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According to Data Bridge Market Research new analysis, The Global Artificial Intelligence in Aviation Market analysis provides a high-level summary of classification, competition, and strategic actions taken in recent years. For a global scenario, the global Artificial Intelligence in Aviation market report provides historical details, future forecasts, and market size.

Artificial Intelligence in Aviation report gives clear idea to this industry in regard with what is already available in the market, what market anticipates, the competitive environment, and what to be get done to surpass the competitor. The report presents key statistics on the market status of Global and Regional manufacturers and proves to be an important source of guidance and direction for companies and individuals interested in the industry. To achieve maximum return on investment (ROI), its very fundamental to figure out market parameters such as brand awareness, market landscape, possible future issues, industry trends and customer behaviour where this Artificial Intelligence in Aviation report comes into picture.

This global Artificial Intelligence in Aviation market research report has complete overview of the market, covering various aspects such as product definition, segmentation based on various parameters, and the prevailing vendor landscape. Information and data provided through the Artificial Intelligence in Aviation report can be very decisive for this industry when it comes to dominating the market or creating a mark in the market as a new emergent. The research study performed in Artificial Intelligence in Aviation report also helps to understand the various drivers and restraints impacting the market during the forecast period. This market research report serves a great purpose of better decision making and achieving competitive advantage.

Data Bridge Market Research analyses the artificial intelligence in aviation market will exhibit a CAGR of 46.3% for the forecast period of 2022-2029 and is likely to reach the USD 9,995.84 million by 2029.

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Top Players Analysed in the Report are:

Some of the major players operating in the artificial intelligence in aviation market are IBM, Microsoft, Amazon Web Services, Inc., Airbus S.A.S., Xilinx, NVIDIA Corporation, Intel Corporation, General Electric, Micron Technology, Inc., Garmin Ltd., Lockheed Martin Corporation, SAMSUNG, Thales Group, MINDTITAN, Mitsubishi Electric Corporation, OMRON Corporation, TAV Technologies and IRIS Automation, among others.

Porters five forces model in the report provides insights into the competitive rivalry, supplier and buyer positions in the market and opportunities for the new entrants in the global Artificial Intelligence in Aviation market over the period. Further, Growth Matrix gave in the report brings an insight into the investment areas that existing or new market players can consider.

Research Methodology

Our primary research involves extensive interviews and analysis of the opinions provided by the primary respondents. The primary research starts with identifying and approaching the primary respondents, the primary respondents are approached include

Our primary research respondents typically include

Secondary research involves extensive exploring through the secondary sources of information available in both the public domain and paid sources. Each research study is based on over 500 hours of secondary research accompanied by primary research. The information obtained through the secondary sources is validated through the crosscheck on various data sources.

Read Detailed Index of full Research Study @ https://www.databridgemarketresearch.com/reports/global-artificial-intelligence-in-aviation-market

Who Will Get Advantage of This Report?

The prime aim of the Global Artificial Intelligence in Aviation Market is to provide industry investors, private equity companies, company leaders and stakeholders with complete information to help them make well-versed strategic decisions associated to the chances in the Concealed Door Closer market throughout the world.

The secondary sources of the data typically include

Key Market Segmentation:

On the basis of technology, the artificial intelligence in aviation has been segmented into computer vision, machine learning, context awareness computing and natural language processing. Machine learning is further segmented into deep learning, supervised learning, unsupervised learning, reinforcement learning and semi-supervised learning.

Based on offering, the artificial intelligence in aviation market has been segmented into hardware, software and services. Hardware is further segmented into processors, memory and networks. Software is further segmented into AI solutions and AI platforms. Services are further segmented into deployment and integration and support and maintenance.

On the basis of application, the artificial intelligence in aviation market has been segmented into dynamic pricing, virtual assistants, flight operations, smart maintenance, manufacturing, surveillance, training and other applications. Manufacturing is further segmented into material movement, predictive maintenance and machinery inspection, production planning, quality control and reclamation.

Key Elements that the report acknowledges:

To check the complete Table of Content click here: @ https://www.databridgemarketresearch.com/toc/?dbmr=global-artificial-intelligence-in-aviation-market

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About Data Bridge Market Research, Private Ltd

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Artificial Intelligence in Aviation Market Giants Spending Is Going To Boom Forecast By 2029 - Digital Journal

A report titledArtificial Intelligence (AI) Marketpublished by Maximize Market Research,provides extensive analysis for the forecasted period of 2021 to 2027. After extensive analysis of the business implications of the pandemic and its induced economic crisis, growth in the global Artificial Intelligence (AI) market is projected to grow at a26.1% percent CAGR and to reach USD233.50 Billion by the end of the analysis period.

