The latest report onArtificial Intelligence and Machine Learning Marketgives a broad assessment of the global Artificial Intelligence and Machine Learning market by categorizing it in terms applications, types, and regions. The report gives a detailed analysis on competitive landscape and strategies that influenced the market in a positive way. Further, the report gives an overview of current market dynamics by studying various key segments based on the product, types, applications, end-to-end industries and market scenario.

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Major Key Playersof Artificial Intelligence and Machine Learning Market Report:

AnkiAIBrainLuminosoAmazonGoogleIBMDeepmindFacebookQualcommCloudMindsIris AIApple

Scope of the Artificial Intelligence and Machine Learning Market Report:

Artificial Intelligence and Machine Learning market research report focuses on demand and supply analysis at the global regional and domestic level. Considering the global perspective, the report presents overall Artificial Intelligence and Machine Learning market by size by analyzing historical data and future prospective. The report focuses on several key regions includingNorth America, Europe, Asia-Pacific and RoW.

2020 has been considered as the base year and the report gives market estimation for the period 2020 to 2027. The report studies the worldwide Artificial Intelligence and Machine Learning market (size, capacity, production and consumption) in key regions.

Make an Inquiry About This Report @ https://www.globalmarketers.biz/report/others/2015-2027-global-artificial-intelligence-and-machine-learning-industry-market-research-report,-segment-by-player,-type,-application,-marketing-channel,-and-region/145884#inquiry_before_buying

For product type segment, this report listed main product type of Artificial Intelligence and Machine Learning market

Deep LearningNatural Language ProcessingMachine VisionOthers

For application segment, this report focuses on the status and outlook for key applications. End users are also listed.

BFSIHealthcare and Life SciencesRetailTelecommunicationGovernment and DefenseManufacturingEnergy and UtilitiesOthers

Place Inquiry for Buying or Customization of Report:https://www.globalmarketers.biz/inquiry/customization/145884

Furtherin the Artificial Intelligence and Machine Learning Market research reports,followingpoints are included along within-depthstudy of each point:

Supply Chain Analysis Production of the Artificial Intelligence and Machine Learning is analyzed with respect to different regions, types and applications. Here, price and revenue analysis of various Artificial Intelligence and Machine Learning Market key players is also covered.

Demand and Consumption Analysis This part of the report thoroughly studiesdemand and consumption for the Artificial Intelligence and Machine Learning Market. This part also sheds light on the gap between demand supply and consumption pattern throughout the globe. Import and export analysis are also given in this part.

Key Strategic DevelopmentsThe study also includes the key strategic developments of the Artificial Intelligence and Machine Learning market, comprising product portfolio, which details production, revenue, price, market share and growth rate on the basis of product diversification. Additionally, the report studies sales volume, market share and growth rate on the basis of applications/end users for each application. The product diversification also includes SWOT and PEST analysis to understand the regional product segmentation market.

Artificial Intelligence and Machine LearningMarket Report Includes:

Market Outlook:Status and Dynamics.

Competitive Landscape:By Manufacturers, Vendors and Development Trends.

Product Revenue for Top Players:Market Share, Size, CAGR, Current Market Situation Analysis, Future Market Forecast for the next 5 years period.

Market Segmentation:By Types, By Applications, By End-Users, By Regions/ Geography.

Sales Revenue:Market Share, Price and Cost Analysis, Growth Rate, Current Market Analysis.

Check Table of Contents of This Report @ https://www.globalmarketers.biz/report/others/2015-2027-global-artificial-intelligence-and-machine-learning-industry-market-research-report,-segment-by-player,-type,-application,-marketing-channel,-and-region/145884#table_of_contents

Benefits of Purchasing Artificial Intelligence and Machine Learning Market Report:

Analyst Support:Get your query fixed from our expert analysts before and after buying the report.

Customers Satisfaction:Our expert team will assist you with all your research needs and customize the report.

Incomparable Expertise:Analysts will provide deep understandings into the reports.

Assured Quality:We focus on the quality and accuracy of the report.

