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The Brookings glossary of AI and emerging technologies – Brookings Institution

Algorithms:

According to author Pedro Domingos, algorithms are a sequence of instructions telling a computer what to do.[1] These software-based coding rules started with simple and routine tasks, but now have advanced into more complex formulations, such as providing driving instructions for autonomous vehicles, identifying possible malignancies in X-rays and CT scans, and assigning students to public schools. Algorithms are widely used in finance, retail, communications, national defense, and many other areas.

Indian engineers Shukla Shubhendu and Jaiswal Vijay define AI as machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment, and intention.[2] This definition emphasizes several qualities that separate AI from mechanical devices or traditional computer software, specifically intentionality, intelligence, and adaptability. AI-based computer systems can learn from data, text, or images and make intentional and intelligent decisions based on that analysis.

Augmented reality puts people in realistic situations that are augmented by computer-generated video, audio, or sensory information. This kind of system allows people to interact with actual and artificial features, be monitored for their reactions, or be trained on the best ways to deal with various stimuli.

Extremely large data sets that are statistically analyzed to gain detailed insights. The data can involve billions of records and require substantial computer-processing power. Data sets are sometimes linked together to see how patterns in one domain affect other areas. Data can be structured into fixed fields or unstructured as free-flowing information. The analysis of big data sets can reveal patterns, trends, or underlying relationships that were not previously apparent to researchers.

Automated tools for answering human questions. Chatbots are being used in retail, finance, government agencies, nonprofits, and other organizations to respond to frequently asked questions or routine inquiries.

Data storage and processing used to take place on personal computers or local servers controlled by individual users. In recent years, however, storage and processing have migrated to digital servers hosted at data centers operated by internet platforms, and people can store information and process data without being in close proximity to the data center. Cloud computing offers convenience, reliability, and the ability to scale applications quickly.

Computers that develop knowledge based on digital pictures or videos.[3] For example, cameras in automated retail outlets that are connected to CV systems can observe what products shoppers picked up, identify the specific items and their prices, and charge consumers credit card or mobile payment system without involving a cash register or sales clerk. CV also is being deployed to analyze satellite images, human faces, and video imagery.

Cars, trucks, and buses that communicate directly with one another and with highway infrastructure. This capacity speeds navigation, raises human safety, and takes advantage of the experiences of other vehicles on the road to improve the driving experience.

The analysis of data to gather substantive insights. Researchers use statistical techniques to find trends or patterns in the data, which give them a better understanding of a range of different topics. Data analytic approaches are used in many businesses and organizations to track day-to-day activities and improve operational efficiency.

Techniques that analyze large amounts of information to gain insights, spot trends, or uncover substantive patterns. These approaches are used to help businesses and organizations improve their processes or identify associations that shed light on relevant questions.

Digital images and audio that are artificially altered or manipulated by AI and/or deep learning to make someone do or say something he or she did not actually do or say. Pictures or videos can be edited to put someone in a compromising position or to have someone make a controversial statement, even though the person did not actually do or say what is shown. Increasingly, it is becoming difficult to distinguish artificially manufactured material from actual videos and images.

A subset of machine learning that relies on neural networks with many layers of neurons. In so doing, deep learning employs statistics to spot underlying trends or data patterns and applies that knowledge to other layers of analysis. Some have labeled this as a way to learn by example and a technique that perform[s] classification tasks directly from images, text, or sound and then applies that knowledge independently.[4] Deep learning requires extensive computing power and labeled data, and is used in medical research, automated vehicles, electronics, and manufacturing, among other areas.

The speed, scope, and timing of technology innovation today is often decided not by government officials but by coders, software designers, and corporate executives. Digital sovereigns set the rules of the road and terms of service for consumers. What they decide, directly or indirectly, has far-reaching consequences for those using their software or platform. The power of business decisionmakers raises important governance questions regarding who should decide on matters affecting society as a whole and the role that policymakers, consumers, and ethicists should play in digital innovation.

Connecting frontline people with others who have differing skills and getting them to work together to solve problems. Distributed collaboration differs from current governance paradigms that emphasize hierarchical, top-down decisionmaking by those who do not always have relevant knowledge about the issues being addressed. The new model takes advantage of the fact that a range of skills are needed to resolve technology issues, and those skills are located in different subject areas and organizational parts. Rather than keeping AI expertise in isolation, distributed collaboration brings together software and product designers, engineers, ethicists, social scientists, and policymakers to draw on their respective expertise and integrate their knowledge to solve pressing problems.

Many technologies can be used in a good or ill manner. The very same facial recognition system could be used to find missing children or provide a means for mass surveillance. It is not the technology per se that raises ethical issues but how the technology is put to use. The dual-use nature of technologies makes regulation difficult because it raises the question of how to gain the benefits of technology innovation while avoiding its detrimental features.

A technology for identifying specific people based on pictures or videos. It operates by analyzing features such as the structure of the face, the distance between the eyes, and the angles between a persons eyes, nose, and mouth. It is controversial because of worries about privacy invasion, malicious applications, or abuse by government or corporate entities. In addition, there have been well-documented biases by race and gender with some facial recognition algorithms.

These are fifth-generation wireless telecommunications networks that have been deployed in major cities and feature faster speeds and enhanced capabilities for transmitting data and images. As such, 5G networks enable new digital products and services, such as video streaming, autonomous vehicles, and automated factories and homes that require a fast broadband.

High-tech military situations in which robots, sensors, AI, and autonomous systems play important roles and command decisions have to unfold at speeds heretofore unseen in warfare. Because of the acceleration of the pace and scope of conflict, countries will have to conduct simultaneous operations in every warfare domain and national leaders will need to accelerate technology innovation to build a safe and stable future.[5]

According to Dorian Pyle and Cristina San Jose of the McKinsey Quarterly, machine learning is based on algorithms that can learn from data without relying on rules-based programming.[6] ML represents a way to classify data, pictures, text, or objects without detailed instruction and to learn in the process so that new pictures or objects can be accurately identified based on that learned information. ML furthermore can be used to estimate continuous variables (such as estimating home sales prices) or to play games. Many of its insights come by examining prior data and learning how to improve understanding.

The analysis of textual information to make sense of its meaning and intentions. NLP software can take a large amount of text and see how words are linked together to assess positive or negative sentiment, relationships, associations, and meaning. For example, researchers can study medical records to see which patient symptoms appear to be most related to particular illnesses.

Researchers use computer software to perform some task by analyzing training examples and by grouping data based on common similarities.[7] Similar to the neural nodes of a brain, neural networks learn in layers and build complex concepts out of simpler ones. They break up tasks, identify objects at a number of different levels, and apply that knowledge to other activities. These kinds of systems allow computers to learn and adapt to changing circumstances, similar to the way a brain functions. Deep learning and many of the most prominent recent applications of machine learning operate through neural networks (e.g., driverless cars, deepfakes, and AlphaGo game playing).

Quantum computers have tremendous capacity for storing and processing information because their storage processes are not in the form of a zero or one, as is the case with traditional computers. Rather, they take advantage of superpositionthe fact that electrons can be in two places at onceto create quantum bits that store multiple values in each point.[8] That capability dramatically increases storage capacity and decreases processing times, thereby improving the scope of data, textual, or image analysis.

Futurist Ray Kurzweil describes a singularity as a machine-based superintelligence [that is] greater than human intelligence.[9] It combines advanced computing power with artificial intelligence, machine learning, and data analytics to create super-powered entities. There are extensive (and unresolved) debates regarding whether humanity will face a computing singularity in the next 50, 100, or 250 years.

The ubiquity of peoples online activities enables technology that tracks behavior and rates people based on their online actions. As an illustration, some organizations have piloted systems that compile data on social media activities, personal infractions, and behaviors such as paying taxes on time. They use that data to rate people for creditworthiness, travel, school enrollment, and government positions.[10] These systems are problematic from an ethical standpoint because they lack transparency and can be used to penalize political opponents.

According to Science magazine, supervised learning is a type of machine learning in which the algorithm compares its outputs with the correct outputs during training. In unsupervised learning, the algorithm merely looks for patterns in a set of data.[11] Supervised learning allows ML and AI to improve information processing and become more accurate.

The backlash against emerging technologies that has developed among many individuals. People worry about a host of problems related to technology innovation, such as privacy invasions, mass surveillance, widening income inequality, and possible job losses. Figuring out how to assuage understandable human fears is a major societal challenge going forward.

Virtual reality uses headsets equipped with projection visors to put people in realistic-seeming situations that are completely generated by computers. People can see, hear, and experience many types of environments and interact with them. By simulating actual settings, VR can train people how to deal with various situations, vary the features that are observed, and monitor how people respond to differing stimuli.

[1] Pedro Domingos, The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World (New York: Basic Books, 2018).

[2] Shukla Shubhendu and Jaiswal Vijay, Applicability of Artificial Intelligence in Different Fields of Life, International Journal of Scientific Engineering and Research, vol. 1, no. 1 (September 2013), pp. 2835.

[3] Jason Brownlee, A Gentle Introduction to Computer Vision, Machine Learning Mastery, July 5, 2019.

[4] Math Works, What Is Deep Learning? undated.

[5] John R. Allen and Amir Husain, Hyperwar and Shifts in Global Power in the AI Century, in Amir Husain and others, Hyperwar: Conflict and Competition in the AI Century (Austin, TX: SparkCognition Press, 2018), p. 15.

[6] Dorian Pyle and Cristina San Jose, An Executives Guide to Machine Learning, McKinsey Quarterly, June, 2015.

