Brossard, Quebec - TheNewswire - July 23, 2020 - Windfall Geotek (TSXV:WIN) is a leader in the use of Artificial Intelligence (AI) in the mining sector for digital exploration and is pleased to announce that it has signed an agreement with BTU Metals Corp to provide new high probability gold targets develop during an internal project on the Red Lake Mining Camp.

As reported by BTU Metals Corp earlier this week, the exploration team has confirmed positive results received from ongoing work using Windfall's proprietary 'CARDS' Artificial Intelligence ("AI") system on the identified high-grade gold targets. BTU is pursuing both high-grade gold targets and copper-dominant massive sulfide targets on its 200 square kilometer property, that shares a 35 kilometer border with Great Bear Resources Ltd ("Great Bear"). One-third of the property area has been analyzed, from which 35 high priority targets have been identified at a high correlation rate with known gold mineralization within the Red Lake camp. More validation and follow-up investigation of the CARDS AI generated gold targets are being conducted by BTU geologists and the highest priority targets are expected to be drill-ready later this summer.

Windfall Geotek used two distinct models in the initial investigation for BTU (Figure 1):

Learn more about the geologic context of these gold target areas at http://www.btumetals.com/aitargets

Paul Wood, BTU CEO, said, "The early identification of so many high priority targets using Windfall's proprietary AI is very encouraging. Our property position is one of the largest in Red Lake and covers nearly 200 square kilometres. Most of the potentially gold bearing rock units are covered with overburden so additional tools and techniques can be particularly effective. This new layer of AI targeting is now being incorporated into the mix as we attempt to vector in on the areas with the highest gold potential.

Figure 1: AI generated gold targets on the BTU Metals property

Michel Fontaine President & CEO of Windfall Geotek states: "Rarely have we seen such enthusiasm in one of our completed projects. We are proud to see our talented team and methodology validated at such a quick pace. This is a great sign that our new business model and final deliverables are of high value to our clients and we look forward to adding more as we progress".

Windfall Identifies High-Grade Gold Targets on BTU Metals Corp. Ground

Windfall is an Artificial Intelligence company that has been in business for over 15 years developing its proprietary CARDS analysis (AI) and data mining techniques. It combines available public and private datasets including geophysical, drill hole and surface data. The algorithms designed and employed by Windfall are designed to highlight areas of interest that have the potential to be geologically similar to other gold deposits and mineralization in the Red Lake region. Windfall has played a part in numerous past discoveries utilizing its methodology as described at: undefined.

About Windfall Geotek - Powered by Artificial Intelligence (AI) since 2005

Windfall Geotek is a services company using Artificial Intelligence (AI) with an extensive portfolio of shares of its clients. Windfall Geotek can count on a multidisciplinary team that includes professionals in geophysics, geology, Artificial Intelligence, and mathematics. The Company's objective is to develop a new royalty stream by significantly enhancing and participating in the exploration success rate of mining and to continue the Land Mine detection application as a high priority.

For further information, please contact:

Michel Fontaine

President and CEO of Windfall Geotek

Telephone: 514-994-5843Email: This email address is being protected from spambots. You need JavaScript enabled to view it.Website: http://www.windfallgeotek.com

Additional information about the Company is available under Windfall Geotek's profile on SEDAR at http://www.sedar.com. Neither the TSX Venture Exchange nor does its Regulation Services Provider (as that term is defined in the policies of the TSX Venture Exchange) accept responsibility for the adequacy or accuracy of this release.

FORWARD LOOKING STATEMENTS This news release may contain forward-looking statements. Forward looking statements are statements that are not historical facts and are generally, but not always, identified by the words "expects", "plans", "anticipates", "believes", "intends", "estimates", "projects", "potential" and similar expressions, or that events or conditions "will", "would", "may", "could" or "should" occur. Although the Company believes the expectations expressed in such forward-looking statements are based on reasonable assumptions, such statements are not guarantees of future performance and actual results may differ materially from those in forward looking statements. Forward-looking statements are based on the beliefs, estimates and opinions of the Company's management on the date such statements were made. The Company expressly disclaims any intention or obligation to update or revise any forward-looking statements whether as a result of new information, future events or otherwise. Neither TSX Venture Exchange nor its Regulation Services Provider (as that term is defined in the policies of TSX Venture Exchange) accepts responsibility for the adequacy of accuracy of this release

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Windfall Geotek Reports Positive Results from recent BTU Gold targets Generated by its CARDS Artificial Intelligence (AI) on The Dixie Halo property...