The latest report onArtificial Intelligence and Machine Learning Marketgives a broad assessment of the global Artificial Intelligence and Machine Learning market by categorizing it in terms applications, types, and regions. The report gives a detailed analysis on competitive landscape and strategies that influenced the market in a positive way. Further, the report gives an overview of current market dynamics by studying various key segments based on the product, types, applications, end-to-end industries and market scenario.

Request a sample of this report @https://www.globalmarketers.biz/report/others/2015-2027-global-artificial-intelligence-and-machine-learning-industry-market-research-report,-segment-by-player,-type,-application,-marketing-channel,-and-region/145884#request_sample

Major Key Playersof Artificial Intelligence and Machine Learning Market Report:

AnkiAIBrainLuminosoAmazonGoogleIBMDeepmindFacebookQualcommCloudMindsIris AIApple

Scope of the Artificial Intelligence and Machine Learning Market Report:

Artificial Intelligence and Machine Learning market research report focuses on demand and supply analysis at the global regional and domestic level. Considering the global perspective, the report presents overall Artificial Intelligence and Machine Learning market by size by analyzing historical data and future prospective. The report focuses on several key regions includingNorth America, Europe, Asia-Pacific and RoW.

2020 has been considered as the base year and the report gives market estimation for the period 2020 to 2027. The report studies the worldwide Artificial Intelligence and Machine Learning market (size, capacity, production and consumption) in key regions.

Make an Inquiry About This Report @ https://www.globalmarketers.biz/report/others/2015-2027-global-artificial-intelligence-and-machine-learning-industry-market-research-report,-segment-by-player,-type,-application,-marketing-channel,-and-region/145884#inquiry_before_buying

For product type segment, this report listed main product type of Artificial Intelligence and Machine Learning market

Deep LearningNatural Language ProcessingMachine VisionOthers

For application segment, this report focuses on the status and outlook for key applications. End users are also listed.

BFSIHealthcare and Life SciencesRetailTelecommunicationGovernment and DefenseManufacturingEnergy and UtilitiesOthers

Place Inquiry for Buying or Customization of Report:https://www.globalmarketers.biz/inquiry/customization/145884

Furtherin the Artificial Intelligence and Machine Learning Market research reports,followingpoints are included along within-depthstudy of each point:

Supply Chain Analysis Production of the Artificial Intelligence and Machine Learning is analyzed with respect to different regions, types and applications. Here, price and revenue analysis of various Artificial Intelligence and Machine Learning Market key players is also covered.

Demand and Consumption Analysis This part of the report thoroughly studiesdemand and consumption for the Artificial Intelligence and Machine Learning Market. This part also sheds light on the gap between demand supply and consumption pattern throughout the globe. Import and export analysis are also given in this part.

Key Strategic DevelopmentsThe study also includes the key strategic developments of the Artificial Intelligence and Machine Learning market, comprising product portfolio, which details production, revenue, price, market share and growth rate on the basis of product diversification. Additionally, the report studies sales volume, market share and growth rate on the basis of applications/end users for each application. The product diversification also includes SWOT and PEST analysis to understand the regional product segmentation market.

Artificial Intelligence and Machine LearningMarket Report Includes:

Market Outlook:Status and Dynamics.

Competitive Landscape:By Manufacturers, Vendors and Development Trends.

Product Revenue for Top Players:Market Share, Size, CAGR, Current Market Situation Analysis, Future Market Forecast for the next 5 years period.

Market Segmentation:By Types, By Applications, By End-Users, By Regions/ Geography.

Sales Revenue:Market Share, Price and Cost Analysis, Growth Rate, Current Market Analysis.

Check Table of Contents of This Report @ https://www.globalmarketers.biz/report/others/2015-2027-global-artificial-intelligence-and-machine-learning-industry-market-research-report,-segment-by-player,-type,-application,-marketing-channel,-and-region/145884#table_of_contents

Benefits of Purchasing Artificial Intelligence and Machine Learning Market Report:

Analyst Support:Get your query fixed from our expert analysts before and after buying the report.

Customers Satisfaction:Our expert team will assist you with all your research needs and customize the report.

Incomparable Expertise:Analysts will provide deep understandings into the reports.

Assured Quality:We focus on the quality and accuracy of the report.

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Global Artificial Intelligence and Machine Learning Market Market 2020 Analysis by Type, Application, Geography and New Technology Development Report...