[7] Larry Hardesty, Explained: Neural Networks, MIT News, April 14, 2017.

[8] Cade Metz, In Quantum Computing Race, Yale Professors Battle Tech Giants, New York Times, November 14, 2017, p. B3.

[9] Quoted in Tom Wheeler, From Gutenberg to Google: The History of Our Future (Brookings, 2019), p. 226. Also see Ray Kurzweil, The Singularity Is Near: Where Humans Transcend Biology (London: Penguin Books, 2006).

[10] Jack Karsten and Darrell M. West, Chinas Social Credit System Spreads to More Daily Transactions, TechTank (blog), Brookings, June 18, 2018.

[11] Matthew Hutson, AI Glossary: Artificial Intelligence, in So Many Words, Science, July 7, 2017.

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The Brookings glossary of AI and emerging technologies - Brookings Institution

Break into artificial intelligence with this four-course …

Once believed to be strictly the purview of science fiction novels, artificial intelligence (AI) is now everywhere we look. As the driving force behind everything from marketing algorithms and banking platforms to surgical robots and space exploration, AI is playing an increasingly important role in our lives whether we realize it or not. Our reliance on these exciting new technologies is only going to become more pronounced in the coming years.

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New Report from Corinium and FICO Signals Increased Demand for Artificial Intelligence in the Age of COVID-19 – PRNewswire

Today, FICO, a global analytics software firm, released a new report from the market intelligence firm Corinium that found the demand for artificial intelligence (AI), data, and digital tools is soaring as the COVID-19 pandemic continues to put a strain on many enterprises.

Conducted by Corinium and sponsored by FICO, the report - Building AI-Driven Enterprises in a Disrupted Environment - surveyed more than 100 c-level analytic and data executives and conducted in-depth interviews to understand how organizations are developing and deploying AI capabilities. The study found that the uncertainties caused by the pandemic have forced many organizations to adopt a more committed, disciplined approach to becoming an AI-driven enterprise, with more than half (57 percent) of the chief data and analytics officers saying that COVID-19 has increased demand for AI, digital products and tools.

Enterprises are seeking new AI-driven ways to mitigate risks and navigate through uncharted territories in the current economic environment. The report reveals the central role AI has in shaping the future as global markets work through and begin to recover from COVID-19; as well as how to mitigate future risk and disruption going forward.

Some key findings include:

Organizations Rally to Add AI CapacityMost data-driven enterprises are now aggressively investing in their AI capabilities, in fact 63 percent of respondents have started scaling AI capacity within their organization. However, enterprise chief data and chief analytics officers are facing a wide range of challenges as they increasingly look to grow AI. 93 percent say ethical considerations represent a barrier to AI adoption. Other barriers identified include:

Ethical and Responsible AIMore than 93 percent of respondents said that ethical considerations represented a barrier to AI adoption within their organizations. However, as pointed out in the report, "ensuring AI is used responsibly and ethically in business context is a huge, but critical task."

Half of survey respondents said they have strong model governance and management rules in place to support ethical AI usage, making this the most common approach to tackling the challenge. However, more work is needed to ensure ethical AI usage as 67 percent of AI leaders don't monitor their models to ensure their continued accuracy and ethical treatment.

"Being ethical is not being blind to what's in the model," said Dr. Scott Zoldi, chief analytics officer, FICO. "Organizations need to ensure that AI is designed robustly and is explainable, transparent, built ethically and governed by auditable, recorded development process that is referenced as data shifts over time."

When asked which business areas are pushing for greater AI responsibility with an organization, data and analytics leader said:

AI Enables Post-COVID Competitive AdvantageFrom better customer experiences and reducing financial crime to automating business processes and improving risk management, respondents believe AI will help their organizations secure a competitive advantage.

A complete copy of the FICO sponsored report, Building AI-Driven Enterprises in a Disrupted Environment, can be downloaded here.

About FICOFICO (NYSE:FICO) powers decisions that help people and businesses around the world prosper. Founded in 1956 and based in Silicon Valley, the company is a pioneer in the use of predictive analytics and data science to improve operational decisions. FICO holds more than 195 US and foreign patents on technologies that increase profitability, customer satisfaction and growth for businesses in financial services, telecommunications, health care, retail and many other industries. Using FICO solutions, businesses in more than 100 countries do everything from protecting 2.6 billion payment cards from fraud, to helping people get credit, to ensuring that millions of airplanes and rental cars are in the right place at the right time.Learn more athttp://www.fico.com.

Join the conversation athttps://twitter.com/fico&http://www.fico.com/en/blogs/.

For FICO news and media resources, visitwww.fico.com/news.

FICO is a registered trademark of Fair Isaac Corporation inthe United Statesand in other countries.

SOURCE FICO

https://www.fico.com

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New Report from Corinium and FICO Signals Increased Demand for Artificial Intelligence in the Age of COVID-19 - PRNewswire

Burden of COVID-19 on the Market & Rehabilitation Plan | Artificial Intelligence (AI) Market in Manufacturing Industry 2019-2023 | The Increasing…

LONDON--(BUSINESS WIRE)--Technavio has been monitoring the artificial intelligence (AI) market in manufacturing industry and it is poised to grow by USD 7.22 billion during 2019-2023, progressing at a CAGR of about 31% during the forecast period. The report offers an up-to-date analysis regarding the current market scenario, latest trends and drivers, and the overall market environment.

Although the COVID-19 pandemic continues to transform the growth of various industries, the immediate impact of the outbreak is varied. While a few industries will register a drop in demand, numerous others will continue to remain unscathed and show promising growth opportunities. Technavios in-depth research has all your needs covered as our research reports include all foreseeable market scenarios, including pre- & post-COVID-19 analysis. Download a Free Sample Report

The market is fragmented, and the degree of fragmentation will accelerate during the forecast period. Amazon Web Services Inc., FANUC Corp., General Electric Co., Google LLC, H2O.ai Inc., IBM Corp., KUKA Aktiengesellschaft, Microsoft Corp., Rockwell Automation Inc., and SAP SE. are some of the major market participants. To make the most of the opportunities, market vendors should focus more on the growth prospects in the fast-growing segments, while maintaining their positions in the slow-growing segments.

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The increasing use of industrial IoT has been instrumental in driving the growth of the market. However, data privacy and compliance maintenance might hamper market growth.

Technavio's custom research reports offer detailed insights on the impact of COVID-19 at an industry level, a regional level, and subsequent supply chain operations. This customized report will also help clients keep up with new product launches in direct & indirect COVID-19 related markets, upcoming vaccines and pipeline analysis, and significant developments in vendor operations and government regulations. https://www.technavio.com/report/artificial-intelligence-market-in-manufacturing-industry-analysis?tnplus

Artificial Intelligence (AI) Market in Manufacturing Industry 2019-2023: Segmentation

Artificial Intelligence (AI) Market in Manufacturing Industry is segmented as below:

To learn more about the global trends impacting the future of market research, download a free sample: https://www.technavio.com/talk-to-us?report=IRTNTR32119

Artificial Intelligence (AI) Market in Manufacturing Industry 2019-2023: Scope

Technavio presents a detailed picture of the market by the way of study, synthesis, and summation of data from multiple sources. The artificial intelligence (AI) market in manufacturing industry report covers the following areas:

This study identifies the increasing human-robot collaboration as one of the prime reasons driving the artificial intelligence (AI) market growth in manufacturing industry during the next few years.

Technavio suggests three forecast scenarios (optimistic, probable, and pessimistic) considering the impact of COVID-19. Technavios in-depth research has direct and indirect COVID-19 impacted market research reports.

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Artificial Intelligence (AI) Market in Manufacturing Industry 2019-2023: Key Highlights

Table of Contents:

PART 01: EXECUTIVE SUMMARY

PART 02: SCOPE OF THE REPORT

PART 03: MARKET LANDSCAPE

PART 04: MARKET SIZING

PART 05: FIVE FORCES ANALYSIS

PART 06: MARKET SEGMENTATION BY APPLICATION

PART 07: CUSTOMER LANDSCAPE

PART 08: GEOGRAPHIC LANDSCAPE

PART 09: DRIVERS AND CHALLENGES

PART 10: MARKET TRENDS

PART 11: VENDOR LANDSCAPE

PART 12: VENDOR ANALYSIS

PART 13: APPENDIX

PART 14: EXPLORE TECHNAVIO

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Technavio is a leading global technology research and advisory company. Their research and analysis focus on emerging market trends and provides actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions. With over 500 specialized analysts, Technavios report library consists of more than 17,000 reports and counting, covering 800 technologies, spanning across 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavios comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.

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Burden of COVID-19 on the Market & Rehabilitation Plan | Artificial Intelligence (AI) Market in Manufacturing Industry 2019-2023 | The Increasing...

The path to real-world artificial intelligence – TechRepublic

Experts from MIT and IBM held a webinar this week to discuss where AI technologies are today and advances that will help make their usage more practical and widespread.

Image: Sompong Rattanakunchon / Getty Images

Artificial intelligence has made significant strides in recent years, but modern AI techniques remain limited, a panel of MIT professors and the director of the MIT-IBM Watson AI Lab said during a webinar this week.

Neural networks can perform specific, well-defined tasks but they struggle in real-world situations that go beyond pattern recognition and present obstacles like limited data, reliance on self-training, and answering questions like "why" and "how" versus "what," the panel said.

The future of AI depends on enabling AI systems to do something once considered impossible: Learn by demonstrating flexibility, some semblance of reasoning, and/or by transferring knowledge from one set of tasks to another, the group said.

