The modern world would be a pale shade of itself if not for the myriad foundational technologies developed at the Bell Telephone Labs. Its engineers invented the transistor and photovoltaic cell, charge-coupled devices, frickin' lasers -- even Unix and the C programming language. Those same engineers also worked with some of the Cold War era's most influential artists -- including Andy Warhol, Robert Rauschenberg, and Yvonne Rainer -- to create a wholly new style of artistic expression. In his new book, Making Art Work: How Cold War Engineers and Artists Forged a New Creative Culture, W. Patrick McCray follows the exploits of often-unsung technicians like rocket pioneer cum kinetic artist, Frank J. Malina and Bell Labs electrical engineer and Experiments in Art and Technology founder Billy Klüver, as they leveraged their technological prowess in the pursuit of creating compelling new works. The following excerpt is reprinted from Making Art Work: How Cold War Engineers and Artists Forged a New Creative Culture by W. Patrick McCray.
Researchers at Oregon State University are making key advances with a new type of optical sensor that more closely mimics the human eye's ability to perceive changes in its visual field. The sensor is a major breakthrough for fields such as image recognition, robotics and artificial intelligence. Findings by OSU College of Engineering researcher John Labram and graduate student Cinthya Trujillo Herrera were published today in Applied Physics Letters. Previous attempts to build a human-eye type of device, called a retinomorphic sensor, have relied on software or complex hardware, said Labram, assistant professor of electrical engineering and computer science. But the new sensor's operation is part of its fundamental design, using ultrathin layers of perovskite semiconductors--widely studied in recent years for their solar energy potential--that change from strong electrical insulators to strong conductors when placed in light.
Active learning (AL) could contribute to solving critical environmental problems through improved spatiotemporal predictions. Yet such predictions involve high-dimensional feature spaces with mixed data types and missing data, which existing methods have difficulties dealing with. Here, we propose a novel batch AL method that fills this gap. We encode and cluster features of candidate data points, and query the best data based on the distance of embedded features to their cluster centers. We introduce a new metric of informativeness that we call embedding entropy and a general class of neural networks that we call embedding networks for using it. Empirical tests on forecasting electricity demand show a simultaneous reduction in average prediction RMSE by up to 63-88% and data usage by up to 50-69% compared to passive learning (PL) benchmarks. Examples include the electricity consumption of buildings, required to operate sustainable power grids; the travel time between city zones, required for the smart charging of electric vehicles; and meteorological conditions, required for weather-based forecasting of wind and solar electricity generation. Sensing and labeling the ground truth data that is necessary for making these predictions in time and space usually comes at a high cost. This cost constrains the total number of sensors that we can place and use to query new data. A fundamental question that arises for many spatiotemporal prediction tasks is where and when to measure and query the data required to make the best possible predictions while staying within a maximum budget for sensors and data.
While photovoltaic (PV) systems are installed at an unprecedented rate, reliable information on an installation level remains scarce. As a result, automatically created PV registries are a timely contribution to optimize grid planning and operations. This paper demonstrates how aerial imagery and three-dimensional building data can be combined to create an address-level PV registry, specifying area, tilt, and orientation angles. We demonstrate the benefits of this approach for PV capacity estimation. In addition, this work presents, for the first time, a comparison between automated and officially-created PV registries. Our results indicate that our enriched automated registry proves to be useful to validate, update, and complement official registries.
Solar AI, a Singapore based startup incubated as a part of ENGIE Factory, collaborated with Omdena, to hyper-scale the deployment of distributed solar and the transition towards 100% renewables by modernizing the way rooftop solar is sold. Solar energy is a promising and freely available resource for managing the forthcoming energy crisis, without hurting the environment. There's enough solar energy hitting the Earth every hour to meet all of humanity's power needs for an entire year. The rooftop solar assessment process can be time consuming and expensive, taking anywhere between 1 hour to 2 full days to calculate the solar potential of each rooftop. In the solar industry, this has resulted in the cost of sales taking up to 30–40% of total project costs, significantly worsening the unit economics of solar projects.
