Artificial Intelligence (AI) in Real Estate Market 2021 An Regional Analysis of the Execution of a …

Artificial Intelligence (AI) in Real Estate Market 2021 An Regional Analysis of the Execution of a …

“

Artificial Intelligence (AI) in Real Estate Market’ report contains all the required data and full guidance has been given to the readers and competitors of the global Artificial Intelligence (AI) in Real Estate industry. It gives an accurate study of the Artificial Intelligence (AI) in Real Estate market for the forecast period from 2020 to 2026. Initially, it introduces the market segments, statistics, and major growing regions governing the global Artificial Intelligence (AI) in Real Estate market. It also sheds light on the production rate, demand / supply ratio and Artificial Intelligence (AI) in Real Estate import / export details come to market in the immediate future. Artificial Intelligence (AI) in Real Estate size, estimation and qualitative intuition can help surround the future. When Artificial Intelligence (AI) in Real Estate collides with past and present market demands and conditions, the inevitable Artificial Intelligence (AI) in Real Estate size can be calculated.

Get a sample of the report from https://www.orbisresearch.com/contacts/request-sample/5472918

Leading competitors in the Artificial Intelligence (AI) in Real Estate market:

Skyline AI
Engel & Völkers
Baidu Inc.
Cape Analytics
IBM
PwC

The worldwide Artificial Intelligence (AI) in Real Estate market outline provides a compact randon of possibilities, challenges, driving variables, and Artificial Intelligence (AI) in Real Estate trend. In addition, it provides share Artificial Intelligence (AI) in Real Estate industry, looking at manufacturers, socioeconomic, types and its applications. Generation technology, Artificial Intelligence (AI) in Real Estate margins and assembling expenses will help to grow and increase the net revenue of the Artificial Intelligence (AI) in Real Estate market. The new exploration innovations Artificial Intelligence (AI) in Real Estate market is measured in this exploration answer to experience the potential for Artificial Intelligence (AI) in Real Estate intrusion over the forecast period from 2026 to 2020.

The report presents a thorough research study of the global Artificial Intelligence (AI) in Real Estate market including accurate forecasts and analysis at the global, regional and country levels. It provides a comprehensive view and detailed value chain analysis of the global Artificial Intelligence (AI) in Real Estate market to help players to closely understand the significant changes in business activities observed in the industry. It also presents an in-depth segmented analysis of the global Artificial Intelligence (AI) in Real Estate market where key product and application segments are highlighted. The readers are provided with the actual market figures related to the global Artificial Intelligence (AI) in Real Estate market size in terms of price and volume for the forecast period 2020-2026.

Different product categories include:

Machine Learning
Natural Language Processing (NLP)
Computer Vision

Global Artificial Intelligence (AI) in Real Estate industry has a number of end-user applications including:

Large Enterprises
Small and Mid-sized Enterprises (SMEs)

New and emerging Artificial Intelligence (AI) in Real Estate players are decisively evaluated with profitable data that will be of importance and invaluable to Artificial Intelligence (AI) in Real Estate market participants as predicted. Artificial Intelligence (AI) in Real Estate estimates, pie-charts, tables and graphical illustrations of figures. The various phases of Artificial Intelligence (AI) in Real Estate are mainly depicted in this report – the initial phase, the growth, the capacity phase, and the stagnation phase. It forecasts a wide assortment of worldwide Artificial Intelligence (AI) in Real Estate market for individuals and venturing into Artificial Intelligence (AI) in Real Estate market.

Benefits of Global Artificial Intelligence (AI) in Real Estate Market Report:

– Artificial Intelligence (AI) in Real Estate provides point by point data on market share, supply chain, and achievement factors that take into consideration the ultimate goal of the reader to satisfy each concert.
– An analysis of the business capabilities of the Artificial Intelligence (AI) in Real Estate industry partnered with usage and manufacturing volume.
– Broad scope of the worldwide Artificial Intelligence (AI) in Real Estate market for better understanding.
– Artificial Intelligence (AI) in Real Estate Market Clear position in an effective configuration for the buyer and trader to hand over the correct information to the intended interest groups, end-clients and consumers.
– Artificial Intelligence (AI) in Real Estate market diagram, noticeable economic gauge, for example, industry development, market size, forecast period and GDP.