SEE: Robotic process automation: A cheat sheet (free PDF) (TechRepublic)

The panel discussion was moderated by David Schubmehl, a research director at IDC, and it began with a question he posed asking about the current limitations of AI and machine learning.

"The striking success right now in particular, in machine learning, is in problems that require interpretation of signalsimages, speech and language," said panelist Leslie Kaelbling, a computer science and engineering professor at MIT.

For years, people have tried to solve problems like detecting faces and images and directly engineering solutions that didn't work, she said.

We have become good at engineering algorithms that take data and use that to derive a solution, she said. "That's been an amazing success." But it takes a lot of data and a lot of computation so for some problems formulations aren't available yet that would let us learn from the amount of data available, Kaelbling said.

SEE:9 super-smart problem solvers take on bias in AI, microplastics, and language lessons for chatbots(TechRepublic)

One of her areas of focus is in robotics, and it's harder to get training examples there because robots are expensive and parts break, "so we really have to be able to learn from smaller amounts of data," Kaelbling said.

Neural networks and deep learning are the "latest and greatest way to frame those sorts of problems and the successes are many," added Josh Tenenbaum, a professor of cognitive science and computation at MIT.

But when talking about general intelligence and how to get machines to understand the world there is still a huge gap, he said.

"But on the research side really exciting things are starting to happen to try to capture some steps to more general forms of intelligence [in] machines," he said. In his work, "we're seeing ways in which we can draw insights from how humans understand the world and taking small steps to put them in machines."

Although people think of AI as being synonymous with automation, it is incredibly labor intensive in a way that doesn't work for most of the problems we want to solve, noted David Cox, IBM director of the MIT-IBM Watson AI Lab.

Echoing Kaelbling, Cox said that leveraging tools today like deep learning requires huge amounts of "carefully curated, bias-balanced data," to be able to use them well. Additionally, for most problems we are trying to solve, we don't have those "giant rivers of data" to build a dam in front of to extract some value from that river, Cox said.

Today, companies are more focused on solving some type of one-off problem and even when they have big data, it's rarely curated, he said. "So most of the problems we love to solve with AIwe don't have the right tools for that."

That's because we have problems with bias and interpretability with humans using these tools and they have to understand why they are making these decisions, Cox said. "They're all barriers."

However, he said, there's enormous opportunity looking at all these different fields to chart a path forward.

That includes using deep learning, which is good for pattern recognition, to help solve difficult search problems, Tenenbaum said.To develop intelligent agents, scientists need to use all the available tools, said Kaelbling. For example, neural networks are needed for perception as well as higher level and more abstract types of reasoning to decide, for example, what to make for dinner or to decide how to disperse supplies.

"The critical thing technologically is to realize the sweet spot for each piece and figure out what it is good at and not good at. Scientists need to understand the role each piece plays," she said.

The MIT and IBM AI experts also discussed a new foundational method known as neurosymbolic AI, which is the ability to combine statistical, data-driven learning of neural networks with the powerful knowledge representation and reasoning of symbolic approaches.

Moderator Schubmehl commented that having a combination of neurosymbolic AI and deep learning "might really be the holy grail" for advancing real-world AI.

Kaelbling agreed, adding that it may be not just those two techniques but include others as well.

One of the themes that emerged from the webinar is that there is a very helpful confluence of all types of AI that are now being used, said Cox. The next evolution of very practical AI is going to be understanding the science of finding things and building a system we can reason with and grow and learn from, and determine what is going to happen. "That will be when AI hits its stride," he said.

Learn the latest news and best practices about data science, big data analytics, and artificial intelligence. Delivered Mondays

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The path to real-world artificial intelligence - TechRepublic

Where it Counts, U.S. Leads in Artificial Intelligence – Department of Defense

When it comes to advancements in artificial intelligence technology, China does have a lead in some places like spying on its own people and using facial recognition technology to identify political dissenters. But those are areas where the U.S. simply isn't pointing its investments in artificial intelligence, said director of the Joint Artificial Intelligence Center. Where it counts, the U.S. leads, he said.

"While it is true that the United States faces formidable technological competitors and challenging strategic environments, the reality is that the United States continues to lead in AI and its most important military applications," said Nand Mulchandani, during a briefing at the Pentagon.

The Joint Artificial Intelligence Center, which stood up in 2018, serves as the official focal point of the department's AI strategy.

China leads in some places, Mulchandani said. "China's military and police authorities undeniably have the world's most advanced capabilities, such as unregulated facial recognition for universal surveillance and control of their domestic population, trained on Chinese video gathered from their systems, and Chinese language text analysis for internet and media censorship."

The U.S. is capable of doing similar things, he said, but doesn't. It's against the law, and it's not in line with American values.

"Our constitution and privacy laws protect the rights of U.S. citizens, and how their data is collected and used," he said. "Therefore, we simply don't invest in building such universal surveillance and censorship systems."

The department does invest in systems that both enhance warfighter capability, for instance, and also help the military protect and serve the United States, including during the COVID-19 pandemic.

The Project Salus effort, for instance, which began in March of this year, puts artificial intelligence to work helping to predict shortages for things like water, medicine and supplies used in the COVID fight, said Mulchandani.

"This product was developed in direct work with [U.S. Northern Command] and the National Guard," he said. "They have obviously a very unique role to play in ensuring that resource shortages ... are harmonized across an area that's dealing with the disaster."

Mulchandani said what the Guard didn't have was predictive analytics on where such shortages might occur, or real-time analytics for supply and demand. Project Salus named for the Roman goddess of safety and well-being fills that role.

"We [now have] roughly about 40 to 50 different data streams coming into project Salus at the data platform layer," he said. "We have another 40 to 45 different AI models that are all running on top of the platform that allow for ... the Northcom operations team ... to actually get predictive analytics on where shortages and things will occur."

As an AI-enabled tool, he said, Project Salus can be used to predict traffic bottlenecks, hotel vacancies and the best military bases to stockpile food during the fallout from a damaging weather event.

As the department pursues joint all-domain command and control, or JADC2, the JAIC is working to build in the needed AI capabilities, Mulchandani.

"JADC2 is ... a collection of platforms that get stitched together and woven together[ effectively into] a platform," Mulchandani said. "The JAIC is spending a lot of time and resources focused on building the AI components on top of JADC2. So if you can imagine a command and control system that is current and the way it's configured today, our job and role is to actually build out the AI components both from a data, AI modeling and then training perspective and then deploying those."

When it comes to AI and weapons, Mulchandani said the department and JAIC are involved there too.

"We do have projects going on under joint warfighting, which are actually going into testing," he said. "They're very tactical-edge AI, is the way I describe it. And that work is going to be tested. It's very promising work. We're very excited about it."

While Mulchandani didn't mention specific projects, he did say that while much of the JAIC's AI work will go into weapons systems, none of those right now are going to be autonomous weapons systems. The concepts of a human-in-the-loop and full human control of weapons, he said, "are still absolutely valid."

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Where it Counts, U.S. Leads in Artificial Intelligence - Department of Defense

The journey that organizations should embark on to realize the true potential of AI – The Indian Express

New Delhi | Updated: July 13, 2020 4:33:10 pm

Implementing Artificial Intelligence (AI) in an organization is a complex undertaking as it involves bringing together multiple stakeholders and different capabilities. Many companies make the mistake of treating AI as a pure play technology implementation project and hence end up encountering many challenges and complexities peculiar to AI. There are three big reasons for increased complexity in an AI program implementation (1) AI is a portfolio based technology (example, comprising sub-categories such as Natural Language Processing (NLP), Natural Language Generation (NLG), Machine Learning) as compared to many standalone technology solutions (2) These sub-category technologies (example, NLP) in turn have many different products and tool vendors with their own unique strengths and maturity cycles (3) These sub-category technologies (example, NLG) are specialists in their functionality and can solve certain specific problems only (example, NLG technology helps create written texts similar to how a human would create it). Hence, organizations need to do three important things Define Ambitious and Achievable Success Criteria, Develop the Right Operating Rhythm, and Create and Celebrate Success Stories to realize the true potential of AI.

Most companies have very narrow or ambiguous success criteria definition of their AI program. These success criteria are not defined holistically and hence may end up providing sub-optimal benefits to the organization. We suggest that the success criteria of an AI program need to not only be ambitious, achievable, and actionable but also tightly integrated with the overall key strategic objectives and priorities of the organization. For example, a bank which is trying to reduce the number of customer complaints and improve the customer experience as key strategic goals can benefit immensely from integrating AI program goals with the goals of this important program (example, leverage machine learning and analytics to analyze past complaints data and better understand customer complaints patterns and journeys and decision points). This interlocking of success criteria will help AI program leaders with the right yardsticks to align and measure their progress and contribution. Additionally, it also helps them get the right visibility and sponsorship at the senior leadership levels in the organization that further improves the chances of success of the AI program.

Also Read: Can Humans and AI coexist to create a hyper-productive HumBot organisation?

A successful AI program requires four key ingredients Right Data, Diverse Skills, Scalable Infrastructure and Seamless Stakeholder Alignment. It is said that Data is the food of an AI program and hence having the right data (example, the volume of data, type of data, and quality of data) at the right time is critical to ensure AI programs have the required fuel and energy to complete their intended journey. While good AI skills are in short supply, leveraging constructs such as having a Nimble CoE (Centers of Expertise) increases chances of optimal utilization of these rare and expensive skills across the organization. Finally, getting various important stakeholders (example, Global Process Owners, IT Leaders, Internal Control & Risks, Continuous Improvement, and HR) seamlessly work together is important to reduce friction and increase AI program velocity.

