Artificial Intelligence (AI) in Construction Market Size 2020 | Brief Analysis by Top Companies ...

Artificial Intelligence (AI) in Construction Market Size 2020 | Brief Analysis by Top Companies …

New Jersey, United States,- The report, titled Artificial Intelligence (AI) in Construction Market, is a comprehensive document that provides valuable insights into market elements like drivers, restraints, competitive landscape, and technology evolution. For a better understanding of the market, the report offers a comprehensive analysis of the key segments and future growth prospects. The current COVID-19 pandemic has significantly changed market dynamics and the global economy. The report provides an impact analysis of the pandemic on the entire market. It also provides an analysis of the current and future impact. The report provides a comprehensive analysis of the dynamic changes in trends and requirements due to the COVID-19 pandemic. The report also includes a post-COVID scenario and prospects for future growth.

Global Artificial Intelligence (AI) in Construction Market was valued at USD 422.64 Million in 2018 and is projected to reach USD 4359.68 Million by 2026, growing at a CAGR of 33.70% from 2019 to 2026.

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The competitive analysis includes the most important players as well as the innovations and business strategies they pursue. The report captures the best long-term growth opportunities for the industry and includes the latest process and product developments. The report provides basic information about the companies as well as their market position, history, market capitalization and sales. The report covers the sales figures, market growth rate, and gross profit margin of each player based on the regional classification and overall market position. The report contains a separate analysis of recent business strategies such as mergers, acquisitions, product launches, joint ventures, partnerships and collaborations.

Key features of the Report:

The report covers extensive analysis of the key market players in the market, along with their business overview, expansion plans, and strategies. The key players studied in the report include:

Building System Planning

Microsoft

IBM

Oracle

SAP

Alice Technologies

eSUB

Smartvid.io

Aurora Computer Services and Autodesk

The report provides valuable insights about the advancements of the Artificial Intelligence (AI) in Construction market and the approaches regarding the Artificial Intelligence (AI) in Construction market with analysis of each region. The report further talks about the dominant aspects of the market and explores each segment.

Market Breakdown:

The market breakdown provides market segmentation data based on the availability of the data and information. The market is segmented on the basis of types and applications.

Artificial Intelligence (AI) in Construction Market Segmentation:

Global Artificial Intelligence (AI) in Construction Market, By Technology

• Natural Language Processing
• Machine Learning and Deep Learning

Global Artificial Intelligence (AI) in Construction Market, By Application

• Field Management
• Project Management
• Risk Management
• Schedule Management
• Others

Global Artificial Intelligence (AI) in Construction Market, By Industry Type

• Heavy Construction
• Institutional Commercials
• Residential
• Others

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To understand the Artificial Intelligence (AI) in Construction market dynamics, the market is analyzed across major global regions and countries. Verified Market Research provides customized specific regional and country-wise analysis of the key geographical regions as follows:

North America: USA, Canada, Mexico

Latin America: Argentina, Chile, Brazil, Peru, and Rest of Latin America

Europe: UK, Germany, Spain, Italy, and Rest of EU

Asia-Pacific: India, China, Japan, South Korea, Australia, and Rest of APAC

Middle East & Africa: Saudi Arabia, South Africa, U.A.E., and Rest of MEA

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Key Elements Addressed in the Report:

Market Scenario:

The report highlights the essential features of the business sphere of the Artificial Intelligence (AI) in Construction industry. It covers development trends, factors driving the growth of the market, and segments influencing the growth of the market. It covers the product types, applications, types, deployments, and developments happening in the market.

Market Highlights:

The report provides an in-depth analysis of the market involving key elements, revenue estimations, cost analysis, import/export, production and consumption trends, CAGR, gross margin, and supply & demand patterns. The report further gives an idea about the development factors and advancement patterns of the Artificial Intelligence (AI) in Construction industry.

Analytical Tools:

The Artificial Intelligence (AI) in Construction Market is assessed through extensive primary and secondary research, which is further validated and verified by industry experts and professionals. The report examines key players operating in the market along with their market position, market share, revenue, gross margin, and business strategies. SWOT analysis and Porter’s Five Forces Analysis are conducted to examine and assess the market and its players. Moreover, the report also offers a feasibility study and investment return analysis to assist the readers in making strategic investment plans.