For more Information or Any Query Visit: https://www.orbisresearch.com/contacts/enquiry-before-buying/5472918

Artificial Intelligence (AI) in Real Estate Industry Research Methodology:

* Previous market estimates are those of the end buyers, the current players of the Artificial Intelligence (AI) in Real Estate market, their execution during the previous forecast and breaking current Artificial Intelligence (AI) in Real Estate information and forecasting future market inclinations.
* Artificial Intelligence (AI) in Real Estate market analysis includes remarkable information, people’s reactions, capability and mutual domain data.
* Revenue is taken as a measure to evaluate Artificial Intelligence (AI) in Real Estate size and the base year is taken into consideration.
* Data recovered from various Artificial Intelligence (AI) in Real Estate sources is then approved using diverse tools and methods, for example, a triangular strategy to gather both the integrity and subjective information of the end products of the Artificial Intelligence (AI) in Real Estate market.
* Once the Artificial Intelligence (AI) in Real Estate information is gathered, it is presented in an understandable format. The report additionally showcases the Artificial Intelligence (AI) in Real Estate market product portfolio of SWOT investigation, late growth, expansion of regions, and individual market pioneers.

Key highlights of the Artificial Intelligence (AI) in Real Estate market in the COVID-19 pandemic include the report:

– Artificial Intelligence (AI) in Real Estate Market competition by major manufacturers in the industry.
– Sourcing strategies, industrial chain information and downstream buyer data discussed.
– Artificial Intelligence (AI) in Real Estate Distributors and Traders Marketing Strategy Analysis, COVID-19 focuses on sector-wise needs in the epidemic.
– Also highlights the key growth areas of the Artificial Intelligence (AI) in Real Estate market and how they will perform in the coming years.
– Artificial Intelligence (AI) in Real Estate Industry Vendors who are providing a wide range of product lines and intensifying the competitive landscape in the COVID-19 crisis.

Direct purchase a single user copy of the report @ https://www.orbisresearch.com/contact/purchase-single-user/5472918

About Us:

Orbis Research (orbisresearch.com) is a single point aid for all your market research requirements. We have vast database of reports from the leading publishers and authors across the globe. We specialize in delivering customized reports as per the requirements of our clients. We have complete information about our publishers and hence are sure about the accuracy of the industries and verticals of their specialization. This helps our clients to map their needs and we produce the perfect required market research study for our clients.

Contact Us:

Hector Costello
Senior Manager – Client Engagements
4144N Central Expressway,
Suite 600, Dallas,
Texas – 75204, U.S.A.
Phone No.: USA: +1 (972)-362-8199 | IND: +91 895 659 5155
Email ID: [email protected]

”

https://farmingsector.co.uk/

Published at Mon, 04 Jan 2021 03:33:45 +0000

Want Cheaper Nuclear Energy? Turn the Design Process Into a Game for Artificial Intelligence

In this AI-designed layout for a boiling water reactor, fuel rods are ideally positioned around two fixed water rods to burn more efficiently. MIT researchers ran the equivalent of 36,000 simulations to find the optimal configurations, which could extend the life of the rods in an assembly by about 5 percent, generating $3 million in savings per year if scaled to the full reactor. Colors correspond to varying amounts of uranium and gadolinium oxide in each rod. Credit: Majdi Radaideh/MIT

Researchers show that deep reinforcement learning can be used to design more efficient nuclear reactors.

Nuclear energy provides more carbon-free electricity in the United States than solar and wind combined, making it a key player in the fight against climate change. But the U.S. nuclear fleet is aging, and operators are under pressure to streamline their operations to compete with coal- and gas-fired plants.