Also Read:With the power of AI, India can reimagine delivery of public services

It is said that success breeds more success. While AI programs typically focus a lot on efficiency and productivity improvements, many AI programs also generate significant non-direct-quantifiable benefits (for example, improvement in stakeholder experience, improvement in employee engagement and morale). A recent Deloitte survey indicates that 44 per cent organizations felt AI has increased the quality of their products/services while 35 per cent organizations found that AI has enabled them to make better decisions in their organizations. Successful companies find a way to identify these simple, holistic stories and narrate them compellingly and consistently in multiple forums at all levels in their organizations. Humans, by design, are inspired better by stories (than by just numbers) and hence creating a powerful story that combines the quantifiable (example, number of hours saved) with other benefits (example, better decision making ability) can galvanize the entire organization and facilitate rapid and increased adoption of AI at all levels and in all units of the organization.

The revered Chinese saint, Lao Tzu, once famously remarked that A journey of thousand miles begins with a single step. The AI journey in an organization is no exception. While AI implementations are typically more complex and nuanced, companies can leverage the 3-pronged approach mentioned above to realize the true and full potential of AI. While a successful AI program implementation can bestow significant financial benefits on an organization, it also activates the divine journey of freeing up humans to do what they do best leverage their sophisticated brains to introspect, explore, learn, love, empathize and solve the most intricate and defining problems of our generations.

The authors are Ravi Mehta, Partner; Sushant Kumaraswamy, Director; Sudhi. H, Associate Director; and Prashant Kumar, Senior Consultant, Deloitte India.

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The journey that organizations should embark on to realize the true potential of AI - The Indian Express

Commentary: Artificial intelligence and automation would actually benefit Singapore – CNA

SINGAPORE: Now that the General Election is over, it is time for Singapore to refocus on the big challenge of creating jobs to tide citizens over a pandemic and double down on digitalisation for the long term.

Much has been said about the concerns people have about livelihoods, with suggestions to safeguard and improve the prospects of jobs for Singaporeans.

Yet disruption is not new to Singapore. History has witnessed how Singapore has upskilled its workforce through computerisation and automation in the 1980s.

Singapore businesses and workers are no strangers to the need to adapt to new technological changes.

Now, Digital Ambassador Corps have been deployed to help small businesses and senior citizens learn and apply technology.

With every change comes hesitance, even resistance. In the push for a Smart Nation, this resistance may come from a fear of the unknown. Reports of artificial Intelligence (AI) and digital technologies cannibalising jobs do not help either.

However, Singapore is in a unique situation. With a small and ageing workforce, Singapore has to tap on AI and automation to preserve its competitive advantage over other economies.

A COUNTRY INCREASINGLY POWERED BY MORE ARTIFICIAL INTELLIGENCE

Digital technologies and AI (including machine learning, computer vision and natural language processing) can boost efficiencies, performance and productivity in various ways.

It is these advanced technologies that help e-commerce retailers like Lazada sell more by analysing massive amount of data, learning customer preferences and providing targeted products to be displayed online for the customers.

In engineering and aviation, AI has been used to increase the performance of gas turbine engines, such as finding an optimal way to increase thrust and decrease fuel consumptions.

In the long term, the savings on fuel could be passed to the passengers. Such performance improvements cannot usually be attained using traditional models.

In logistics in Singapore and around the world, AI has also been utilised to predict traffic patterns and route conditions. For companies like Grab, the use of AI has enabled drivers to complete as many jobs as possible in the shortest amount of time.

Grab also uses natural language processing methods to address customer feedback and enable users to find the services they need with greater ease.

AI is also extensively used in the development of autonomous vehicles like the National University of Singapore (NUS) autonomous shuttle at its Kent Ridge campus.

In healthcare, AI has been employed to optimise hospital management and processes like managing a large number of patient beds in the case of Tan Tock Seng Hospital. Predictive analytics can help optimise hospital bed assignment decisions by predicting when patients will be discharged to make more beds available.

AI will be an integral part of Singapores healthcare system to help doctors make better decisions and design early intervention programmes and improved care pathways for patients using predictive modelling.

One application of machine learning is precision medicine where AI can help predict what treatment protocols are most feasible and with higher success rate on a patient based on various patient characteristics and the treatment context.

Another example is robotic surgery (like the da Vinci Surgical System used in Gleneagles Hospital Singapore) which can help surgeons improve their ability to perform precise and minimally invasive incisions and surgeries. Important decisions are still made by human surgeons.

In educational applications and tools, AI has helped the development of skills and testing systems and allows the adjustment of learning based on differentiating students needs in Institutes of Higher Learning in Singapore.

Students can thus enjoy more customised testing and learning tailored to the specific needs and ability level of each student.

JOBS ARE CHANGING

In areas where AI and digital technologies improve businesses significantly, the nature of jobs has changed.

Certain jobs like routine clerical work may be reduced while the employment rates for professionals and those in the service sectors have increased. Understanding what tasks AI is suited or not suited for will be a business priority for firms. Singapores learning, retraining and upskilling efforts must take full advantage of the AI era.

Prior research has shown AI is suited to perform tasks that provide clear feedback with definable goals and metrics. AI is also efficient at recognising associations based on empirical and statistical data.

AI can help improve traffic volume and flow in metropolitan areas like Singapore, New York or London using pre-defined performance and congestion measures at the system level by analysing large amounts of traffic data.

On the other hand, AI is not so good at unstructured tasks and reasoning, especially based on background information that is previously unknown to the computer.

This is why AI (or machine learning) can be used to spot irregular heartbeat from scans and detect diseases from medical imaging, but it cannot explain as well as doctors how and why one is diagnosed with a certain disease.

In other words, the interpretation of the causes and severity of these diseases and their linkages to other diseases are much more difficult for AI to ascertain. AI also does not perform well when the tasks to be learned change quickly.

Humans do much better at interpreting data and drawing inferences even when the tasks evolve over time.

YOUR JOB MAY REMAIN BUT SOME TASKS COULD BE OUTSOURCED TO AI

In light of the above understanding, how we should we adjust, retrain or upskill the valuable human resource we have in Singapore to prepare for the new paradigm involving AI and digital technologies?

We understand that most jobs have many interrelated tasks. People say the jobs AI could likely replace include telemarketing, receptionists, computer support specialists (think chatbots used by banks like OCBC) and market research analysts.

However, it doesnt mean these jobs will disappear entirely. AI is weak on relatively unstructured, creative tasks and those involving emotional intelligence.

The focus of the training or upskilling of such roles should be on these areas. Upskilling courses can cover developing strategies in branding, designing and marketing.

Use AI to gather your data, but use humans to develop business and innovation strategies and design marketing campaigns based on understanding those data.

People and leadership skills will continue to be important, yet another area that AI currently does not fill the void. The expertise in asking interesting questions and looking for new and innovative solutions, which is required in researchers or entrepreneurs, will also be deemed more valuable.

The age of AI and digital technologies is already here. It is clear they can and probably should be applied to different industries and have the potential to significantly improve productivity.

In the process, they will transform our work and lives. While some jobs may be replaced, many other job and career opportunities will be created.

Singapore has the infrastructure, talents and resources to take advantage of the benefits brought about by the AI revolution.

With national emphasis on innovation and Industry 4.0, as well as additional resources and upskilling opportunities, this could yet be another pivotal point for Singapore to create and deliver value in a competitive global arena.

DrKenneth GHuang is an Associate Professor with the Department of Strategy & Policy at National University of Singapore (NUS) Business School and the Department of Industrial Systems Engineering & Management at NUS. The opinions expressed are those of the writer and do not represent the views and opinions of NUS.

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Commentary: Artificial intelligence and automation would actually benefit Singapore - CNA

Healthcare Artificial Intelligence Market Analysis Of Global Trends, Demand And Competition 2020-2028 – Cole of Duty

Trusted Business Insights answers what are the scenarios for growth and recovery and whether there will be any lasting structural impact from the unfolding crisis for the Healthcare Artificial Intelligence market.

Trusted Business Insights presents an updated and Latest Study on Healthcare Artificial Intelligence Market 2019-2026. The report contains market predictions related to market size, revenue, production, CAGR, Consumption, gross margin, price, and other substantial factors. While emphasizing the key driving and restraining forces for this market, the report also offers a complete study of the future trends and developments of the market.The report further elaborates on the micro and macroeconomic aspects including the socio-political landscape that is anticipated to shape the demand of the Healthcare Artificial Intelligence market during the forecast period (2019-2029).It also examines the role of the leading market players involved in the industry including their corporate overview, financial summary, and SWOT analysis.

Get Sample Copy of this Report @ Healthcare Artificial Intelligence Market Research Report Forecast to 2029 (Includes Business Impact of COVID-19)

Abstract, Snapshot, Market Analysis & Market Definition: Healthcare Artificial Intelligence MarketIndustry / Sector Trends

Healthcare Artificial Intelligence Market size was valued at USD 1.3 billion in 2018 and is expected to witness 41.7% CAGR from 2019 to 2025.

U.S. Healthcare Artificial Intelligence Market Size, By Application, 2018 & 2025 (USD Million)

Growing application of artificial intelligence in the field of drug discovery, medical imaging industry, precision medicine and genomics coupled with increasing personalized treatments customized to an individual patients requirement will drive the global market. Rising demand for artificial intelligence technology to perform data mining and accelerate the speed of healthcare delivery services, emergence of novel and promising applications for disease diagnosis and monitoring will further augment the market growth in forthcoming years.