New York, July 22, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Artificial Intelligence in Healthcare Diagnosis Market Forecast to 2027 - COVID-19 Impact and Global Analysis by Diagnostic Tool ; Application ; End User ; Service ; and Geography" - https://www.reportlinker.com/p05932654/?utm_source=GNW 0% during 2020-2027. The growth of the market is mainly attributed to factors such rising adoption of AI in disease identification and diagnosis, and increasing investments in AI healthcare startups. However, the lack of skilled workforce and ambiguity in regulatory guidelines for medical software are the factor hindering the growth of the market.

Artificial Intelligence in healthcare is one of the most significant technological advancements in medicine so far.The involvement of multiple startups in the development of AI-driven imaging and diagnostic solutions is the major factors contributing to the growth of the market.

China, the US, and the UK are emerging as popular hubs for healthcare innovations.Additionally, the British government has announced the establishment of a National Artificial Intelligence Lab that would collaborate with the countrys universities and technology companies to conduct research on cancer, dementia, and heart diseases.

The UK-based startups have received benefits from the governments robust library of patient data, as British citizens share their anonymous healthcare data with the British National Health Service. As a result, the number of artificial intelligence startups in the healthcare sector has significantly grown in the past few years, and the trend is expected to be the same in the coming years.

Based on diagnostic tool, the global artificial intelligence in healthcare diagnosismarket is segmented intomedical imaging tool, automated detection system, and others. The medical imaging toolsegment held the largest share of the market in 2019, and the market for automated detection systemis expected to grow at the highest CAGR duringthe forecast period.

Based on application, the global artificial intelligence in healthcare diagnosismarket is segmented into eye care, oncology, radiology, cardiovascular, and others. The oncologysegment held the larger share of the market in 2019,and the radiologysegment is expected to register the highest CAGR during the forecast period.Based on service, the global artificial intelligence in healthcare diagnosis market is segmented into tele-consultation, tele monitoring, and others. The tele-consultationsegment held the largest share of the market in 2019,however,tele monitoringsegment it is further expected to report the highest CAGR in the market during the forecast period.

Based on end user, the global artificial intelligence in healthcare diagnosismarket is segmented into hospital and clinic, diagnostic laboratory, and home care. The hospital and clinicsegment held the highest share of the market in 2019 and is expected to register the highest CAGR in the market during the forecast period.

The Computer Science and Artificial Intelligence Laboratory (CSAIL), Food and Drug Administration (FDA), National Institute of Health (NIH), and European Medical Association are a few of the major secondary sources referred to while preparing this report.Read the full report: https://www.reportlinker.com/p05932654/?utm_source=GNW

About ReportlinkerReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.

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Artificial Intelligence in Healthcare Diagnosis Market Forecast to 2027 - COVID-19 Impact and Global Analysis by Diagnostic Tool ; Application ; End...

The global pandemic is pushing the healthcare system even harder to find ways to help hospitals efficiently address cost and streamline operations. From managing healthcare billing and the insurance process to providing a faster diagnosis of a serious disease, artificial intelligence (AI) has the potential to completely change how hospitals operate. MedTech Intelligence recently discussed some of the areas of impact with Jim McGowan, head of product at ElectrifAI.

MedTech Intelligence: How is AI helping hospitals manage healthcare bills and the insurance process?

Jim McGowan: The original areas within a hospital where AI created efficiency were in registration and insurance processing, most notably in revenue cycle management (RCM). RCM was envisioned as a seamless process across patient appointment and registration; claim coding and submission; payment reconciliation; and appeals. Over time these solutions grew so complex that parallel industries around Pay and Chase emerged, in which providers needed incremental support to capture all their revenue. With margins in the low single digits each dollar counts.

These RCM systems are rule based, which is antiquated AI technology. [Our] RevCaptureAi solution combats the limitations of these traditional revenue cycles with the dynamic intelligence of artificial intelligence (AI) and machine learning (ML) that track, analyze and generate insights about your missed charges. In a billion-dollar health system, just 1% of missed total charges adds up to $10 million in lost revenue. This is the opportunity.

Both providers and payers are implementing chatbots to more efficiently engage with patients/members by automating common support topics like confirming eligibility, getting claims/payment status, scheduling appointments and more. Machine learning is in the early stages of adoption. ElectrifAi has used machine learning to capture missed codes on hospital bills for [more than] five years, and building practical solutions to AI problems for [more than] 15 [years].