One of the key places to cut costs is deep in the reactor core, where energy is produced. If the fuel rods that drive reactions there are ideally placed, they burn less fuel and require less maintenance. Through decades of trial and error, nuclear engineers have learned to design better layouts to extend the life of pricey fuel rods. Now, artificial intelligence is poised to give them a boost.

Researchers at MIT and Exelon show that by turning the design process into a game, an AI system can be trained to generate dozens of optimal configurations that can make each rod last about 5 percent longer, saving a typical power plant an estimated $3 million a year, the researchers report. The AI system can also find optimal solutions faster than a human, and quickly modify designs in a safe, simulated environment. Their results were published in December 2020 in the journal Nuclear Engineering and Design.

“This technology can be applied to any nuclear reactor in the world,” says the study’s senior author, Koroush Shirvan, an assistant professor in MIT’s Department of Nuclear Science and Engineering. “By improving the economics of nuclear energy, which supplies 20 percent of the electricity generated in the U.S., we can help limit the growth of global carbon emissions and attract the best young talents to this important clean-energy sector.”

In a typical reactor, fuel rods are lined up on a grid, or assembly, by their levels of uranium and gadolinium oxide within, like chess pieces on a board, with radioactive uranium driving reactions, and rare-earth gadolinium slowing them down. In an ideal layout, these competing impulses balance out to drive efficient reactions. Engineers have tried using traditional algorithms to improve on human-devised layouts, but in a standard 100-rod assembly there might be an astronomical number of options to evaluate. So far, they’ve had limited success.

The researchers wondered if deep reinforcement learning, an AI technique that has achieved superhuman mastery at games like chess and Go, could make the screening process go faster. Deep reinforcement learning combines deep neural networks, which excel at picking out patterns in reams of data, with reinforcement learning, which ties learning to a reward signal like winning a game, as in Go, or reaching a high score, as in Super Mario Bros.

Here, the researchers trained their agent to position the fuel rods under a set of constraints, earning more points with each favorable move. Each constraint, or rule, picked by the researchers reflects decades of expert knowledge rooted in the laws of physics. The agent might score points, for example, by positioning low-uranium rods on the edges of the assembly, to slow reactions there; by spreading out the gadolinium “poison” rods to maintain consistent burn levels; and by limiting the number of poison rods to between 16 and 18.

“After you wire in rules, the neural networks start to take very good actions,” says the study’s lead author Majdi Radaideh, a postdoc in Shirvan’s lab. “They’re not wasting time on random processes. It was fun to watch them learn to play the game like a human would.”

Through reinforcement learning, AI has learned to play increasingly complex games as well as or better than humans. But its capabilities remain relatively untested in the real world. Here, the researchers show that reinforcement learning has potentially powerful applications.

“This study is an exciting example of transferring an AI technique for playing board games and video games to helping us solve practical problems in the world,” says study co-author Joshua Joseph, a research scientist at the MIT Quest for Intelligence.

Exelon is now testing a beta version of the AI system in a virtual environment that mimics an assembly within a boiling water reactor, and about 200 assemblies within a pressurized water reactor, which is globally the most common type of reactor. Based in Chicago, Illinois, Exelon owns and operates 21 nuclear reactors across the United States. It could be ready to implement the system in a year or two, a company spokesperson says.

Reference: “Physics-informed reinforcement learning optimization of nuclear assembly design” by Majdi I. Radaideh, Isaac Wolverton, Joshua Joseph, James J. Tusar, Uuganbayar Otgonbaatar, Nicholas Roy, Benoit Forget and Koroush Shirvan, 5 December 2020, Nuclear Engineering and Design.
DOI: 10.1016/j.nucengdes.2020.110966

The study’s other authors are Isaac Wolverton, a MIT senior who joined the project through the Undergraduate Research Opportunities Program; Nicholas Roy and Benoit Forget of MIT; and James Tusar and Ugi Otgonbaatar of Exelon.

Published at Mon, 04 Jan 2021 03:22:30 +0000