Advancements in data analytics will surge the healthcare artificial intelligence market growth during the analysis period. Huge amount of data is generated every year in healthcare industry and ever-increasing volume of big data has generated the need to adopt artificial intelligence technology to manage data efficiently. Artificial intelligence has revolutionized the field of healthcare by designing treatment plans, assisting in repetitive tasks, medication management, and drug discovery. It can also be effectively used for healthcare data management by collecting, storing, and normalizing the data. Recently, the artificial intelligence research division of the Google, launched its Google Deepmind Health project, for data mining of medical records to provide faster and better health services. Development of technologically upgraded data software and solutions will foster the industry growth. However, high capital requirement may create affordability issues and hamper the industry growth.

Market Segmentation, Outlook & Regional Insights: Healthcare Artificial Intelligence Market

Healthcare Artificial Intelligence Market, By Application

Drug discovery segment accounted for USD 345.0 million in 2018 and is anticipated to have significant growth during the forecast timeframe. Drug discovery is one of the recent applications of artificial intelligence that has transformed drug discovery process and can be used to cut the cost of production for new drug development. Astra Zeneca recently entered into collaboration with Berg, a Boston based specialist in artificial intelligence for drug discovery. Such ongoing initiatives from industry players are bound to have positive impact on industry growth.

Hospital workflow segment held considerable revenue share in 2018 and is anticipated to witness 40.6% CAGR during the forecast period. Increasing adoption of artificial intelligence technology for collection of data of patient to support decision making in hospital workflow has significantly improved outcomes, reduced wait times and costs that will enhance segmental growth in forthcoming years.

Germany Healthcare Artificial Intelligence Market Size, By Application, 2018 (USD Million)

Healthcare Artificial Intelligence Market, By Region

North America healthcare artificial intelligence market dominated the global market with USD 653.9 million in 2018 and is anticipated to show similar trend over the forthcoming years. High regional growth is attributed to massive adoption of HCIT solutions and increasing focus on population health management. Moreover, various government initiatives and funding in North America are focusing on encouraging the growth of healthcare artificial intelligence market.

Asia Pacific healthcare artificial intelligence market will witness lucrative growth of 44.4% over the forecast period due to rising R&D expenditure, developments in pharmaceutical and biotechnology sectors. Additionally, presence of large patient pool will trigger demand for better healthcare services, developing healthcare infrastructure and rising disposable income will further support Asia Pacific healthcare artificial intelligence market growth.

Asia Pacific Healthcare Artificial Intelligence Market Size, By Country, 2025 (USD Million)

Key Players, Recent Developments & Sector Viewpoints: Healthcare Artificial Intelligence Market

Some of the key industry players operational in the healthcare artificial intelligence market include AiCure, APIXIO, Inc., Atomwise, Inc., Butterfly Network, Inc., Cyrcadia Health Inc., Enlitic, Inc., IBM (Watson Health), iCarbonX, Insilico Medicine, Inc., Lifegraph, Modernizing Medicine, Pathway Genomics Corporation, Sense.ly, Sophia Genetics, Welltok and Zebra Medical Vision Ltd. Leading players operational in healthcare artificial intelligence industry adopt several strategic initiatives such as mergers, partnerships, collaborations, acquisitions, new product launch and geographical expansions that will allow the companies to sustain market position. For instance, in March 2017, IBM announced global strategic partnership with Salesforce for delivering joint solutions to the companies to make smarter decisions using artificial intelligence. Partnerships with strong leaders will foster companys growth.

Healthcare Artificial Intelligence Industry Viewpoint

Over the past few decades with technology upgradation, companies such as IBM Watson have introduced software and solutions that have seamless applications in healthcare industry. Since the introduction of healthcare artificial intelligence, industry has experienced numerous growth opportunities. Several industry players initiated the development of data analytic software for handling and processing large amounts of patient data generated.

Artificial intelligence was initiated in 1956 and started gaining significant importance in medical field since 1972. The programs and solutions introduced, facilitated the process of drug discovery. Currently, the players have started addressing the identified gaps in healthcare services and have developed applications and solutions that are aimed to enhance productivity at hospitals and clinics by providing exceptional operational ease. Also, efforts are been made to introduce artificial intelligence based surgical robots that will help in reducing surgical complications. As this industry is still in initial phases of growth, key industry players will leverage advanced technology to conceptualize and market highly upgraded and reliable software that will probably replace the conventional systems utilized earlier proving beneficial for the industry growth

Key Insights Covered: Exhaustive Healthcare Artificial Intelligence Market1. Market size (sales, revenue and growth rate) of Healthcare Artificial Intelligence industry.2. Global major manufacturers operating situation (sales, revenue, growth rate and gross margin) of Healthcare Artificial Intelligence industry.3. SWOT analysis, New Project Investment Feasibility Analysis, Upstream raw materials and manufacturing equipment & Industry chain analysis of Healthcare Artificial Intelligence industry.4. Market size (sales, revenue) forecast by regions and countries from 2019 to 2025 of Healthcare Artificial Intelligence industry.

Research Methodology: Healthcare Artificial Intelligence Market

Quick Read Table of Contents of this Report @ Healthcare Artificial Intelligence Market Research Report Forecast to 2029 (Includes Business Impact of COVID-19)

Trusted Business InsightsShelly ArnoldMedia & Marketing ExecutiveEmail Me For Any ClarificationsConnect on LinkedInClick to follow Trusted Business Insights LinkedIn for Market Data and Updates.US: +1 646 568 9797UK: +44 330 808 0580

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Healthcare Artificial Intelligence Market Analysis Of Global Trends, Demand And Competition 2020-2028 - Cole of Duty

How Artificial Intelligence Innovations Induced Industrywide Advancements? – Analytics Insight

Living in a world surrounded by technologies, we can conveniently verify that Artificial Intelligence is behind most of the innovations that take place today.It has become one of the innovation resources of the current era. From finances to healthcare, technology has its reach everywhere, transforming every industry for better. Although every technology in the mainstream today is evolving at a fast pace, in comparison to other fields, AI has shown some great and remarkable advancements in the past year. Lets have a look back into 2019 and see how the technology has introduced some of the most innovative breakthroughs of this decade.

In the monetary arena, the innovations empowered by AI and ML have taken a significant leap. As per the observation presented by a Deloitte report, AI leaders In financial services: Common traits of frontrunners in the artificial intelligence race, various AI platforms and tools are revolutionizing several aspects of the BFSI sector. In particular, machine learning tools are driving better customer engagement while helping authorities manage portfolios and plan future services and scenarios. Through ML implementation, financial institutions are also able to own long-term strategies and enforce operational improvements. As illustrated by the report, organizations that are dubbed frontrunners are observing revenue growth of 19% company-wide. This significant implementation of AI subset to achieve better business can be attributed as an AI innovation.

Transforming the agriculture sector, an Australia-based agtech company FluroSat served farmers with real-time information. Using this information, farmers were able to assess plant health and detect crop stress. FluroSats platform, FluroSense, provides a mix of satellite data, farming records, and AI in an analytics engine that helps farmworkers foresee crop performance. Also, using the received data, the AI tool can provide recommendations on how to optimize crops. Since its launch in 2019, FluroSense is being employed by more than 1000 agronomists across eight countries.

Moreover, across the healthcare and pharmaceutical industry, technology is making great advancements. Living under the reign of terror induced by a coronavirus, no other generation than us can understand the true benefits of technology in reshaping the healthcare industry. Today AI and its subsets are being used extensively for drug discovery and medical treatment planning. However, the upsurge of AI in this industry has been noted in 2019 when a Hong Kong biotech startup called InSilico Medicine partnered with the University of Toronto researchers to create a drug in order to advance the concept to initial testing.As noted byFortune, the significance of AI on pre-clinical development and on the economics of healthcare is worth watching. Whats eye-popping here is the timescale: just 46 days from molecular design to animal testing in mice. Considering that, on average, it takes more than a decade and costs US$350 million to US$2.7 billion to bring a new drug to market, depending on which study one believes, the potential impact on the pharmaceutical industry is huge.

In an effort to transform recruitment processes and HR operations, Harver developed an AI software that automates the hiring process making use of data to sort, test, and vet candidates. The software is extremely helpful in simplifying the hiring process with decreased chances of unconscious bias in the process. As noted by Springwise, Harver is different from other AI-based hiring programs because it focuses on the pre-interview selection process. The companys software offers HR teams several adaptable assessment modules. The tests can examine everything from personality to typing skills. The program then assesses the results and determines which candidates are the best fit for an interview.

In its creative approaches, the innovations of AI are quite remarkable. One of the most prestigious publications theNew Yorkerrecently agitated a discussion if AI possesses the capability to write for the publication. The article mentions the recently-releasedGPT-2by Open.AI. GPT-2 is an AI platform that builds on deep training of a vast neural net in order to establish fairly realistic language abilities. According toOpen.AI, Weve trained a large-scale unsupervised language model which generates coherent paragraphs of text, achieves state-of-the-art performance on many language modeling benchmarks, and performs rudimentary reading comprehension, machine translation, question answering, and summarizationall without task-specific training. Moreover, some kindred mechanism is being employed to create basic articles, and sports reports.

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How Artificial Intelligence Innovations Induced Industrywide Advancements? - Analytics Insight

Break into artificial intelligence with this four-course online training for $35 – ZDNet

Once believed to be strictly the purview of science fiction novels, artificial intelligence (AI) is now everywhere we look. As the driving force behind everything from marketing algorithms and banking platforms to surgical robots and space exploration, AI is playing an increasingly important role in our lives whether we realize it or not. Our reliance on these exciting new technologies is only going to become more pronounced in the coming years.