Artificial intelligence (AI), often known as machine intelligence, is a branch of computer science concerned with the creation and management of technology that can learn to make decisions and conduct transactions on behalf of people. Artificial intelligence algorithms are now being put to the test against intelligence standards that are beyond human comprehensions, such as AI applications in supercomputers and quantum computers. In the next years, such breakthroughs in artificial intelligence technology are expected to encourage the growth of the artificial intelligence industry.

COVID-19 Impact Analysis onArtificial Intelligence (AI) Market:

The outbreak of COVID-19 is likely to give some lucrative opportunities for the global artificial intelligence (AI) industrys growth during the projection period. This is owing to a growth in demand for automation solutions in various SMEs and major firms over time, as well as an increase in digital transformation trends in corporate organizations. Furthermore, businesses are more likely to focus on solutions that might help them improve efficiency and total productivityduring the pandemic. As a result, in the future years, corporations are projected to make major investments in artificial intelligence (AI) technologies.

Artificial Intelligence (AI) MarketRegional Insights:

The United States has a strong innovation ecosystem that is fuelled by strategic federal investments in advanced technology, as well as the presence of global research institutions and innovative scientists and entrepreneurs. All of these elements have boosted artificial intelligence development in the North American area.

Asia Pacific is expected to have a significant artificial intelligence market share during the forecast period owing to the increasing implementation of 5G technology in countries such as China, South Korea, Japan, Singapore, and India.

Artificial Intelligence (AI) MarketDynamics:

Artificial Intelligence will gain considerable relevance in the industrial sector as increasing digitalization occurs. As a result, AI technology is more likely to emerge as a solution for large-scale data processing. Many industry firms are supporting investments to stay ahead of the competition as demand for cutting-edge AI technology continues to rise.

The potential of Artificial Intelligence technology to efficiently assess acquired data and use complex techniques to predict future steps in real-time enhance productivity development; for example, Netflix can recommend movies based ontheir users prior watching experiences.By integrating workflow management tools, trend forecasting, and other innovations, AI has transformed business management in the modern business environment.Increased investment in AI technology and the machine learning sector is primarily driven by such factors.

Growing applications and simple deployment methods have drawn governments attention to AI technology resulting in increased government investments in AI and similar technologies. Government agencies, public sector organizations, and non-governmental organizations (NGOs) have started allocating funds for AI-based pilot initiatives in a variety of areas including road and public safety, traffic management, and government document digitalization.

Request For Free Sample @https://www.maximizemarketresearch.com/request-sample/11207

Artificial Intelligence (AI) MarketSegmentation:

By End User:

BFSIFashion and RetailHealthcare and LifesciencesManufacturingAutomotiveAerospace and DefenseConstructionFood and BeverageOthers

By Component:

HardwareSoftware and Services

Artificial Intelligence (AI) Market Competitors:

IBM CorporationIntel CorporationMicrosoft CorporationGoogle LLCAmazon Web Services Inc.Oracle CorporationSalesforce.com Inc.SAP SESAS Institute Inc.Cisco Systems Inc.Siemens SENVIDIA CorporationZebra Medical Vision Inc.

To Get A Detailed Report Summary And Research Scope of Artificial Intelligence (AI) Market, Click Here @https://www.maximizemarketresearch.com/market-report/artificial-intelligence-ai-market/11207/

About Maximize Market Research:

Maximize market research, a global market research firm with a dedicated team of specialists and data, has carried out extensive research about the Artificial Intelligence (AI) market. Maximize Market Research has a strong unified team of industry specialists and analysts across sectors to ensure the entire Industry ecosystem is taken in perspective, factoring all recent development, latest trends, and futuristic the technologicalimpact of uniquely specific industries. In line with the agreed scope and objective of the study, the companys approach is uniquely custom detailed. Maximize Market Research is positioned to estimate and forecast the market size with the competitive landscape of the industries.

Contact Maximize Market Research:

3rd Floor, Navale IT Park, Phase 2

Pune Banglore Highway, Narhe,

Pune, Maharashtra 411041, India

[emailprotected]

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Global Artificial Intelligence (AI) Market Is Estimated To Reach USD 233.50 Billion In 2027 At A CAGR Of 26.1% Percent By Forecast 2027 - Digital...

The Use of Artificial Intelligence as a Strategy to Analyse Urban Informality

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Within the Latin American and Caribbean region, it has been recorded that at least 25% of the population lives in informal settlements.Given that their expansion is one of the major problems afflicting these cities, a project is presented, supported by the IDB, which proposes how new technologies are capable of contributing to the identification and detection of these areas in order to intervene in them and help reduce urban informality.

Informal settlements, also known as slums, shantytowns, camps or favelas, depending on the country in question, are uncontrolled settlements on land where, in many cases, the conditions for a dignified life are not in place. Through self-built dwellings, these sites are generally the result of the continuous growth of the housing deficit.