The report titled, Machine Learning For Managing Diabetes Market boons an in-depth synopsis of the competitive landscape of the market globally, thus helping establishments understand the primary threats and prospects that vendors in the market are dealt with. It also incorporates thorough business profiles of some of the prime vendors in the market. The report includes vast data relating to the recent discovery and technological expansions perceived in the market, wide-ranging with an examination of the impact of these intrusions on the markets future development.

Machine Learning For Managing Diabetes Market research reports growth rates and the market value based on market dynamics, growth factors. The complete knowledge is based on the latest innovations in the industry, opportunities, and trends. In addition to SWOT analysis by key suppliers, the report contains a comprehensive market analysis and major players landscape.

Ask for Sample Copy of This Report: https://www.healthcareintelligencemarkets.com/request_sample.php?id=29107

Top Key Players Included in This Report:

Allscripts Healthcare Solutions, Inc., Orion Health, Medecision, Inc., Emmi Solutions LLC, Mckesson Corporation, Cerner Corporation and Getwellnetwork, Inc.

Major highlights of this research report:

The report on the Machine Learning For Managing Diabetes Market has newly added by IT Intelligence Markets to its huge repository. The global market is expected to increase from 2020 to 2027. Primary and secondary research methodologies have been used for curating this research report.

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The competitive landscape of the Machine Learning For Managing Diabetes Market is described in terms of the players and their statistics. For each key player, the report reveals production rates, costing, overall pricing, revenue generation, and market share within the Machine Learning For Managing Diabetes Market.

The research on the Machine Learning For Managing Diabetes Market will be applicable to investors, business owners, industry experts, and various c level peoples. Profiling of the several top-level industries has been included in this informative report.

The research study has taken the help of graphical presentation techniques such as infographics, charts, tables, and pictures. It provides guidelines for both established players and new entrants in the Machine Learning For Managing Diabetes Market.

*If you have any special requirements, please let us know and we will offer you the report as per your requirements.

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Table of Contents:

About Us:

HealthCare Intelligence Markets Reports provides market intelligence & consulting services to a global clientele spread over 145 countries. Being a B2B firm, we help businesses to meet the challenges of an ever evolving market with unbridled confidence. We craft customized and syndicated market research reports that help market players to build game changing strategies. Besides, we also provide upcoming trends & future market prospects in our reports pertaining to Drug development, Clinical & healthcare industries. Our intelligence enables our clients to take decisions with which in turn proves a game-changer for them. We constantly strive to serve our clients better by directly allowing them sessions with our research analysts so the report is at par with their expectations.

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Latest Report on Machine Learning For Managing Diabetes Market (COVID 19 Updated) Climbs on Positive Outlook of Excellent Growth by 2027: Allscripts...

H2O.ai and SAS Institute

The scope of the Report:

The report analyzes the key opportunities, CAGR, and Y-o-Y growth rates to allow readers to understand all the qualitative and quantitative aspects of the Machine Learning market. A competition analysis is imperative in the Machine Learning market and the competition landscape serves this objective. A wide company overview, financials, recent developments, and long and short-term strategies adopted are par for the course. Various parameters have been taken into account while estimating market size. The revenue generated by the leading industry participants in the sales of Machine Learning across the world has been calculated through primary and secondary research. The Machine Learning Market analysis is provided for the international markets including development trends, competitive landscape analysis, and key regions development status.

By Regions:

* North America (The US, Canada, and Mexico)

* Europe (Germany, France, the UK, and Rest of the World)

* Asia Pacific (China, Japan, India, and Rest of Asia Pacific)

* Latin America (Brazil and Rest of Latin America.)

* Middle East & Africa (Saudi Arabia, the UAE, , South Africa, and Rest of Middle East & Africa)

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Highlights of the Machine Learning market study:

Speculations for sales:

The report contains historical revenue and volume that backing information about the market capacity, and it helps to evaluate conjecture numbers for key areas in the Machine Learning market. Additionally, it includes a share of every segment of the Machine Learning market, giving methodical information about types and applications of the market.

Key point summary of the Machine Learning market report:

This report gives a forward-looking prospect of various factors driving or restraining market growth.