Key Questions Answered in the Report:

What will be the expected growth of the Artificial Intelligence (AI) in Construction market till 2027?
What key factors will influence the growth of the market?
What are the key drivers, restraints, opportunities, threats, and challenges the market will face in the coming years?
Which segment and region are expected to dominate the market in the forecast period?

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Our 250 Analysts and SME’s offer a high level of expertise in data collection and governance use industrial techniques to collect and analyze data on more than 15,000 high impact and niche markets. Our analysts are trained to combine modern data collection techniques, superior research methodology, expertise, and years of collective experience to produce informative and accurate research.

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Published at Sun, 23 Aug 2020 19:18:45 +0000

What’s New In Gartner’s Hype Cycle For Emerging Technologies, 2020

Health passports lead all technologies in their potential to make a transformational impact in two years or less, accelerated by early adopters in China (Health Code) and India (Aarogya Setu).
Social distancing technologies enter the Hype Cycle for the first time at the top of the Peak of Inflated Expectations due to the extraordinary amount of media coverage and client inquiries.
Gartner continues to expand its coverage of AI’s potential in this year’s Hype Cycle, with several categories added this year, including Composite AI, Generative AI, Responsible AI, AI-augmented development Embedded AI and AI-augmented design.

These and many other new insights are from Gartner Hype Cycle For Emerging Technologies, 2020 published earlier this year and summarized in the recent Gartner blog post, 5 Trends Drive the Gartner Hype Cycle for Emerging Technologies, 2020. Gartner’s definition of Hype Cycles includes five phases of a technology’s lifecycle and is explained here.

Of the many Hype Cycles Gartner produces every year, this one is noteworthy as its methodology evaluated more than 1,700 emerging technologies into a concise set of the 30 most transformational in the next five to ten years. AI-specific technologies appearing for the first time in the Hype Cycle are defined following the graphic. The Gartner Hype Cycle for Emerging Technologies, 2020, is shown below:

AI-Specific Details Of What’s New In Gartner’s Hype Cycle for Emerging Technologies, 2020