San Francisco-based Aurora Solar, which taps a combination of lidar sensor data, computer-assisted design, and computer vision to streamline solar panel installations, today announced a $50 million raise. The company says it will leverage the funds to accelerate hiring across all teams and ramp up development of new features and services for solar installers and solar sales consultants. Despite recent setbacks, solar remains a bright spot in the still-emerging renewable energy sector. In the U.S., the solar market is projected to top $22.9 billion by 2025, driven by falling materials costs and growing interest in offsite and rooftop installations. Moreover, in China -- the world's leading installer of solar panels and the largest producer of photovoltaic power -- 1.84% of the total electricity generated in the country two years ago came from solar.
You can now run computations on your phone that would have been unthinkable a few years ago. But as small devices get smarter, we discover new uses for them that overwhelm their resources. If you want your phone to recognize a picture of your face (image classification) or to find faces in pictures (object detection), you want it to run a convolutional neural net (CNN). Modern computer vision applications are mostly built using CNNs. This is because vision applications tend to have a classifier at their heart--so, for example, one builds an object detector by building one classifier that tells whether locations in an image could contain an object, then another that determines what the object is.
The Dubai Electricity and Water Authority (Dewa) has adopted the use of Smart Dubai's Ethical AI Toolkit. It reports using it for 13 artificial intelligence (AI) use cases across various departments, registering an average performance rate of almost 90 per cent on complying with the principles and guidelines set out. Smart Dubai developed the toolkit to set clear guidelines on the ethical use of AI to prevent having a fragmented, incoherent approach to ethics, where every entity sets its own rules. Dewa's use of the toolkit was spread across several different departments. The Innovation & the Future (I&TF) division's use cases included outage planning and load forecasting, solar power generation forecasting, network design and area planning, visual inspection on solar photovoltaics and the virtual assistant Rammas.
This paper presents an eXplainable Fault Detection and Diagnosis System (XFDDS) for incipient faults in PV panels. The XFDDS is a hybrid approach that combines the model-based and data-driven framework. Model-based FDD for PV panels lacks high fidelity models at low irradiance conditions for detecting incipient faults. To overcome this, a novel irradiance based three diode model (IB3DM) is proposed. It is a nine parameter model that provides higher accuracy even at low irradiance conditions, an important aspect for detecting incipient faults from noise. To exploit PV data, extreme gradient boosting (XGBoost) is used due to its ability to detecting incipient faults. Lack of explainability, feature variability for sample instances, and false alarms are challenges with data-driven FDD methods. These shortcomings are overcome by hybridization of XGBoost and IB3DM, and using eXplainable Artificial Intelligence (XAI) techniques. To combine the XGBoost and IB3DM, a fault-signature metric is proposed that helps reducing false alarms and also trigger an explanation on detecting incipient faults. To provide explainability, an eXplainable Artificial Intelligence (XAI) application is developed. It uses the local interpretable model-agnostic explanations (LIME) framework and provides explanations on classifier outputs for data instances. These explanations help field engineers/technicians for performing troubleshooting and maintenance operations. The proposed XFDDS is illustrated using experiments on different PV technologies and our results demonstrate the perceived benefits.
A solar-powered autonomous drone scans for forest fires. A surgeon first operates on a digital heart before she picks up a scalpel. A global community bands together to print personal protection equipment to fight a pandemic. "The future is now," says Frédéric Vacher, head of innovation at Dassault Systèmes. And all of this is possible with cloud computing, artificial intelligence (AI), and a virtual 3D design shop, or as Dassault calls it, the 3DEXPERIENCE innovation lab. This open innovation laboratory embraces the concept of the social enterprise and merges collective intelligence with a cross-collaborative approach by building what Vacher calls "communities of people--passionate and willing to work together to accomplish a common objective." This podcast episode was produced by Insights, the custom content arm of MIT Technology Review. It was not produced by MIT Technology Review's editorial staff. "It's not only software, it's not only cloud, but it's also a community of people's skills and services available for the marketplace," Vacher says. "Now, because technologies are more accessible, newcomers can also disrupt, and this is where we want to focus with the lab." And for Dassault Systèmes, there's unlimited real-world opportunities with the power of collective intelligence, especially when you are bringing together industry experts, health-care professionals, makers, and scientists to tackle covid-19. Vacher explains, "We created an open community, 'Open Covid-19,' to welcome any volunteer makers, engineers, and designers to help, because we saw at that time that many people were trying to do things but on their own, in their lab, in their country."