It presents an in-depth analysis of changing competition dynamics and puts you ahead of competitors.

It gives a six-year forecast evaluated on the basis of how the market is predicted to grow.

It assists in making informed business decisions by creating a pin-point analysis of market segments and by having complete insights of the Machine Learning market.

This report helps users in comprehending the key product segments and their future.

Strategic Points Covered in TOC:

Chapter 1: Introduction, market driving force product scope, market risk, market overview, and market opportunities of the global Machine Learning market

Chapter 2: Evaluating the leading manufacturers of the global Machine Learning market which consists of its revenue, sales, and price of the products

Chapter 3: Displaying the competitive nature among key manufacturers, with market share, revenue, and sales

Chapter 4: Presenting global Machine Learning market by regions, market share and with revenue and sales for the projected period

Chapter 5, 6, 7, 8 and 9: To evaluate the market by segments, by countries and by manufacturers with revenue share and sales by key countries in these various regions

Finally, the report global Machine Learning market describes Machine Learning industry expansion game plan, the Machine Learning industry knowledge supply, appendix, analysis findings and the conclusion. It includes a through explanation of the cutting-edging technologies and investments being made to upgrade the existing ones.

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Machine Learning Market Future Growth by In Depth Industry Analysis, Size, Trends and Forecast to 2026 - Cole of Duty

The global healthcare industry is booming. As per recent research, it is expected to cross the $2 trillion mark this year, despite the sluggish economic outlook and global trade tensions. Human beings, in general, are living longer and healthier lives.

There is increased awareness about living organ donation. Robots are being used for gallbladder removals, hip replacements, and kidney transplants. Early diagnosis of skin cancers with minimum human error is a reality. Breast reconstructive surgeries have enabled breast cancer survivors to partake in rebuilding their glands.

All these jobs were unthinkable sixty years ago. Now is an exciting time for the global health care sector as it progresses along its journey for the future.

However, as the worldwide population of 7.7 billion is likely to reach 8.5 billion by 2030, meeting health needs could be a challenge. That is where significant advancements in machine learning (ML) can help identify infection risks, improve the accuracy of diagnostics, and design personalized treatment plans.

source: Deloitte Insights 2020 global health care outlook

In many cases, this technology can even enhance workflow efficiency in hospitals. The possibilities are endless and exciting, which brings us to an essential segment of the article:

Do you understand the concept of the LACE index?

Designed in Ontario in 2004, it identifies patients who are at risk of readmission or death within 30 days of being discharged from the hospital. The calculation is based on four factors length of stay of the patient in the hospital, acuity of admission, concurring diseases, and emergency room visits.

The LACE index is widely accepted as a quality of care barometer and is famously based on the theory of machine learning. Using the past health records of the patients, the concept helps to predict their future state of health. It enables medical professionals to allocate resources on time to reduce the mortality rate.

This technological advancement has started to lay the foundation for closer collaboration among industry stakeholders, affordable and less invasive surgery options, holistic therapies, and new care delivery models. Here are five examples of current and emerging ML innovations:

From the initial screening of drug compounds to calculating the success rates of a specific medicine based on physiological factors of the patients the Knight Cancer Institute in Oregon and Microsofts Project Hanover are currently applying this technology to personalize drug combinations to cure blood cancer.

Machine learning has also given birth to new methodologies such as precision medicine and next-generation sequencing that can ensure a drug has the right effect on the patients. For example, today, medical professionals can develop algorithms to understand disease processes and innovative design treatments for ailments like Type 2 diabetes.

Signing up volunteers for clinical trials is not easy. Many filters have to be applied to see who is fit for the study. With machine learning, collecting patient data such as past medical records, psychological behavior, family health history, and more is easy.

In addition, the technology is also used to monitor biological metrics of the volunteers and the possible harm of the clinical trials in the long-run. With such compelling data in hand, medical professionals can reduce the trial period, thereby reducing overall costs and increasing experiment effectiveness.

Every human body functions differently. Reactions to a food item, medicine, or season differ. That is why we have allergies. When such is the case, why is customizing the treatment options based on the patients medical data still such an odd thought?

