coming up with new ways to create AI devices that can help make life easier for humans. This blog post will list the top 5 AI inventions which have helped change our world today! Artificial intelligence is the imitation of human cognitive processes by computers, especially computer systems, in order to simulate human intellect.