ElectrifAIs CEO Edward Scott discusses artificial intelligence and machine learning during the coronavirus crisis in Beating COVID-19 Is a Team SportMTI: How is the technology streamlining medication management? What is its role in managing procedures?

McGowan: Medication errors are still a significant issue in hospitals. EMR solutions were implemented to improve workflow and data capture for a complete patient view. These solutions have reduced adverse drug events (ADEs). Technology has been used to create many checks-and-balances within hospitals, which requires a double-check and scan of a barcode for each patient and medication to validate the drug was prescribed by a physician. There is continued work needed to capture the full patient history as these solutions are hospital system specific, do not include interoperability with the PBM data, and do not share with other hospital systems. Ultimately, a more complete patient system of record may be necessary to ensure that each system connects to each other to share data.

One of the areas where AI in healthcare has shown the most promise is in diagnostics, which can ultimately be leveraged in operating and emergency room settings. Right now, early diagnosis is one of the most important factors in the ultimate outcome of a patients care. AI deep-learning algorithms are being used to shave down the time it takes to diagnose serious illnesses. Our PulmoAi X-ray solution is an example of a tool that amplifies the work of radiologists, who leverage AI to triage cases as emergency rooms and ICUs overflow.AI is being used within healthcare for evidence-based recommendations. AI algorithms ingest collected vitals, lab results, medication orders and comorbidities and produce smarter triage tools.

We have seen growth in digital applications for mental health and virtual assistants to answer patient questions. As telehealth grows, I would not be surprised if the virtual assistants handle increasingly large volumes of questions, significantly greater than live operators. These bots are becoming much more important as the front-end to a telehealth call.

AI and Robotics for laser eye surgery and orthopedic surgeries are growing. AI-based visualizations are exploding in the market. AI is attempting to enter every facet of healthcare.

MTI: What factors should technology developers consider when designing AI solutions for hospitals?

McGowan: There are a number of important factors: Regulatory concerns, community demographics, fitting into existing workflows, technical proficiency of both the hospital personnel and consumers.

Healthcare is a highly regulated industry. HIPAA balances portability with privacy. This is for a very good reason, but has a lot of side effects, like complicating marketing efforts. You cant send an email to a patient telling her its okay to get the hip surgery she canceled when COVID-19 struck, because you cant guarantee someone else wont read it. If you send someone a reminder about their diabetes medication and are too specific in the email, what happens when that email is opened by someone other than the specific patient? Solutions that require you to log into a website to view the information was the evolution during the 2010s and continued to evolve with the growth in depth and sophistication of the mobile app solutions. Inappropriate sharing of data, even within a family, can create legal liability that hampers more specific and appropriate messaging.

When building solutions, AI can enable a very quick solution to the above concerns. Tools like robotic process automation (RPA) and chat bots have allowed providers to quickly create solutions that gather patient information and respond with an appropriate response, even in the patients preferred language. These more natural language conversations guide the patient to a choice without being overly and overtly intrusive.Most importantly, AI and ML people really have to deeply understand their craft if they want to influence medical decisions of any kind. Data science is not just technology development. It requires deep understanding of the problem domain being addressed, as well as statistics, inference, and logic. And data science without exceptional data engineering is useless. There is no magic inside the algorithms. If the data is bad, the results will be bad. Weve seen data systems where almost half the data is inaccurate. Let that sink in. Would you go to a doctor if half the facts in their medical books were wrong? AI solutions start with great data engineering.

Id like to talk directly to the C-Suite in the hospitals for a moment.

Lets discuss the elephant in the room: many hospitals are poorly run businesses, with razor thin margins and inadequate spending controls. These are not financially healthy organizations.

This year we saw 42 hospitals file for bankruptcyso far. All have two things in common: They all had revenue capture solutions, and they all couldnt pay their bills.

First, revenue capture doesnt address your problem: you need elective surgeries. Revenue Capture fixes leaks in your billing process. Hospitals dont go bankrupt because their billing process is too leaky. The revenue isnt coming in. The elective surgeries arent there.

Second, the revenue capture programs you do have use rules-based systems, and those dont work when the rules change. COVID-19 changed the rules. You needed a machine-learning based solution. Rules-based systems have been around since the 1950s. The world has moved on. We have a machine learning based revenue capture solution, and not one hospital using it has gone bankrupt. And still, that should not be your priority right nowthats just a part of getting healthy.