For decades, the possibility of collecting information about the Earth's surface through satellite imagery has been contributing to the analysis and production of increasingly accurate and useful maps for urban planning. In this way, not only the growth of cities can be seen, but also the speed at which they are growing and the characteristics of their buildings.

Advances in artificial intelligence facilitate the processing of a large amount of information.When a satellite or aerial image is taken of a neighbourhood where a municipal team has previously demarcated informal areas, the image is processed by an algorithm that will identify the characteristic visual patterns of the area observed from space.The algorithm will then identify other areas with similar characteristics in other images, automatically recognising the districts where informality predominates.It is worth noting that while satellites are able to report both where and how informal settlements are growing, specialised equipment and processing infrastructure are also required.

This particular project brings to the table the role of artificial intelligence in detecting and acting on informal settlements in Colombia where, during 2018, the population exceeded 48 million inhabitants, with three out of four people residing in cities. In fact, it is estimated that by 2050 it will increase by 28% with an urban population in equal or greater proportion. Thus, there is a real need to build new urban homes.

The Government of Colombia appointed the National Planning Department (DNP) to support the Ministry of Housing in defining new methodologies to address the problem of informal housing. In 2021, the DNP was supported by the Housing and Urban Development Division of the IDB and the company GIM and carried out a pilot project using artificial intelligence to obtain detailed information on Colombian informal housing. The Mayor's Office of Barranquilla provided the data for the project.

In practice, it was demonstrated that there was a coincidence of about 85% between the areas delimited by the algorithm maps and those produced by the local specialists, which was sufficient to recognise and prioritise those areas in need of intervention.

The idea is to be able to use this system in other regions. The IDB seek to promote this technology used in Barranquilla to all of Latin America and the Caribbean through a software package called AMISAI (Automated Mapping of Informal Settlements with Artificial Intelligence), which is part of the Open Urban Planning Toolbox, a catalogue of digital tools used for open-source urban planning.

Source:- Luz Adriana Moreno Gonzlez, Vronique de Laet, Hctor Antonio Vzquez Brust, Patricio Zambrano Barragn, Can Artificial Intelligence Help Reduce Urban Informality?

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The Use of Artificial Intelligence as a Strategy to Analyse Urban Informality - ArchDaily

DUBLIN, March 14, 2022 /PRNewswire/ -- The "The State of Artificial Intelligence in Contact Centers" report has been added to ResearchAndMarkets.com's offering.

This Report provides a maturity model for the service experience in contact centers, looking ahead to the next 15 years.

It describes how AI can and should be used, application by application, to enhance contact center performance and provides recommendations and best practices for implementing AI-enabled solutions. It offers both a strategic perspective and tactical guidance to help companies realize the maximum benefits from their AI initiatives.

Artificial intelligence is being added to all of the systems and applications used by contact center agents. It has already introduced a basic form of human-like understanding and intelligence into self-service solutions and is on its way to delivering practical and quantifiable improvements to many other applications.

The State of Artificial Intelligence in the Contact Center analyzes how artificial intelligence (AI) can be applied to transform the customer experience (CX), drive a new era in servicing, and significantly improve the performance of contact centers. It explains AI, its underlying technologies and how it enhances contact center systems and applications.

The Report provides use cases for AI today and anticipates the exciting future of this technology, also analyzing the value proposition and payback for its adoption in each application.

Report Includes

Key Topics Covered:

1. Executive Summary

2. Introduction

3. Contact Center AI Defined and Explained3.1 Rules vs. AI3.2 Where Automation Fits in the World of AI3.3 Data is a Key to the Success of AI Initiatives

4. The Role of AI in Enhancing the CX

5. The Vision for AI in Contact Centers5.1 Operational Impact of the AI Hub in Contact Centers

6. Contact Center AI-Enabled Applications6.1 Contact Center Portfolio of AI-Enabled Systems and Applications6.2 AI-Enabled Systems and Applications for Contact Centers6.2.1 Intelligent Virtual Agent/Conversational AI6.2.2 Interaction (Speech and Text) Analytics6.2.3 Analytics-Enabled Quality Management6.2.4 Virtual Assistant6.3 Targeted AI Systems and Applications for Contact Centers6.3.1 Transcription6.3.2 Real-Time Guidance6.3.3 Predictive Behavioral Routing6.3.4 Predictive Analytics6.4 Emerging AI Systems and Applications for Contact Centers6.4.1 Workforce Management6.4.2 Customer Journey Analytics6.4.3 Customer Relationship Management6.4.4 Contact Center Performance Management6.4.5 Automatic Call Distributor6.4.6 Dialer/Campaign Management6.5 Contributing AI Systems and Applications for Contact Centers6.5.1 Robotic Process Automation6.5.2 Intelligent Hiring6.5.3 Desktop Analytics6.5.4 Knowledge Management6.5.5 Voice Biometrics6.5.6 Voice-of-the-Customer/Surveying