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Health passports are mobile apps that indicate the relative level of infection risk a person is and whether they can gain access to buildings, supermarkets, restaurants, public spaces and transportation. Early adopters in China and India are proving the combination of health passports and screening methodologies that are effective in stopping the spread of Covid-19 while also giving people the freedom to use public spaces and transportation. China’s Health Code is widely used as a screening tool to minimize the risk of Covid-19 transmission. It provides the user with a color QR code based on their designated health status: Red is confirmed infected with Covid-19; Yellow should be in quarantine and Green is free to travel. Health Code checks are very common, making it difficult to move without having a green code. Early leaders in health passports include Alipay, Bizagi, Circle Pass Enterprises, Folio, Vottun and WeChat.
Formative AI is a new category of technologies that Gartner predicts will be able to sense and dynamically respond to changing situational conditions. One of the primary uses cases of formative AI would be providing UI/UX designers with real-time interactive feedback to improve the usability of software and intelligent products. Gartner also predicts formative AI will be used to streamline how mathematical and machine learning models are created and fine-tuned over time. Key technologies that comprise formative AI in the Hype Cycle include the following: AI-augmented design, AI-augmented development, ontologies and graphs, small data, composite AI, adaptive machine learning (ML), self-supervised learning, generative AI and generative adversarial networks.
Augmented AI design is new to the Hype Cycle this year and has the potential to transform how digital and smart, connected products are designed, produced and sold. Gartner defines AI-augmented design as the use of artificial intelligence (AI), machine learning and natural language processing technologies to generate automatically and evolve via machine learning, user flows, screen designs, content and presentation layer code for digital products. AI-augmented design is already transforming the customer experience through decision support and personalization in CX products and site builder platforms, including those from B12 who have added AI to assemble content and best practices for your business type in under a minute. Gartner expects to see AI at work in the digital product design platform market. Gartner predicts Adobe Xd, Figma and InVision will take the lead in this emerging technology category.
Composite AI is new to the Hype Cycle this year and refers to the aggregating or combining of different AI techniques to improve learning accuracy and efficiency. Gartner believes that Composite AI will be an enabling technology for organizations that don’t have access to large historical data sets or have AI expertise in-house to complete complex analyses. Second, Gartner believes that Composite AI will help expand the scope and quality of AI applications. Early leaders in this area include ACTICO, Beyond Limits, BlackSwan Technologies, Cognite, Exponential AI, FICO, IBM, Indico, Petuum and ReactiveCore.
Embedded AI is one of the fascinating new technologies on the Hype Cycle, given its potential to increase the accuracy, insights and intelligence gained from current and next-generation sensors. Gartner defines Embedded AI refers to the use of AI/ML techniques within embedded systems to enable an analysis of locally captured data. Reducing the time it takes to analyze sensor data while improving the insights gained could transform sensor-based intelligence and the data being captured using IoT and smart sensors. From consumer electronics products to production machinery and long-life assets, embedded AI could provide insights needed to improve customer experiences, increase production efficiency and know more about the Maintenance, Repair and Overhaul (MRO) cycles. Early leaders in this area include Arm, Cartesiam, NXP Semiconductors, One Tech, Renesas Electronics and STMicroelectronics.
Generative AI is also new on the Hype Cycle for the first time this year and is the technology most often used for creating “deep fakes” videos and digital content. Generative AI is a variety of ML methods that learn a representation of artifacts from the data and use it to generate brand-new, completely original, realistic artifacts that preserve a likeness to the training data, but do not repeat it. Generative AI can produce original content (images, video, music, speech, text — even in combination), improve or alter existing content and create new data elements. Generative AI is the technologies used for creating “deep fakes” digital content by bad actors attempting to cause dangerous disruption to regional and political stability. The Partnership on AI and DARPA is pursuing the detection of “deep fakes” to counteract fraud, disinformation, instigation of social unrest.
Responsible AI is another new category on the Hype Cycle this year and is defined as a series of technologies whose purpose is to assist businesses in making more ethical, balanced business decisions by attempting to reduce bias. The goal of Responsible AI is to streamline how organizations put responsible practices in place to ensure positive AI development and use. One of the most urgent use cases of Response AI is identifying and stopping “deep fakes” production globally. Gartner defines the category with use cases that involve improving business and societal value, reducing risk, increasing trust and transparency and reducing bias mitigation with AI. Of the new AI-based additions to the Hype Cycle this year, this is one that leads all others on its potential to use AI for good. Gartner believes responsible AI also needs to increase the explainability, accountability, safety, privacy and regulatory compliance of organizations as well.
AI-augmented development is on the Hype Cycle for the first time this year and its purpose is to improve the cycle times of application and DevOps teams in creating high-quality software faster and more consistently. Gartner defines AI-augmented development (AIAD) is the use of AI technologies such as machine learning (ML), natural language processing (NLP) and similar technologies to accelerate app and DevOps cycles. Early leaders in this area include Codota, Deep Code, Google, Kite, Mendix, Microsoft, OutSystems and Parasoft.
Self-supervised learning is new to the Hype Cycle this year, positioned as an enabling technology to help organizations adopt supervised machine learning techniques. While in initial R&D at Craftworks, Facebook, Google and Microsoft, self-supervised learning is a nascent technology that seeks to overcome one of the biggest drawbacks of supervised learning, which is the need to have access to typically large amounts of labeled data. Gartner predicts the potential impact and benefits of self-supervised learning are very large, as it will extend the applicability of machine learning to organizations that do not have the availability of large datasets.
Twenty-two technologies were removed or reassigned from this years’ Hype Cycle for Emerging Technologies compared to 2019. The 22 technologies that are no longer on the Hype Cycle for Emerging Technologies include the following: 3D sensing cameras, 5G, AI cloud services, AR cloud, Augmented Intelligence, Autonomous driving Level 4, Autonomous driving Level 5, Biochips, Decentralized web, DigitalOps, Edge AI, Edge analytics, Emotion AI, Flying autonomous vehicles, Graph analytics, Immersive workspaces, Knowledge graphs, Light cargo delivery drones, Low Earth orbit satellite systems, Personification, Synthetic data and Transfer learning.