You need to restart elective surgeries. You need to manage your finances.

Customer engagement isnt optional for any other business, and it isnt optional for yours. Machine learning can help.

You also need to get control of your spending. Spend analytics is critical. Again, this is not optional for any business, hospital or not. Machine learning can help.

AIespecially machine learninghelps improve the health of the patient, the financial health of the hospital, and ultimately the health of the community. The pandemic should not be a reason to push off these technologiesits the reason you should embrace them today.

Artificial intelligence and machine learning are proving to be meaningful weapons in our arsenal during the coronavirus crisis.

Change is constant, and we continue to evolve.

A recent paper released by Duke University cites the promise of AI, but urges policy changes in order to bring AI-enabled clinical decision software to fruition.

Expanded designs that enable clinicians to leverage data in making healthcare decisions, but privacy challenges remain.

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Tech Q&A: Artificial Intelligence Has Promise of Streamlining Hospital Processes, Diagnostic Tools - MedTech Intelligence

MUNICH, July 23, 2020 /PRNewswire/ --Nagarro, a global leader in digital engineering and technology solutions, announced the launch of AI-powered solutions to help organizations kick-start work and life amid the COVID-19 crisis. Based on machine vision technology, these solutions provide powerful workplace interventions quickly and effectively, and have the potential to transform how we work and interact by ensuring better health and safety of employees and visitors.

Nagarro's COVID-AI suite of solutions is designed to leverage state-of-the-art AI models running on low-cost edge devices and can be deployed at scale in a matter of weeks, with very little overhead. It has mechanisms to ensure social distancing behaviour, encourage PPE practices such as wearing masks, and monitor as well as mitigate high risk scenarios such as large collections of people.

Nagarro's COVID-AI suite of solutions includes:

"As the world grapples with COVID-19, every ounce of technological innovation and ingenuity harnessed to fight this pandemic brings us one step closer to overcoming it. AI and ML are playing a key role in better understanding and addressing the COVID-19 crisis, " said Anurag Sahay, VP & Global Head - AI & Data Sciences, Nagarro. "Organizations, businesses and establishments are finding new ways to operate effectively. At Nagarro, we are using AI powerfully to help bring some of these interventions in place. We believe that machine vision-based AI platforms have significant potential to transform how we work and live during the new normal."

Nagarro recently conducted a webinar highlighting how the COVID-AI suite of solutions can help organizations accelerate the adaptation to the new normal. To view the webinar recording, click here https://www.nagarro.com/webinar/ai-to-the-rescue-during-covid

Write to [emailprotected] for more information about Nagarro COVID-AI solutions.

About Nagarro

Nagarro drives technology-led business breakthroughs for industry leaders and challengers. When our clients want to move fast and make things, they turn to us. Today, we are more than 7,000 experts across 22 countries. Together we form Nagarro, the global services division of Munich-based Allgeier SE.

Contact:

Megha Jha [emailprotected]

Logo: https://mma.prnewswire.com/media/844192/Nagarro_Logo.jpg

SOURCE Nagarro

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Nagarro launches machine vision-based artificial intelligence solutions that mitigate COVID-19 risks and enhance workplace safety - PR Newswire India

The Global Artificial Intelligence in Retail market gives detailed Evaluation about all the Important aspects related to the marketplace. The analysis on global Artificial Intelligence in Retail economy, offers profound insights regarding the Artificial Intelligence in Retail market covering all of the crucial aspects of the market. Moreover, the report offers historical information with future prediction over the forecast period. Various important factors such as market trends, earnings growth patterns market shares and demand and supply are contained in almost all the market research report for every industry. A number of the vital facets analysed in the report contains market share, production, key regions, earnings rate in addition to key players.

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Artificial Intelligence in Retail Steady Growth to be Witnessed by 2019-2030 - Cole of Duty

The Artificial Intelligence in Fintech market report provides with a comprehensive analysis of this business space and comprises of crucial insights pertaining to current revenue, market tendencies, market size, periodic deliverables, market share, and profit predictions over study period.