7. The Contact Center AI Journey7.1 The Contact Center Maturity Model7.1.1 Reactive Contact Centers, 20217.1.2 Responsive Contact Centers, 2022 - 20257.1.3 Real-Time Contact Centers, 2026 - 20307.1.4 Proactive Contact Centers, 2031 - 20357.1.5 Predictive Contact Centers, 20367.2 Role and Contributions of AI in Contact Centers

8. Final Thoughts

For more information about this report visit https://www.researchandmarkets.com/r/pt0g92

Media Contact:

Research and Markets Laura Wood, Senior Manager [emailprotected]

For E.S.T Office Hours Call +1-917-300-0470 For U.S./CAN Toll Free Call +1-800-526-8630 For GMT Office Hours Call +353-1-416-8900

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Global Artificial Intelligence in Contact Centers Market Report 2022-2036: Use Cases for AI Today and the Exciting Future of this Technology - PR...

How can government agencies avoid causing more harm than good when they use artificial intelligence and machine learning? A new report attempts to answer this question with a framework and best practices to follow for agencies pursuing algorithm-based tools.

The report comes from the Pittsburgh Task Force on Public Algorithms. The task force studied municipal and county governments use of AI, machine learning and other algorithm-based systems that make or assist with decisions impacting residents opportunities, access, liberties, rights and/or safety.

Local governments have adopted automated systems to support everything from traffic signal changes to child abuse and neglect investigations. Government use of such tools is likely to grow as the technologies mature and agencies become more familiar with them, predicts the task force.

This status quo leaves little room for public or third-party oversight, and residents often have little information on these tools, who designed them or whom to contact with complaints.

The goal isnt to quash tech adoption, just to make it responsible, said David Hickton, task force member and founding director of the University of Pittsburgh Institute for Cyber Law, Policy and Security.

The task force included members of academia, community organizations and civil rights groups, and received advice from local officials.

We hope that these recommendations, if implemented, will offer transparency into government algorithmic systems, facilitate public participation in the development of such systems, empower outside scrutiny of agency systems, and create an environment where appropriate systems can responsibly flourish, the report states.

While automated systems often intend to reduce human error and bias, algorithms make mistakes, too. After all, an algorithm reflects human judgments. Developers choose what factors the algorithms will assess and how heavily each factor is weighted, as well as what data the tool will use to make decisions.

Governments therefore should avoid adopting automated decision-making systems until theyve consulted with residents through multiple channels, not just public comment sessions who would be most impacted.

Residents must understand the tools and the ways theyll be used, believe the proposed approach tackles whatever issue in a productive way, and agree the potential benefits provided by an algorithmic system outweigh the risk of errors, the task force said.

Sufficient transparency allows the public to ensure that a system is making trade-offs consistent with public policy, the report states. A common trade-off is balancing the risk of false positives and false negatives. A programmer may choose to weigh those in a manner different than policymakers or the public might prefer.

Constituents and officials must decide how to balance the risk of an automated system making a mistake. For instance, Philadelphia probation officials have used an algorithm to predict the likelihood of people released on probation becoming reoffenders. These officials have required individuals on probation to receive more or less supervision based on the findings. In this case, accepting more false positives means increasing the chance that people will get inaccurately flagged as higher risk and be subjected to unnecessary intensive supervision, while accepting more false negatives may lead to less oversight for individuals who are likely to reoffend.

For example, an individual may be flagged by a pretrial risk assessment algorithm as unlikely to make their court date. But theres a big difference between officials jailing the person before the court date and officials following up with texted court date reminders and transportation assistance.

Community members told the task force that the safest use of algorithms may be to identify root problems (especially in marginalized communities) and allocate services, training and resources to strengthen community support systems.

Residents also emphasized that issues can be complex and often require decision-makers to consider individual circumstances, even if also using algorithms for help.

Systems should be vetted before adoption and reviewed regularly such as monthly to see if theyre performing well or need updates. Ideally, independent specialists could evaluate sensitive tools and employees training on them, and in-house staff would examine the workings of vendor-provided algorithms.

Contract terms should require vendors to provide details that can help evaluate their algorithms fairness and effectiveness. This step could prevent companies from hiding under claims of trade secrecy.

Local government faces few official limitations around how they can use automated decision-making systems, Hickton said, but residents could put pressure on election officials to make changes. Governments could theoretically appoint officials or boards in charge of overseeing and reviewing algorithms to improve accountability.

I can't predict where this will all go, but I'm hopeful that what we've done is put a spotlight on a problem and that we are giving the public greater access and equity in the discussion and the solutions, he said.

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How Should Local Governments Approach AI and Algorithms? - Government Technology