Machine learning helps medical professionals determine the risk for each patient, depending on their symptoms, past medical records, and family history using micro-bio sensors. These minute gadgets monitor patient health and flag abnormalities without bias, thus enabling more sophisticated capabilities of measuring health.

Cisco reports that machine-to-machine connection in global healthcare is growing at a rate of 30% CAGR which is the highest compared to any other industry!

Machine learning is mainly used to mine and analyze patient data to find out patterns and carry out the diagnosis of so many medical conditions, one of them being skin cancer.

Over 5.4mn people in the US are diagnosed with this disease annually. Unfortunately, the diagnosis is a virtual and time-taking process. It relies on long clinical screenings, comprising a biopsy, dermoscopy, and histopathological examination.

But machine learning changes all that. Moleanalyzer, an Australia-based AI software application, calculates and compares the size, diameter, and structure of the moles. It enables the user to take pictures at predefined intervals to help differentiate between benign and malignant lesions on the skin.

The analysis lets oncologists confirm their skin cancer diagnosis using evaluation techniques combined with ML, and they can start the treatment faster than usual. Where experts could identify malignant skin tumors, only 86.6% correctly, Moleanalyzer successfully detected 95%.

Healthcare providers have to ideally submit reports to the government with necessary patient records that are treated at their hospitals.

Compliance policies are continually evolving, which is why it is even more critical to ensure the hospital sites to check if they are being compliant and functioning within the legal boundaries. With machine learning, it is easy to collect data from different sources, using different methods and formatting them correctly.

For data managers, comparing patient data from various clinics to ensure they are compliant could be an overwhelming process. Machine learning helps gather, compare, and maintain that data as per the standards laid down by the government, informs Dr. Nick Oberheiden, Founder and Attorney, Oberheiden P.C.

The healthcare industry is steadily transforming through innovative technologies like AI and ML. The latter will soon get integrated into practice as a diagnostic aid, particularly in primary care. It plays a crucial role in shaping a predictive, personalized, and preventive future, making treating people a breeze. What are your thoughts?

Image: Depositphotos.com

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How Machine Learning Is Redefining The Healthcare Industry - Small Business Trends

Zindi the data science platform that connects data scientists and people who need their problems solved is hosting a free webinar titled From Models to Medical Care: in conversation with epidemiologists on the scientific frontlines.

The webinar will see 3 epidemiologists; Prof Wim Delva, Dr Brooke Nichols and Dr Elaine Nsoesie discuss how machine learning models are put into practice in the fight against COVID-19.

Zindis data scientist Johno Whitaker will also discuss Machine Learning approaches from one of their recently held competitions along with a question and answer session.

For data scientists and those in healthcare this will be an insightful webinar to tune into. The Zoom webinar will be hosted on the 5th of May from 5 PM- 7 PM.

If anything goes wrong, click here to enter your query.

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African AI Platform To Host Webinar On How Machine Learning Can Be Used To Fight COVID-19 - Technology Zimbabwe

Intelligent tutoring systems have been shown to be effective in helping to teach certain subjects, such as algebra or grammar, but creating these computerized systems is difficult and laborious. Now, researchers at Carnegie Mellon University have shown they can rapidly build them by, in effect, teaching the computer to teach.Using a new method that employs artificial intelligence, a teacher can teach the computer by demonstrating several ways to solve problems in a topic, such as multicolumn addition, and correcting the computer if it responds incorrectly.

Notably, the computer system learns to not only solve the problems in the ways it was taught, but also to generalize to solve all other problems in the topic, and do so in ways that might differ from those of the teacher, said Daniel Weitekamp III, a Ph.D. student in CMU's Human-Computer Interaction Institute (HCII).

"A student might learn one way to do a problem and that would be sufficient," Weitekamp explained. "But a tutoring system needs to learn every kind of way to solve a problem." It needs to learn how to teach problem solving, not just how to solve problems.

That challenge has been a continuing problem for developers creating AI-based tutoring systems, said Ken Koedinger, professor of human-computer interaction and psychology. Intelligent tutoring systems are designed to continuously track student progress, provide next-step hints and pick practice problems that help students learn new skills.

