Energy

The NLP community has been focusing a lot on chasing the SOTA on standard and recent leaderboards (GLUE, SentEval...) over the recent years. While this aspiration has led to improvements in model performances, it has also resulted in a worrisome increase in model complexity and computational resources required to train and use the current state-of-the-art models. There is currently a lack of incentive to keep models small and efficient and research the optimal trade-offs between performances and efficiency. SustaiNLP 2020 (co-located with EMNLP 2020) has officially launched a shared-task/competition to promote the development of effective, energy-efficient models for difficult NLU tasks. The competition will end on 08/28.

Researchers at the Center for Nanoscale Materials (CNM), a U.S. Department of Energy (DOE) Office of Science User Facility located at the DOE's Argonne National Laboratory, have invented a machine-learning based algorithm for quantitatively characterizing, in three dimensions, materials with features as small as nanometers. Researchers can apply this pivotal discovery to the analysis of most structural materials of interest to industry. "What makes our algorithm unique is that if you start with a material for which you know essentially nothing about the microstructure, it will, within seconds, tell the user the exact microstructure in all three dimensions," said Subramanian Sankaranarayanan, group leader of the CNM theory and modeling group and an associate professor in the Department of Mechanical and Industrial Engineering at the University of Illinois at Chicago. "For example, with data analyzed by our 3D tool," said Henry Chan, CNM postdoctoral researcher and lead author of the study, "users can detect faults and cracks and potentially predict the lifetimes under different stresses and strains for all kinds of structural materials." Most structural materials are polycrystalline, meaning a sample used for purposes of analysis can contain millions of grains.

Artificial Intelligence (AI) in the oil and gas industry stands to reach US$2.85 billion by 2022. Because data is never special. Oil rigs may generate somewhere around 50 terabytes a year, but that kind of big data needs to be applicable to be useful and, unfortunately, humans do a terrible job of classifying things into datasets. Indeed, a good scenario will see 10% of the resulting datasets actually be beneficial. Most competing firms are also known to have access to the same datasets.

Sennheiser's second-generation high-end true wireless earbuds gain noise cancelling and longer battery life to do battle with Sony and Apple. The German firm's first earbuds were some of the best-sounding available. Now Sennheiser hopes its £280 Momentum True Wireless 2 can steal the show once again. The first thing you notice is just how big the earbuds are. Despite being slightly smaller than the previous versions they are still large, shaped like a fez with the eartip projecting out of one corner.

Despite years of hype (and plenty of worries) about the all-conquering power of Artificial Intelligence (AI), there still remains a significant gap between the promise of AI and its reality for business. Tech firms have pitched AI's capabilities for years, but for most organisations, the benefits of AI remain elusive. It's hard to gauge the proportion of businesses that are effectively using artificial intelligence today, and to what extent. Adoption rates shown in recent reports fall anywhere between 20% and 30%, with adoption typically loosely defined as "implementing AI in some form". A survey led by KPMG among 30 of the Global 500 companies found that although 30% of respondents reported using AI for a selective range of functions, only 17% of the companies were deploying the technology "at scale" within the enterprise.

As global energy regulations are strengthened, improving energy efficiency while maintaining performance of electronic appliances is becoming more important. Especially in air conditioning, energy efficiency can be maximized by adaptively controlling the airflow based on detected human locations; however, several limitations such as detection areas, the installation environment, and sensor quantity and real-time performance which come from the constraints in the embedded system make it a challenging problem. In this study, by using a low resolution cost effective vision sensor, the environmental information of living spaces and the real-time locations of humans are learned through a deep learning algorithm to identify the living area from the entire indoor space. Based on this information, we improve the performance and the energy efficiency of air conditioner by smartly controlling the airflow on the identified living area. In experiments, our deep learning based spatial classification algorithm shows error less than 5 .

Toyota has revealed plans to build a prototype "city" of the future on a 175-acre site at the base of Mt. Called the Woven City, it will be a fully connected ecosystem powered by hydrogen fuel cells. Envisioned as a "living laboratory," the Woven City will serve as a home to full-time residents and researchers who will be able to test and develop technologies such as autonomy, robotics, personal mobility, smart homes and artificial intelligence in a real-world environment.

The adoption of artificial intelligence and machine learning technologies has never been more critical. Due to COVID-19, many organizations need to find a new way of working. Ensuring production rates are reliable, if not increased, while limiting the number of personnel - in some cases down to 50%. Many asset heavy industries, such as water, transportation & energy are considered critical infrastructure. Every effort needs to be made to maintain these.

In the grand scheme of things, artificial intelligence (AI) is still in the very early stages of adoption by most organizations. However, most leaders are quite excited to implement AI into the company's business functions to start realizing its extraordinary benefits. While we have no way of knowing all the ways artificial intelligence and machine learning will ultimately impact business functions, here are 10 business functions that are ready to use artificial intelligence. Not only can AI help to develop marketing strategies, but it's also instrumental in executing them. Already AI sorts customers according to interest or demographic, can target ads to them based on browsing history, powers recommendation engines, and is a critical tool to give customers what they want exactly when they want it.