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Market segmentation

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Cloud Based and On Premise

By Application, Artificial Intelligence in Fintech has been segmented into:

Regions and Countries Level Analysis

Regional analysis is another highly comprehensive part of the research and analysis study of the global Artificial Intelligence in Fintech market presented in the report. This section sheds light on the sales growth of different regional and country-level Artificial Intelligence in Fintech markets. For the historical and forecast period 2015 to 2026, it provides detailed and accurate country-wise volume analysis and region-wise market size analysis of the global Artificial Intelligence in Fintech market.

The report offers in-depth assessment of the growth and other aspects of the Artificial Intelligence in Fintech market in important countries (regions), including:

Competitive Landscape and Artificial Intelligence in Fintech Market Share Analysis

Artificial Intelligence in Fintech competitive landscape provides details by vendors, including company overview, company total revenue (financials), market potential, global presence, Artificial Intelligence in Fintech sales and revenue generated, market share, price, production sites and facilities, SWOT analysis, product launch. For the period 2015-2020, this study provides the Artificial Intelligence in Fintech sales, revenue and market share for each player covered in this report.

The major players covered in Artificial Intelligence in Fintech are:

Study objectives of Artificial Intelligence in Fintech Market Report:

Key questions Answered in this Artificial Intelligence in Fintech Market Report:

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Artificial Intelligence in Fintech Market by Technology, Solutions, Application, Price, Demand Analysis and Growth Opportunities to 2026 - CueReport

Global Artificial Intelligence and Cognitive Computing Market Size study report with COVID-19 effect is considered to be an extremely knowledgeable and in-depth evaluation of the present industrial conditions along with the overall size of the Artificial Intelligence and Cognitive Computing industry, estimated from 2020 to 2025. The research report also provides a detailed overview of leading industry initiatives, potential market share of Artificial Intelligence and Cognitive Computing, and business-oriented planning, etc. The study discusses favorable factors related to current industrial conditions, levels of growth of the Artificial Intelligence and Cognitive Computing industry, demands, differentiable business-oriented approaches used by the manufacturers of the Artificial Intelligence and Cognitive Computing industry in brief about distinct tactics and futuristic prospects.

Major Players Covered in this Report are:Microsoft Corporation, Apple Inc., Salesforce, Inc., Intel, Inc., Fair Isaac Corporation, Facebook, Alphabet Inc., CognitiveScale, Inc., Amazon Inc., SAP SE, IBM Corporation

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In this study, the years considered to estimate the market size of Artificial Intelligence and Cognitive Computing are as follows:

Research Objectives

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Artificial Intelligence and Cognitive Computing Market 2020 Industry Size, Trends, Global Growth, Insights and Forecast Research Report 2025 - Cole of...

Overview of Artificial Intelligence for Edge Devices Market:-

The data presented in the global Artificial Intelligence for Edge Devices Market report is a compilation of data identified and collected from various sources. The scope of growth of the Artificial Intelligence for Edge Devices Market during the forecast period is identified after analyzing different data sources. The report is a valuable guidance tool that can be used to increase the market share or to develop new products that can revolutionize the market growth. The analysis of the collected data also helps in providing an overview of the Artificial Intelligence for Edge Devices Market industry which further helps people make an informed choice. Latent growth factors that can manifest themselves during the forecast period are identified as they are key to the Artificial Intelligence for Edge Devices Market growth. The Artificial Intelligence for Edge Devices Market report presents the data from the year 2020 to the year 2026 during the base period while forecasting the same during the forecast period for the year 2020 to the year 2026.

Regional Description of Artificial Intelligence for Edge Devices Market:-

The global Artificial Intelligence for Edge Devices Market is segmented into different categories based on the regions that they are located in. This can enable an easier collection of data while giving more accurate representations of the market share in the various segments. The different regions mentioned in the global Artificial Intelligence for Edge Devices Market report are

North America (Covered in Chapter 6 and 13)United StatesCanadaMexicoEurope (Covered in Chapter 7 and 13)GermanyUKFranceItalySpainRussiaOthersAsia-Pacific (Covered in Chapter 8 and 13)ChinaJapanSouth KoreaAustraliaIndiaSoutheast AsiaOthersMiddle East and Africa (Covered in Chapter 9 and 13)Saudi ArabiaUAEEgyptNigeriaSouth AfricaOthersSouth America (Covered in Chapter 10 and 13)BrazilArgentinaColumbiaChileOthers

Data that is collected from these different regions are comprehensively analyzed according to different methods and to identify different factors and parameters. The companies that operate in these different regions and occupy a large market share are also analyzed to identify new and improved methods to increase sales.