When Koedinger and others began building the first intelligent tutors, they programmed production rules by hand a process, he said, that took about 200 hours of development for each hour of tutored instruction. Later, they would develop a shortcut, in which they would attempt to demonstrate all possible ways of solving a problem. That cut development time to 40 or 50 hours, he noted, but for many topics, it is practically impossible to demonstrate all possible solution paths for all possible problems, which reduces the shortcut's applicability.

The new method may enable a teacher to create a 30-minute lesson in about 30 minutes, which Koedinger termed "a grand vision" among developers of intelligent tutors.

"The only way to get to the full intelligent tutor up to now has been to write these AI rules," Koedinger said. "But now the system is writing those rules."

A paper describing the method, authored by Weitekamp, Koedinger and HCII System Scientist Erik Harpstead, was accepted by the Conference on Human Factors in Computing Systems (CHI 2020), which was scheduled for this month but canceled due to the COVID-19 pandemic. The paper has now been published in the conference proceedings in the Association for Computing Machinery's Digital Library.

The new method makes use of a machine learning program that simulates how students learn. Weitekamp developed a teaching interface for this machine learning engine that is user friendly and employs a "show-and-correct" process that's much easier than programming.

For the CHI paper, the authors demonstrated their method on the topic of multicolumn addition, but the underlying machine learning engine has been shown to work for a variety of subjects, including equation solving, fraction addition, chemistry, English grammar and science experiment environments.

The method not only speeds the development of intelligent tutors, but promises to make it possible for teachers, rather than AI programmers, to build their own computerized lessons. Some teachers, for instance, have their own preferences on how addition is taught, or which form of notation to use in chemistry. The new interface could increase the adoption of intelligent tutors by enabling teachers to create the homework assignments they prefer for the AI tutor, Koedinger said.

Enabling teachers to build their own systems also could lead to deeper insights into learning, he added. The authoring process may help them recognize trouble spots for students that, as experts, they don't themselves encounter.

"The machine learning system often stumbles in the same places that students do," Koedinger explained. "As you're teaching the computer, we can imagine a teacher may get new insights about what's hard to learn because the machine has trouble learning it."ReferenceWeitekamp et al. (2020). An Interaction Design for Machine Teaching to Develop AI Tutors. CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. DOI: https://doi.org/10.1145/3313831.3376226

This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source.

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Are Tutor Computers The Future of Algebra and Grammar Lessons? - Technology Networks

Originally published in Medium, April 28, 2020

The landscape is evolving quickly. Machine Learning will transition to a commonplace part of every Software Engineers toolkit.

In every field we get specialized roles in the early days, replaced by the commonplace role over time. It seems like this is another case of just that.

Lets unpack.

Machine Learning Engineer as a role is a consequence of the massive hype fueling buzzwords like AI and Data Science in the enterprise. In the early days of Machine Learning, it was a very necessary role. And it commanded a nice little pay bump for many! But Machine Learning Engineer has taken on many different personalities depending on who you ask.

The purists among us say a Machine Learning Engineer is someone who takes models out of the lab and into production. They scale Machine Learning systems, turn reference implementations into production-ready software, and oftentimes cross over into Data Engineering. Theyre typically strong programmers who also have some fundamental knowledge of the models they work with.

But this sounds a lot like a normal software engineer.

Ask some of the top tech companies what Machine Learning Engineer means to them and you might get 10 different answers from 10 survey participants. This should be unsurprising. This is a relatively young role and the folks posting these jobs are managers, oftentimes of many decades who dont have the time (or will) to understand the space.

To continue reading this article, click here.

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Machine Learning Engineers Will Not Exist In 10 Years - Machine Learning Times - machine learning & data science news - The Predictive Analytics...

The novel Coronavirus (COVID-19) has caused a slowdown in the global economy and disrupted the stock markets. Hence, companies in the Artificial Intelligence & Advanced Machine Learning market are tapping incremental opportunities via alternative business solutions to revive market growth post the lockdown period. Get a full analysis report on the impact of Coronavirus which has affected the Artificial Intelligence & Advanced Machine Learning market and learn how businesses are tackling the situation.

