Human intelligence has been creating and maintaining complex systems since the beginnings of civilizations. In modern times, digital twins have emerged to aid operations of complex systems, as well as improve design and production. Artificial intelligence (AI) and extended reality (XR) – including augmented reality (AR) and virtual reality (VR) – have emerged as tools that can help manage operations for complex systems. Digital twins can be enhanced with AI and emerging user interface (UI) technologies like XR can improve people's abilities to manage complex systems via digital twins. Digital twins can marry human and AI to produce something far greater by creating a usable representation of complex systems. End users do not need to worry about the formulas that go into machine learning (ML), predictive modeling and artificially intelligent systems, but also can capitalize on their power as an extension of their own knowledge and abilities. Digital twins combined with AR, VR and related technologies provide a framework to overlay intelligent decision making into day-to-day operations, as shown in Figure 1. Figure 1: A digital twin can be enhanced with artificial intelligence (AI) and intelligent realities user interfaces, such as extended reality (XR), which includes augmented reality (AR) and virtual reality (VR). The operations of a physical twin can be digitized by sensors, cameras and other such devices, but those digital streams are not the only sources of data that can feed the digital twin. In addition to streaming data, accumulated historical data can inform a digital twin. Relevant data could include data not generated from the asset itself, such as weather and business cycle data. Also, computer-aided design (CAD) drawings and other documentation can help the digital twin provide context.
The New York Times reported that an AI system known as Aristo had become the first to successfully pass a standardized eighth-grade science test. The achievement arrived four years after a competition in which 700-plus scientists all failed to build a system capable of accomplishing the same task despite the incentive of the contest's $80,000 prize. Aristo has been viewed as a significant breakthrough in the evolution of AI technology, with far-reaching implications for natural language processing, business intelligence and more. The system provides a vivid illustration of the differences between human and artificial intelligence. It shows why the most effective AI systems still incorporate help from human experts -- a fact that has big implications for AI in business and other applications. The Aristo system represents a major step toward imbuing AI with what one Wired article refers to as "common sense," the expansive and unconscious background knowledge that we apply when navigating new situations or engaging in conversation.
Most data organisations hold is not labeled, and labeled data is the foundation of AI jobs and AI projects. "Labeled data, means marking up or annotating your data for the target model so it can predict. In general, data labeling includes data tagging, annotation, moderation, classification, transcription, and processing." Particular features are highlighted by labeled data and the classification of those attributes maybe be analysed by models for patterns in order to predict the new targets. An example would be labelling images as cancerous and benign or non-cancerous for a set of medical images that a Convolutional Neural Network (CNN) computer vision algorithm may then classify unseen images of the same class of data in the future. Niti Sharma also notes some key points to consider.
Quadrotor drones are extremely maneuverable flying machines. In the hands of a skilled pilot, they can perform feats of aerial acrobatics not possible with any other aircraft. However, most of us are not skilled pilots. What if there was an AI that could do all that fancy flying for you? Researchers from the University of Zurich and ETH Zurich have created just such a system, which operates entirely on-board the aircraft and has never crashed -at least in real life.
Facebook and Carnegie Mellon University have announced they are trying to use artificial intelligence (AI) to find new "electrocatalysts" that can help to store electricity generated by renewable energy sources. Electrocatalysts can be used to convert excess solar and wind power into other fuels, such as hydrogen and ethanol, that are easier to store. However, today's electrocatalysts are rare and expensive, with platinum being a good example, and finding new ones hasn't been easy as there are billions of ways that elements can be combined to make them. Researchers in the catalysis community can currently test tens of thousands of potential catalysts a year but Facebook and Carniegie Mellon believe they can increase the number to millions, or even billions, of catalysts with the help of AI. The social media giant and the university on Wednesday released some of their own AI software "models" that can help to find new catalysts but they want other scientists to have a go as well.
Alphabet's X lab has unveiled its latest moonshot project: a crop-inspecting robot named the "plant buggy." The solar-powered prototype roams autonomously through fields, using GPS software to identify the location of plants. When it finds them, it uses cameras and machine perception tools to study their traits and any issues in the field. The cart combines data collected from the field, such as plant height and fruit size, with environmental factors including weather forecasts and soil information. This is all analyzed by machine learning to evaluate how the crops are growing and interacting with their surroundings.
Our traditional solution to the unpredictable nature of renewable energy sources like solar and wind power has generally been to simply dump the excess wattage back into the local grid or sequester it away in utility-scale batteries. But as more and more of our power generation is created by renewables, their production capacities can potentially outstrip that of the local grid while battery technology can quickly become prohibitively expensive at scale. One alternative is putting that excess power to work driving catalytic reactions. "There are a lot of different ways that we can store the energy," Zack Ulissi, CMU Assistant Professor of Chemical Engineering and Materials Science and Engineering, told Engadget. "The most well known is you take water and you electrolyze it to split it into hydrogen and oxygen. And then you can take that hydrogen and run it into a hydrogen fuel cell."
In 2018, Alphabet's X lab said it was in the process of exploring how it could use artificial intelligence to improve farming. On Monday, X announced that its "computational agriculture" project is called Mineral. The Mineral team has spent the last several years "developing and testing a range of software and hardware prototypes based on breakthroughs in artificial intelligence, simulation, sensors, robotics and more." One of the tools that has come out of the project is a robotic plant buggy. Powered by solar panels, the machine makes its way across a farmer's field, examining every plant it passes along the way with an array of cameras and sensors.
From the stone age, to the bronze, iron age, and modern silicon age, the discovery and characterization of new materials has always been instrumental to humanity's progress and development. With the current pressing need to address sustainability challenges and find alternatives to fossil fuels, we look for solutions in the development of new materials that will allow for renewable energy. To discover materials with the required properties, materials scientists can perform high-throughput materials discovery, which includes rapid synthesis and characterization via X-ray diffraction (XRD) of thousands of materials. A central problem in materials discovery, the phase map identification problem, involves the determination of the crystal structure of materials from materials composition and structural characterization data. This analysis is traditionally performed mainly by hand, which can take days for a single material system.