So it should come as no surprise that the best and most lucrative careers of the future will have at least something to do with AIeven if your job doesn't require you to wear a white lab coat every day.

The good news is that AI isn't actually as scary as it sounds, and it's possible to gain a thorough understanding of the field through instruction that's both affordable and easy to understand as you acquire one of LinkedIn's most highly-rated skills.

TheUltimate Artificial Intelligence Scientist Certification Bundle comes with four courses and over 87 hours of content that will get you up to speed with the various methodologies, platforms and programming languages that AI professionals use every day, and it's currently available for 95% off at just $34.99.

If you're completely new to the fascinating world of AI, start with the Machine Learning A-Z course. This top-rated module comes with 40 hours of content that will walk you through the technologies that create high-powered algorithms. This course will even teach you how to create algorithms of your own that you can use in a variety of analytical frameworks.

From there, you'll be ready to tackle more complex topics and themes in the Deep Learning A-Z course. With over 30,000 positive ratings from over 200,000 happy students, this extensive training will teach you how neural networks are formed, how to apply self-organizing maps that can be used to predict future behavior, and more.

This training bundle also comes with a course that's dedicated to teaching you about Python --one of the world's most popular and versatile programming languages used in multiple industries. Even if you've never written a line of code before in your life, you'll complete this module having learned how to build AI-driven apps with this powerful coding tool.

Finally, there's the top-rated Tensorflow course, which will teach you how to bring all of this new knowledge together in order to create AI solutions to everyday problems, how to maintain and develop your own neural networks, and more.

You don't need to spend an exorbitant amount of time or money in order to get the skills and tools you need to embrace the AI revolution. Usually priced at nearly $800, the Ultimate Artificial Intelligence Scientist Certification Bundle will give you a head start over the competition for just $34.99-- 95% off for a limited time.

Prices are subject to change.

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Break into artificial intelligence with this four-course online training for $35 - ZDNet

Automotive Artificial Intelligence Industry Market Trend Analysis and Major Factors Forecast Report till 2025 – CueReport

The research report on Automotive Artificial Intelligence Industry market delivers an exhaustive analysis of this business space while offering significant information pertaining to the factors that are affecting the revenue generation as well as the industry growth. The document also comprises of a detailed assessment of the regional scope of the market alongside its regulatory outlook. Additionally, the report provides with a detailed SWOT analysis while elaborating market driving factors.

Additional information including limitations & challenges faced by new entrants and market players in tandem with their respective impact on the revenue generation of the companies is enumerated. The document scrutinizes the impact of COVID-19 pandemic on growth as well as future remuneration of the market.

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Emphasizing on the competitive scenario of the Automotive Artificial Intelligence Industry market:

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From the regional perspective of Automotive Artificial Intelligence Industry market:

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Strategic Analysis Covered in TOC: - Key Topics Covered

Initially, the document offers an outline of the global market with a complete take a look at key drivers, constraints, challenges, traits and product types sold by using the employer. The file studies the Automotive Artificial Intelligence Industry market capacity of key packages with the identity of forecast opportunities. The local evaluation with a focus on specific international locations and area of interest markets is presented. The pinnacle organization profiles with key-word market size and proportion estimation, revenue strategies, products, and other factors are studied.

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Automotive Artificial Intelligence Industry Market Trend Analysis and Major Factors Forecast Report till 2025 - CueReport

Integrating Artificial Intelligence in Treatment Planning – Imaging Technology News

At the American Association of Physicists in Medicine (AAPM) 2019 meeting, new artificial intelligence (AI) software to assist with radiotherapy treatment planning systems was highlighted. The goal of the AI-based systems is to save staff time, while still allowing clinicians to do the final patient review.

RaySearch demonstrated a new U.S. Food and Drug Administration (FDA)-cleared machine learning treatment planning system. The RaySearch RayStation machine learning algorithm is being used clinically by University Health Network, Princess Margaret Cancer Center, Toronto, Canada, where it was rolled out over several months in late-2019. Medical physicist Leigh Conroy, Ph.D., was involved in this rollout and helped conduct a study, showing the automated plans and traditionally made plans to radiation oncologists to get valuable feedback. She spoke at the AAPM 2019 meeting on this topic.

In an interview with itnTV, Conroy explained that she worked with an algorithm that uses machine learning to create automated treatment plans. With this, we train the algorithm using a curated set of high-quality, previously delivered plans. Then, it is able to detect the patient that is most similar to a novel patient and create a new treatment plan with no user interaction beyond pressing the play button, she explained.

Conrads study directly compared those automated treatment plans to traditionally generated manual plans. The radiation oncologist then compared those two plans head-to-head and decided whether each plan was acceptable, and chose the favored plan. The plans are not modified as they are performing them, however they are modifiable. The automated plan is modifiable, but for the purposes of this study, we are not going to be modifying them so we can directly compare the output of the machine learning algorithm to the output of the planners, she said.

The system was trained using a model that was developed at Princess Margaret, and the model is being used clinically. The way that we are doing our study is if the doctor does choose the automated plan, then its underlined that the physicist knows when they are doing their plan QA that it is an automated plan, and it goes to the same QA that it normally would, and the patient-specific QA is a fully deliverable plan, and that is the plan that the patient is treated with.

Depending on how things go with the study, it is predicted that AI should see a regular implementation. That is the point of the study, to make sure we can do this and work it into our regular process and eventually provide it if the doctors continue to like the automated plans, she said.

One of the main ideas in implementing AI is to save time to get more patient throughput. We are not measuring the end to end timing of a planner vs. the machine learning. But one of the major advantages is that it takes about 20 minutes for a new patient, however there is no user interacting during those 20 minutes, so the planner can go and do other work or other plans during that time, and come back so there is a fully done plan.

There is a different process depending on what plan is being created. However, the AI would help to free them up to be able to do other duties. From a planner expertise perspective, some of the planning techniques such as head and neck might be more complicated so it might take longer for the planner to do it. Its more reliant on their level of expertise, she explained.

Varian also has developed AI-driven automated treatment planning software that is currently being used by several hospitals.

Recently, Varian released the newest version of its treatment planning system, Eclipse v16. This new release includes intelligent features such as RapidPlan PT, a clinical application of machine learning in proton treatment planning, and RT Peer Review, a collaborative workspace designed to streamline and accelerate the peer review process for radiotherapy treatment plans.

Previously only available for photon-based radiotherapy treatment planning, RapidPlan is knowledge-based treatment planning software that enables clinicians to leverage knowledge and data from similar prior treatment plans to quickly develop high-quality personalized plans for patients. This knowledge-based planning software is now available for proton treatment planning with RapidPlan PT. The software also allows dose prediction with machine learning models that can be used as a decision support tool to determine which patients would be appropriate for proton or photon therapy.

With the number of operational proton treatment rooms continuing to increase, there is a need for experienced proton therapy clinicians, said Kolleen Kennedy, chief growth officer, president, Proton Solutions, Varian, in a written statement. RapidPlan PT helps bridge the learning curve, allowing established centers to share their models and clinical experience. The machine learning in RapidPlan PT has the potential to reduce proton treatment plan optimization from a one- to eight-hour process, as reported by clinical proton centers, to less than 10 minutes, while also potentially improving plan quality.

Eclipse v16 has received the CE mark and is 510(k) pending.

Artificial Intelligence Greatly Speeds Radiation Therapy Treatment Planning

VIDEO: Use of Machine Learning to Automate Radiotherapy Treatment Planning

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Artificial Intelligence in Transportation Market, Share, Growth, Trends And Forecast To 2025 – Cole of Duty

The Latest Research Report on Artificial Intelligence in Transportation Market size | Industry Segment by Applications, by Type, Regional Outlook, Market Demand, Latest Trends, Artificial Intelligence in Transportation Industry Share & Revenue by Manufacturers, Company Profiles, Growth Forecasts 2025. Analyzes current market size and upcoming 5 years growth of this industry.

The report presents a highly comprehensive and accurate research study on the globalArtificial Intelligence in Transportation market. It offers PESTLE analysis, qualitative and quantitative analysis, Porters Five Forces analysis, and absolute dollar opportunity analysis to help players improve their business strategies. It also sheds light on critical Artificial Intelligence in Transportation Marketdynamics such as trends and opportunities, drivers, restraints, and challenges to help market participants stay informed and cement a strong position in the industry. With competitive landscape analysis, the authors of the report have made a brilliant attempt to help readers understand important business tactics that leading companies use to maintainArtificial Intelligence in Transportation market sustainability.

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Global Artificial Intelligence in Transportation Market to reach USD 4.5 billion by 2025.

Global Artificial Intelligence in Transportation Market valued approximately USD 1.2 billion in 2017 is anticipated to grow with a healthy growth rate of more than 18% over the forecast period 2018-2025. The growth of the Artificial Intelligence in Transportation market is majorly driven by the development of autonomous vehicles and increasing focus towards reducing the operating cost of transportation. Major developments in Market are related to software. Companies such as IBM and Alphabet Inc. are investing heavily in Artificial Intelligence software, which is benefiting the market of the category. Furthermore, the declining prices of hardware will increase the share of the software category in the market by 2025.