Assessment of the Global Artificial Intelligence & Advanced Machine Learning Market

According to the latest report on the Artificial Intelligence & Advanced Machine Learning market, the market is expected to reach a value of ~US$XX by 20XX and register a CAGR growth of ~XX% during the forecast period (20XX-20XX). The report provides a thorough understanding of the various factors that are expected to influence the current and future prospects of the Artificial Intelligence & Advanced Machine Learning market including the major trends, growth opportunities, restraints, and drivers.

The SWOT and Porters Five Forces Analysis by analysts of marketresearchhub.us offers a fair idea of the operations of some of the key players operating in the Artificial Intelligence & Advanced Machine Learning market. The current structure of the market and the estimated growth of the market over the forecast period is accurately represented in the report along with graphs, figures, and tables.

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Segregation of the Artificial Intelligence & Advanced Machine Learning Market:

The key players covered in this studyiCarbonXJiboNext ITPrisma LabsAIBrainQuadratyxNVIDIAInbentaNumentaIntel

Market segment by Type, the product can be split intoSmart WalletsVoice-Assisted BankingMarket segment by Application, split intoInsuranceBanking and Capital Markets

Market segment by Regions/Countries, this report coversNorth AmericaEuropeChinaJapanSoutheast AsiaIndiaCentral & South America

The study objectives of this report are:To analyze global Artificial Intelligence & Advanced Machine Learning status, future forecast, growth opportunity, key market and key players.To present the Artificial Intelligence & Advanced Machine Learning development in North America, Europe, China, Japan, Southeast Asia, India and Central & South America.To strategically profile the key players and comprehensively analyze their development plan and strategies.To define, describe and forecast the market by type, market and key regions.

In this study, the years considered to estimate the market size of Artificial Intelligence & Advanced Machine Learning are as follows:History Year: 2015-2019Base Year: 2019Estimated Year: 2020Forecast Year 2020 to 2026For the data information by region, company, type and application, 2019 is considered as the base year. Whenever data information was unavailable for the base year, the prior year has been considered.

The report includes a Y-o-Y growth assessment of each of these market segments and sub-segments. Further, the market share, size, revenue growth, and CAGR growth of each segment is accurately presented in the in-depth study of the Artificial Intelligence & Advanced Machine Learning market.

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Valuable Insights Enclosed in the Report

The presented study resolves the following doubts related to the Artificial Intelligence & Advanced Machine Learning market:

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Global trade impact of the Coronavirus Artificial Intelligence & Advanced Machine Learning Market size and Key Trends in terms of volume and value...

Amazon

Global AI/Machine Learning Market: Competitive Landscape

This section of the report lists various major manufacturers in the market. The competitive analysis helps the reader understand the strategies and collaborations that players focus on in order to survive in the market. The reader can identify the players fingerprints by knowing the companys total sales, the companys total price, and its production by company over the 2020-2026 forecast period.

Global AI/Machine Learning Market: Regional Analysis

The report provides a thorough assessment of the growth and other aspects of the AI/Machine Learning market in key regions, including the United States, Canada, Italy, Russia, China, Japan, Germany, and the United Kingdom United Kingdom, South Korea, France, Taiwan, Southeast Asia, Mexico, India and Brazil, etc. The main regions covered by the report are North America, Europe, the Asia-Pacific region and Latin America.

The AI/Machine Learning market report was prepared after various factors determining regional growth, such as the economic, environmental, technological, social and political status of the region concerned, were observed and examined. The analysts examined sales, production, and manufacturer data for each region. This section analyzes sales and volume by region for the forecast period from 2020 to 2026. These analyzes help the reader understand the potential value of investments in a particular country / region.

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Key Benefits for Stakeholders:

The report provides an in-depth analysis of the size of the AI/Machine Learning world market, as well as recent trends and future estimates, in order to clarify the upcoming investment pockets.

The report provides data on key growth drivers, constraints and opportunities, as well as their impact assessment on the size of the AI/Machine Learning market.

Porters 5 Strength Rating shows how effective buyers and suppliers are in the industry.

The quantitative analysis of the AI/Machine Learning world industry from 2020 to 2026 is provided to determine the potential of the AI/Machine Learning market.

This AI/Machine Learning Market Report Answers To Your Following Questions:

Who are the main global players in this AI/Machine Learning market? What is the profile of your company, its product information, its contact details?