Method of Research

The data presented in the report is analyzed according to a number of tests that determine various information and conclusions from the collected data. One of the major analysis methods that is commonly used is the SWOT analysis. This is used to identify and categorize the data collected according to different parameters. The strengths and weaknesses of the different organizations mentioned in the report are identified and suitable alternatives and solutions are suggested. The threats that an organization faces are also included and they can be either from competitors or due to failed marketing ideas and more. The list of opportunities relevant to a certain organization and their role in the Artificial Intelligence for Edge Devices Market is identified as they can play a major role in either increasing the market share of the company or the revenue earned.

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Artificial Intelligence for Edge Devices Market Capacity, Production, Revenue and Forecast (2020-2026)| Impact of Worldwide COVID-19 Spread Analysis...

Mindtree, a Larsen & Toubro Group Company, was a Gold Sponsor at this years virtual ARC European Industry Forum. Business strategies and models are undergoing a sea change, and it is clear that flexibility, agility, and innovative approaches are required to succeed during these uncertain times. After giving a brief overview, Prabhu Venkatramanan, Head Digital Technology and Solutions, Larsen & Toubro, spoke about the applications and use cases of artificial intelligence (AI). His entire presentation can be viewed here.

India headquartered Larsen & Toubro, with a revenue of $21 billion and presence in over 30 countries, specializes in technology, engineering, heavy manufacturing, and construction, which is one of their largest businesses. However, surveys and reports revealed that the construction industry was a laggard and there was huge potential to improve processes. Hence, it became imperative to understand how digital interventions can improve productivity and efficiency. Focus was on major areas, such as safety of construction equipment and the associated workers. Another aspect was to gauge project progress across multiple global locations with different quality requirements and restrictions. A wide variety of technologies were used to do all this, especially IoT for the connected equipment.

These applications have been running since the digital transformation initiative began four years ago. So, there is a huge amount of data, which is a gold mine, said Mr. Venkatramanan. Armed with this data, they wanted to optimize operations and organizational efficiency. And that is when artificial intelligence made an entry.

The companys Alchemy Construction Intelligence Platform is an analytics engine to identify patterns and generate actionable intelligence.

As most of the projects are distributed across multiple regions, it is tedious for the personnel to read and understand the quality and safety manuals, policies and so on. This can be done using speech; the user can ask the query to a bot in the mobile, and the bot responds to it from the SLP (speech language processing) document side. This bot is connected to all the different digital systems, the safety and quality platforms, and a system for digital stores for looking at material stock availability, material reorder status, minimum availability etc. For personnel on the move or handling many projects, the required information is just a question away. Theres also a screen that accompanies the speech, and this provides the contextual trend and information.

A lot of work is being done in the area of comprehension using natural language understanding (NLU). The company works on many tender documents, and some of the business units work on 20-30 tenders (each is about 1,000-2,000 pages) every week. An AI model has been developed to turn out a three-page summary in three minutes listing the risky processes. This is a tremendous improvement in how fast tenders can be assessed and awarded to contractors.

In conclusion, Mr. Venkatramanan said that the companys focus is on predictive maintenance and managing resources intelligently by using AI. Moving ahead, they would like to be able to predict project delays, but this will involve analyzing thousands of parameters that impact the project.

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Applications and Use Cases of Artificial Intelligence| ARC Advisory - ARC Viewpoints

Global Artificial Intelligence in Transportation Market was valued at USD 1.85 billion in 2018 which is expected to reach USD 4.8 billion by 2027 at a CAGR 17.3%.

Artificial intelligence technology is the computer operated task which involves human intelligence such as solving complex problem solving, and decision making. Global artificial intelligence in transportation market is exponentially growing due to government rules and regulations for vehicle safety and security and implementation of enhanced driver assistance systems.

However, high cost of artificial intelligence systems and high infrastructure cost are the major challenges for the growth of artificial intelligence in transportation market. Also, machine learning is data driven and cyber security and data privacy will affect the global artificial intelligence in transportation market growth.

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Market Key Players

Various key players are discussed in this report such as Daimler, Scania, Paccar, Man, Volvo, Continental, Magna, Bosch, Valeo, and ZF.

Market Taxonomy

By Offering

By Technology

By Process

By Region

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Global Artificial Intelligence in Transportation Market Deployment Model, Organization Size, Vertical, and Region Global Forecast to 2027 - 3rd Watch...