The regional analysis of Global Artificial Intelligence in Transportation Market is considered for the key regions such as Asia Pacific, North America, Europe, Latin America and Rest of the World. North America is estimated to account for the largest share in the global AI in transportation market, valued at more than 44.0% in 2017. The region includes developed countries such as the U.S. and Canada, which are prominent markets of AI in transportation. Government support and sales of long-haul and premium trucks are driving the market in the region. The U.S. has accounted for a major portion of market revenues in the region till now, due to considerable government and private sector investment, coupled with a favorable policy framework. A well-developed trucking industry with an estimated 15 million registered trucks in the country, ensures considerable long-term opportunity for AI in transportation.

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming eight years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within each of the regions and countries involved in the study. Furthermore, the report also caters the detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, the report shall also incorporate available opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:

By Machine Learning Technology:

oComputer Vision

oContext Awareness

oDeep Learning

oNatural Language processing

By Process:

oData Mining

oImage Recognition

oSignal Recognition

By Application:

oAutonomous Trucks

oHMI in Trucks

oSemi-Autonomous Truck

By Offering:

oHardware

oSoftware

By Regions:

oNorth America

oU.S.

oCanada

oEurope

oUK

oGermany

oAsia Pacific

oChina

oIndia

oJapan

oLatin America

oBrazil

oMexico

oRest of the World

Furthermore, years considered for the study are as follows:

Historical year 2015, 2016

Base year 2017

Forecast period 2018 to 2025

The industry is seeming to be fairly competitive. Some of the leading market players include Volvo, Daimler, Scania, Paccar, Continental, Magna, Bosch, ZF, Nvidia, Intel, Microsoft and so on. Acquisitions and effective mergers are some of the strategies adopted by the key manufacturers. New product launches and continuous technological innovations are the key strategies adopted by the major players.

Target Audience of the Global Artificial Intelligence in Transportation Market in Market Study:

oKey Consulting Companies & Advisors

oLarge, medium-sized, and small enterprises

oVenture capitalists

oValue-Added Resellers (VARs)

oThird-party knowledge providers

oInvestment bankers

oInvestors

Have Any Query Or Specific Requirement?Ask Our Industry Experts!

Table of Contents:

Study Coverage:It includes study objectives, years considered for the research study, growth rate and Artificial Intelligence in Transportation market size of type and application segments, key manufacturers covered, product scope, and highlights of segmental analysis.

Executive Summary:In this section, the report focuses on analysis of macroscopic indicators, market issues, drivers, and trends, competitive landscape, CAGR of the global Artificial Intelligence in Transportation market, and global production. Under the global production chapter, the authors of the report have included market pricing and trends, global capacity, global production, and global revenue forecasts.

Artificial Intelligence in Transportation Market Size by Manufacturer: Here, the report concentrates on revenue and production shares of manufacturers for all the years of the forecast period. It also focuses on price by manufacturer and expansion plans and mergers and acquisitions of companies.

Production by Region:It shows how the revenue and production in the global market are distributed among different regions. Each regional market is extensively studied here on the basis of import and export, key players, revenue, and production.

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Artificial Intelligence in Transportation Market, Share, Growth, Trends And Forecast To 2025 - Cole of Duty

Artificial Intelligence In Transportation Market 2020: Industry Analysis By Size, Share, Key Players, Growth Trends and Forecast Till 2025 – Apsters…

This meticulous research based analytical review on Artificial Intelligence In Transportation market is a high end expert handbook portraying crucial market relevant information and developments, encompassing a holistic record of growth promoting triggers encapsulating trends, factors, dynamics, challenges, and threats as well as barrier analysis that accurately direct and influence profit trajectory of Artificial Intelligence In Transportation market. The report is also an up-to-date reference point of all major developments throughout the market in terms of major mergers and acquisitions, geographical expansion ventures, new portfolio diversification initiatives and the like.

Leading Companies Reviewed in the Report are:

Continental AG, NVIDIA Corporation, Intel Corporation, Microsoft Corporation, Alphabet Inc., ZF Friedrichshafen AG, Robert Bosch GmbH, Valeo SA, and more others.

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This mindfully crafted, well researched, and methodically presented research report on the mentioned Artificial Intelligence In Transportation market suggests tangible progress in recognizing exact growth rendering factors. The report also makes progress in decoding the growth enablement triggers besides also deciphering the potential of each of the market dimensions in harnessing full-fledged, effortless growth. The report also incorporates ample understanding on numerous analytical practices such as SWOT and PESTEL analysis to source optimum profit resources in Artificial Intelligence In Transportation market.

A thorough run down on essential elements such as drivers, threats, challenges, opportunities are discussed at length in this elaborate report on Artificial Intelligence In Transportation market and eventually analyzed to document logical conclusions. The report is a highly decisive data center encompassing a whole plethora of relevant information pertaining to historic data, also suggesting relevant cues on future growth predictions and forecast, based on which players in the Artificial Intelligence In Transportation market can thereby effectively deliver lucrative business discretion to ensure sustainable revenue flow amidst cut-throat market competition as well as emergence of new and disruptive market participants, intensifying competition.

Quick Read Table of Contents of this Report @ https://www.adroitmarketresearch.com/industry-reports/artificial-intelligence-in-transportation-market

Global Artificial Intelligence In Transportation Market is segmented based by type, application and region.

Based on Type, the Market has been segmented into:

By Process, (Data Mining,Image Recognition,Signal Recognition)

Based on application, the Market has been segmented into:

By Application, (Autonomous Trucks,HMI in Trucks,Semi-Autonomous Truck)

In its subsequent sections, this report also shares crucial data on competitive landscape, identifying frontline players, complete with detailed analysis of marketing initiatives and strategies adopted to secure favorable investment returns and sustainable revenue pools in the Artificial Intelligence In Transportation market. This in-depth analytical presentation of the Artificial Intelligence In Transportation market is a ready-to-go market synopsis encompassing a gamut of market relevant factors that tend to have a steady and tangible impact on holistic growth prospects of the Artificial Intelligence In Transportation market.

This in-depth research output is a thorough consequence of intricate primary and secondary research initiatives directed by research experts and analysts to offer substantial aid to various market players and stakeholders such as supply chain professionals and industry veterans, who make logical conclusions pertaining the Artificial Intelligence In Transportation market, in a bid to influence highly profitable business discretion in the Artificial Intelligence In Transportation market. Thorough, minute details encapsulating market players, complete with their company portfolios and product overview are tagged in the subsequent sections of the report to eventually influence a favorable business discretion directed towards sustainable revenue flow in the Artificial Intelligence In Transportation market.

Do You Have Any Query Or Specific Requirement? Ask to Our Industry [emailprotected] https://www.adroitmarketresearch.com/contacts/enquiry-before-buying/525

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Artificial Intelligence In Transportation Market 2020: Industry Analysis By Size, Share, Key Players, Growth Trends and Forecast Till 2025 - Apsters...

High Growth of Steady Explore Artificial Intelligence (AI) in Construction Market size, Growth analysis & forecast report to 2025 – 3rd Watch News

The Artificial Intelligence (AI) in Construction market study now available with Market Study Report, LLC, delivers a concise outlook of the powerful trends driving market growth. This report also includes valuable information pertaining to market share, market size, revenue forecasts, regional landscape and SWOT analysis of the industry. The report further elucidates the competitive backdrop of key players in the market as well as their product portfolio and business strategies.

The Artificial Intelligence (AI) in Construction market is an intrinsic study of the current status of this business vertical and encompasses a brief synopsis about its segmentation. The report is inclusive of a nearly accurate prediction of the market scenario over the forecast period market size with respect to valuation as sales volume. The study lends focus to the top magnates comprising the competitive landscape of Artificial Intelligence (AI) in Construction market, as well as the geographical areas where the industry extends its horizons, in magnanimous detail.

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A rundown of the competitive spectrum:

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Unveiling the regional landscape:

An outline of the Artificial Intelligence (AI) in Construction market segmentation:

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Pivotal highlights of Artificial Intelligence (AI) in Construction market:

The Artificial Intelligence (AI) in Construction market report enumerates quite some details about the factors impacting the industry, influence of technological developments on the vertical, risks, as well as the threats that substitutes present to the industry players. In addition, information about the changing preferences and needs of consumers in conjunction with the impact of the shifting dynamics of the economic and political scenario on the Artificial Intelligence (AI) in Construction market has also been acknowledged in the study.

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High Growth of Steady Explore Artificial Intelligence (AI) in Construction Market size, Growth analysis & forecast report to 2025 - 3rd Watch News

Enterprise Artificial Intelligence Market 2020: Challenges, Growth, Types, Applications, Revenue, Insights, Growth Analysis, Competitive Landscape,…

The report covers analysis on regional and country level market dynamics. The scope also covers competitive overview providing company market shares along with company profiles for major revenue contributing companies.

Enterprise Artificial Intelligence Market, By Deployment this market is segmented on the basis of Cloud and On-Premises. Enterprise Artificial Intelligence Market, By Service this market is segmented on the basis of Professional Service and Managed Service. Enterprise Artificial Intelligence Market, By End User this market is segmented on the basis of Automotive, Media And Entertainment, Healthcare, Retail, IT & Telecommunication, BFSI and Aerospace. Enterprise Artificial Intelligence Market, By Deployment this market is segmented on the basis of Cloud and On-Premises. Enterprise Artificial Intelligence Market, By Solution this market is segmented on the basis of Business Intelligence, Customer Management, Sales & Marketing, Finance & Operations, Digital Commerce and Others. Enterprise Artificial Intelligence Market, By Region this market is segmented on the basis of North America, Europe, Asia-Pacific and Rest of the World. Enterprise Artificial Intelligence Market, By Company this market is segmented on the basis of Intel Co, Microsoft Co, Amazon Web Services, Oracle, SAP, IBM, Google Inc., SAS Institute, Microsoft Co and Hewlett Packard Enterprise.