What was the status of the global market? What was the capacity, the production value, the cost and the profit of the market?

What are the forecasts of the global industry taking into account the capacity, the production and the value of production? How high is the cost and profit estimate? What will be the market share, supply, and consumption? What about imports and export?

What is market chain analysis by upstream raw materials and downstream industry?

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Tags: AI/Machine Learning Market Size, AI/Machine Learning Market Trends, AI/Machine Learning Market Growth, AI/Machine Learning Market Forecast, AI/Machine Learning Market Analysis

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AI/Machine Learning Market Size by Top Key Players, Growth Opportunities, Incremental Revenue , Outlook and Forecasts to 2026 - Latest Herald

In 2004, a few unmanned vehicles showed up at the starting gate of the lengthy course across the Mojave Desert this was the inaugural DARPA Grand Challenge. It signified the beginning of the technological race to develop a practical self-driving car, which sparked a global movement that continues even today.

The networking community too embarked on a similar journey to provide production-ready, economically feasible, Self-Driving Networks. Self-Driving Networks are autonomous networks that use Artificial Intelligence (AI) and Machine Learning (ML) to program independently and carry out prescribed intentions while eliminating complex programming and management tasks required today to run the networks. In view of this, the proliferation of data breaches and cyberattacks in todays networking environment has also increased, leading to extensive repercussions across businesses. As such, ML-based security solutions have become a major cybersecurity investment for organizations today.

By Rohit Sawhney Systems Engineering Manager at Juniper Networks India

Many experts believe that AI and ML will dominate cybersecurity in the future. Last year, at the Gartner IT Symposium/Xpo, analysts discussed how these two technologies will augment human decision-making, emotions, and relationships.

Rapid technological advances are enabling AI to disrupt the networking industry with new insights and automation. AI in the networking domain will be able to reduce IT costs and offer the best possible user experience. Not only will AI be able to reduce IT costs, but it will also bring in more productivity and efficiency in networking. Together, machine learning and AI could be key enablers, helping to reduce human effort and make cybersecurity faster, more consistent and accurate.

In fact, many Enterprises are already making greater investments to integrate solutions with machine learning algorithms into their existing security infrastructure. While traditional antivirus programs are still widely used to detect and neutralize threats, they do not have the capability to detect and mitigate sophisticated threats. ML-based security solutions like the Juniper ATP can help monitor potential threats in the network through threat intelligence features allowing IT security teams to detect any suspicious activity before the attack occurs.

AI comes to the rescue as it reduces the number of monotonous tasks that take up an engineers time, while ensuring they are always completed accurately, regardless of frequency and quantity. This allows engineers to focus on other business strategic tasks while maintaining network health and safety.

In a recent survey conducted by KPMG for its report, Living in an AI World 2020, analysts found that 92% of respondents agree that leveraging spectrum of AI technologies will make their companies run more efficiently. However, in the networking domain, IT simply cant meet the needs of todays stringent network requirements, without a robust AI strategy. The following are some technology elements that an AI strategy should include:

ML a subset of AI, is a prerequisite for any successful deployment of AI technologies. ML uses algorithms to parse data, learn from it, and determines or predicts without requiring explicit instructions. With that said, AI/ML can be leveraged for the following tasks in the networking domain:

About the Author

Rohit Sawhney is a Systems Engineering Manager at Juniper Networks India. He leads the team of Technical Consultants supporting Junipers North/East India & SAARC business. Prior to joining Juniper Networks, he has worked with IBM India and has industry experience of over 20 years. Rohit is a certified by Juniper Networks, Cisco and VMWare. He holds a masters degree in Computer Application from Sikkim Manipal University of Health, Medical and Technological Sciences and a Bachelors of Science in Electronics from Delhi University.

Disclaimer

CISO MAG did not evaluate/test the products mentioned in this article, nor does it endorse any of the claims made by the writer. The facts, opinions, and language in the article do not reflect the views of CISO MAG and CISO MAG does not assume any responsibility or liability for the same. CISO MAG does not guarantee the satisfactory performance of the products mentioned in this article.

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How AI and ML in the networking domain strengthens security - CISO MAG