The open-endedness of the challenge means that AIs need to master multiple objectives. To win, they must impress eight human judges from a range of backgrounds, including architects, archaeologists, and game designers. These judges score the AI city planners in four areas: how well they adapt their designs to specific locations; how well the layouts work, according to criteria such as whether there are bridges and roads between different areas; how appealing they are aesthetically; and how much the designs evoke a narrative--are there details that tell a story about how a town came to be, such as a ruin or a pit from which building materials might have been mined? "Making a Minecraft village for an unseen map is something a 10-year-old human could do," says Salge. "But it is really difficult for an AI." For example, one entrant started by identifying the type of environment--desert or forest, say--and then generated buildings that looked as if they had been built out of common local materials.
Digital transformation is one of the top priorities for industrial companies. The largest players are already moving in this direction, for many years continuously working to improve production efficiency and launching large-scale optimisation programs. They're called advanced analytics or digital innovation, and at their core, the technology could be summarised under artificial intelligence. In all cases, the efforts to utilise AI models or data analytics systems are part of a bigger digital transformation effort of the progressing companies. In an industrial context, such strategies for cost-saving and process optimisation often start from pilot projects, or top management directives for digital change guide them. In general, changes in processes or investments in capital-intensive and competitive industries require large sums of money. Traditional capital expenditures usually stretch over a long period, so a current financial standing may not allow for a complete physical overhaul of the plants or facilities. These high costs lead to the search for cheaper alternatives.
The UK's HS2 has trialled a new artificial intelligence-based carbon and cost estimating solution to decrease carbon emissions. The technology was trialled at several HS2 locations managed by the Skanska Costain STRABAG joint venture. The solution helps in automating building information model (BIM) processes. It enables simulating multiple design options using different combinations and types of construction materials. The process helps in measuring and comparing the environmental impacts and carbon emissions for each simulation and accordingly design a cost-effective and environmentally friendly construction model.
Sophisticated heating and cooling systems in Buildings adjust themselves based on the predicted weather. But when the forecast is imperfect – as it often is – buildings can end up wasting energy. A new approach developed by Cornell Researchers predicts the accuracy of the weather forecast using a machine learning model trained with years' worth of data on forecasts and actual weather conditions. The Researchers combined that predictor with a mathematical model that considers building characteristics including the size and shape of rooms, the construction materials, the location of sensors and the position of windows. The result is a smart control system that can reduce energy usage by up to 10 percent, according to a case study the research team conducted on Toboggan Lodge, a nearly 90-year-old building on Cornell's campus.
Toronto, Ontario--(Newsfile Corp. - September 16, 2020) - GoldSpot Discoveries Corp. (TSXV: SPOT) (the "Company" or "GoldSpot") has been engaged by Cerrado Gold Inc. ("Cerrado") to apply machine learning and its proprietary data science expertise to identify new exploration targets on Cerrado's Minera Don Nicolas (MDN) project, located in Santa Cruz, Argentina. In its analysis, GoldSpot will work with Cerrado's technical team to integrate and analyze geological and remote sensing data available in the area. The process will explore the potential for gold mineralization within the MDN properties, to produce GoldSpot Smart Targets which fuse geoscience knowledge with data science insights. "Minera Don Nicolas is in the mineral and data rich Deseado Massif, an area where GoldSpot is having significant success, particularly at Yamana Gold's Cerro Moro project. MDN has robust property-wide datasets and we look forward to supporting Cerrado's technical team and advancing exploration efforts. The project has significant potential with a land package of more than 273,000 hectares," stated Denis Laviolette, Executive Chairman and President of GoldSpot Discoveries.
Metal-organic frameworks (MOFs) are a class of materials that contain nano-sized pores. These pores give MOFs record-breaking internal surface areas, which can measure up to 7,800 m2 in a single gram of material. As a result, MOFs are extremely versatile and find multiple uses: separating petrochemicals and gases, mimicking DNA, producing hydrogen, and removing heavy metals, fluoride anions, and even gold from water are just a few examples. Because of their popularity, material scientists have been rapidly developing, synthesizing, studying, and cataloguing MOFs. Currently, there are over 90,000 MOFs published, and the number grows every day.
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Data mining is considered to be one of the popular terms of machine learning as it extracts meaningful information from the large pile of datasets and is used for decision-making tasks. It is a technique to identify patterns in a pre-built database and is used quite extensively by organisations as well as academia. The various aspects of data mining include data cleaning, data integration, data transformation, data discretisation, pattern evaluation and more. Below, we have listed the top eight data mining techniques in machine learning that is most used by data scientists. Association Rule Learning is one of the unsupervised data mining techniques in which an item set is defined as a collection of one or more items.