Based on Deployment, the global Enterprise Artificial Intelligence market is segmented in Cloud and On-Premises. The report also bifurcates the global Enterprise Artificial Intelligence market based on Solution in Business Intelligence, Customer Management, Sales & Marketing, Finance & Operations, Digital Commerce, and Others.

You will get latest updated report as per the COVID-19 Impact on this industry. Our updated reports will now feature detailed analysis that will help you make critical decisions.

Browse Full Report: https://www.marketresearchengine.com/enterprise-artificial-intelligence-market

The global Enterprise Artificial Intelligence market report scope includes detailed study covering underlying factors influencing the industry trends.

The global Enterprise Artificial Intelligence market report provides geographic analysis covering regions, such as North America, Europe, Asia-Pacific, and Rest of the World. The Enterprise Artificial Intelligence market for each region is further segmented for major countries including the U.S., Canada, Germany, the U.K., France, Italy, China, India, Japan, Brazil, South Africa, and others.

The global Enterprise Artificial Intelligence market is expected to exceed more than US$ 12 Billion by 2024 at a CAGR of 42% in the given forecast period.

The global Enterprise Artificial Intelligence market is segregated on the basis of Deployment as Cloud and On-Premises. Based on Service the global Enterprise Artificial Intelligence market is segmented in Professional Service and Managed Service. Based on End User the global Enterprise Artificial Intelligence market is segmented in Automotive, Media and Entertainment, Healthcare, Retail, IT & Telecommunication, BFSI, and Aerospace.

Enterprise AI is the ability to implant AI methodology into the very core of the organization and into the data governance strategy. This means augmenting the work of individuals across all groups and disciplines with AI for additional innovative operations, processes, products, and more. Companies that wish to be more economical, or develop new product within the returning years can need to adopt Enterprise AI to create it happen.

Artificial Intelligence (AI), which combines the human capacities for learning, perception, and interaction all at a level of complexity that ultimately supersedes our own talents.

Competitive Rivalry

Intel Co, Microsoft Co, Amazon Web Services, Oracle, SAP, IBM, Google Inc., SAS Institute, Microsoft Co, Hewlett Packard Enterprise, and others are among the major players in the global Enterprise Artificial Intelligence market. The companies are involved in several growth and expansion strategies to gain a competitive advantage. Industry participants also follow value chain integration with business operations in multiple stages of the value chain.

The Enterprise Artificial Intelligence Market has been segmented as below:

The Enterprise Artificial Intelligence Market is segmented on the lines of Enterprise Artificial Intelligence Market, By Deployment, Enterprise Artificial Intelligence Market, By Service, Enterprise Artificial Intelligence Market, By End User, Enterprise Artificial Intelligence Market, By Deployment, Enterprise Artificial Intelligence Market, By Solution, Enterprise Artificial Intelligence Market, By Region and Enterprise Artificial Intelligence Market, By Company.

The report covers:

The report scope includes detailed competitive outlook covering market shares and profiles key participants in the global Enterprise Artificial Intelligence market share. Major industry players with significant revenue share include Intel Co, Microsoft Co, Amazon Web Services, Oracle, SAP, IBM, Google Inc., SAS Institute, Microsoft Co, Hewlett Packard Enterprise, and others.

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Table of Contents:

Other Related Market Research Reports:

Artificial Intelligence Software Platform Market to Grow US$ 11.5 Billion by 2024

Artificial Intelligence (AI) In Supply Chain Market to Reach US$ 10 Billion by 2024

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Excerpt from:

Enterprise Artificial Intelligence Market 2020: Challenges, Growth, Types, Applications, Revenue, Insights, Growth Analysis, Competitive Landscape,...

Artificial Intelligence in Construction Market will drastically increase in the Future | brandessenceresearch.biz – Cole of Duty

Research report on global Artificial Intelligence in Construction market 2020 with industry primary research, secondary research, product research, size, trends and Forecast.

The report presents a highly comprehensive and accurate research study on the globalArtificial Intelligence in Construction market. It offers PESTLE analysis, qualitative and quantitative analysis, Porters Five Forces analysis, and absolute dollar opportunity analysis to help players improve their business strategies. It also sheds light on critical Artificial Intelligence in Construction Marketdynamics such as trends and opportunities, drivers, restraints, and challenges to help market participants stay informed and cement a strong position in the industry. With competitive landscape analysis, the authors of the report have made a brilliant attempt to help readers understand important business tactics that leading companies use to maintainArtificial Intelligence in Construction market sustainability.

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Global Artificial Intelligence in Construction Market to reach USD XX billion by 2025.

Global Artificial Intelligence in Construction Market valued approximately USD XX billion in 2017 is anticipated to grow with a healthy growth rate of more than XX% over the forecast period 2018-2025. The major driving factor of global artificial intelligence in construction market are growing demand across end user industries, technological advancements have encouraged the organizations especially construction and engineering sector and the increasing digital data. In addition, a rapid surge in the growth of the digital data has been witnessed owing to the growing adoption of Building Information Systems (BIM), security sensors, drones, and machine telematics. This is encouraging construction companies to adopt advanced analytics solutions to take the full advantage of the huge amount of digital data and extract actionable insights. The major restraining factor of global artificial intelligence in construction market are unstructured construction environment and lack of skilled workforce. Moreover, adoption of the drones, robots, and autonomous vehicles in the construction sector is also backing the growth of the artificial intelligence in construction market. Artificial Intelligence in Construction Management is the core of artificial intelligence. With data collected at various cycles of the construction project across many different projects in construction firms, this provides valuable learning information for artificial intelligence applications. Artificial intelligence serves as a helpful tool for every phase of the construction project. The major key benefits of artificial intelligence are By using Construction Language Analysis, from tools such as Autodesk BIM 360 software, algorithms are able to understand complex data and predict potential problems, by using AI technology in the construction industry and scanning software, they can track the body movement of bricklayers to analyses their form in order to reduce the amount of injuries on-site and artificial intelligence in construction can be used to measure a projects parameters which is then fed into a computer which understands the data and requirements of their physical location.

The regional analysis of Global Artificial Intelligence in Construction Market is considered for the key regions such as Asia Pacific, North America, Europe, Latin America and Rest of the World. North-America has accounted the dominant share in the global Artificial Intelligence in Construction market due to high investments by construction companies. Additionally, Asia Pacific is also expected to register a considerable growth rate in the market over the forecasted period 2018-2025. China, Japan, South Korea, and India are the leading countries in this region. The market growth is due to increase in demand by the economies to develop smart city projects which require better amenities that boost the real estate sector.

The leading market player are:

IBM

Microsoft

Oracle

SAP

Alice Technologies

Aurora Computer Services

Autodesk

Coins Global

Beyond Limits

Plangrid

Renoworks Software

Bentley Systems

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming eight years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within each of the regions and countries involved in the study. Furthermore, the report also caters the detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, the report shall also incorporate available opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:

By Application:

oProject Management

oField Management

oRisk Management

oSchedule Management

oSupply-Chain Management

oOthers

By Industry:

oResidential

oInstitutional Commercial

oHeavy construction

oOthers

By Component:

oSolutions

oServices

By Stage of Construction:

oPre-Construction

oConstruction Stage

oPost-Construction

By Technology:

oMachine Learning & Deep Learning

oNatural Language Processing

By Deployment:

oCloud

oOn-Premises

By Regions:

oNorth America

oU.S.

oCanada

oEurope

oUK

oGermany

oAsia Pacific

oChina

oIndia

oJapan

oLatin America

oBrazil

oMexico

oRest of the World

Furthermore, years considered for the study are as follows:

Historical year 2015, 2016

Base year 2017

Forecast period 2018 to 2025

Target Audience of the Global Artificial Intelligence in Construction Market in Market Study:

oKey Consulting Companies & Advisors

oLarge, medium-sized, and small enterprises

oVenture capitalists

oValue-Added Resellers (VARs)

oThird-party knowledge providers

oInvestment bankers

oInvestors

Have Any Query Or Specific Requirement?Ask Our Industry Experts!

Table of Contents:

Study Coverage:It includes study objectives, years considered for the research study, growth rate and Artificial Intelligence in Construction market size of type and application segments, key manufacturers covered, product scope, and highlights of segmental analysis.

Executive Summary:In this section, the report focuses on analysis of macroscopic indicators, market issues, drivers, and trends, competitive landscape, CAGR of the global Artificial Intelligence in Construction market, and global production. Under the global production chapter, the authors of the report have included market pricing and trends, global capacity, global production, and global revenue forecasts.

Artificial Intelligence in Construction Market Size by Manufacturer: Here, the report concentrates on revenue and production shares of manufacturers for all the years of the forecast period. It also focuses on price by manufacturer and expansion plans and mergers and acquisitions of companies.

Production by Region:It shows how the revenue and production in the global market are distributed among different regions. Each regional market is extensively studied here on the basis of import and export, key players, revenue, and production.

About Us:

We publish market research reports & business insights produced by highly qualified and experienced industry analysts. Our research reports are available in a wide range of industry verticals including aviation, food & beverage, healthcare, ICT, Construction, Chemicals and lot more. Brand Essence Market Research report will be best fit for senior executives, business development managers, marketing managers, consultants, CEOs, CIOs, COOs, and Directors, governments, agencies, organizations and Ph.D. Students.

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Artificial Intelligence in Construction Market will drastically increase in the Future | brandessenceresearch.biz - Cole of Duty


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