Mediaboss Marketing

Reports published inMarket Research Incfor the Cloud Machine Learning market are spread out over several pages and provide the latest industry data, market future trends, enabling products and end users to drive revenue growth and profitability. Industry reports list and study key competitors and provide strategic industry analysis of key factors affecting market dynamics. This report begins with an overview of the Cloud Machine Learning market and is available throughout development. It provides a comprehensive analysis of all regional and major player segments that provide insight into current market conditions and future market opportunities along with drivers, trend segments, consumer behavior, price factors and market performance and estimates over the forecast period.

Request a pdf copy of this report athttps://www.marketresearchinc.com/request-sample.php?id=39704

Key Strategic Manufacturers::Oracle Corporation , Google Inc, IBM, Alibaba Group, Microsoft Corporation, Amazon Web Services , Fair, Isaac

(Market Size & Forecast, Different Demand Market by Region, Main Consumer Profile etc

The report gives a complete insight of this industry consisting the qualitative and quantitative analysis provided for this market industry along with prime development trends, competitive analysis, and vital factors that are predominant in the Cloud Machine Learning Market.

The report also targets local markets and key players who have adopted important strategies for business development. The data in the report is presented in statistical form to help you understand the mechanics. The Cloud Machine Learning market report gathers thorough information from proven research methodologies and dedicated sources in many industries.

Avail 40% Discount on this report athttps://www.marketresearchinc.com/ask-for-discount.php?id=39704

Key Objectives of Cloud Machine Learning Market Report: Study of the annual revenues and market developments of the major players that supply Cloud Machine Learning Analysis of the demand for Cloud Machine Learning by component Assessment of future trends and growth of architecture in the Cloud Machine Learning market Assessment of the Cloud Machine Learning market with respect to the type of application Study of the market trends in various regions and countries, by component, of the Cloud Machine Learning market Study of contracts and developments related to the Cloud Machine Learning market by key players across different regions Finalization of overall market sizes by triangulating the supply-side data, which includes product developments, supply chain, and annual revenues of companies supplying Cloud Machine Learning across the globe.

Furthermore, the years considered for the study are as follows:

Historical year 2016-2020

Base year 2020

Forecast period 2021to 2029

Table of Content:

Cloud Machine Learning Market Research ReportChapter 1: Industry OverviewChapter 2: Analysis of Revenue by ClassificationsChapter 3: Analysis of Revenue by Regions and ApplicationsChapter 6: Analysis of Market Revenue Market Status.Chapter 4: Analysis of Industry Key ManufacturersChapter 5: Marketing Trader or Distributor Analysis of Market.Chapter 6: Development Trend of Cloud Machine Learning market

Continue for TOC

If You Have Any Query, Ask Our Experts:https://www.marketresearchinc.com/enquiry-before-buying.php?id=39704

About Us

Market Research Inc is farsighted in its view and covers massive ground in global research. Local or global, we keep a close check on both markets. Trends and concurrent assessments sometimes overlap and influence the other. When we say market intelligence, we mean a deep and well-informed insight into your products, market, marketing, competitors, and customers. Market research companies are leading the way in nurturing global thought leadership. We help your product/service become the best they can with our informed approach.

Contact Us

Market Research Inc

Kevin

51 Yerba Buena Lane, Ground Suite,

Inner Sunset San Francisco, CA 94103, USA

Call Us:+1 (628) 225-1818

Write Us@sales@marketresearchinc.com

https://www.marketresearchinc.com

View post:
Cloud Machine Learning Market: Indoor Applications Projected to be the Most Attractive Segment during 2021-2029 KSU | The Sentinel Newspaper - KSU |...

Lately, there has been a lot of discussion about the direction in which artificial intelligence is going and how much good and bad it brings to us as humans. It is questionable if machine learning will, eventually, reach a stage where human minds might become obsolete.

But, there is one thing that we can say with certainty; there has been an exponential growth regarding artificial intelligence in the last couple of years. And, one of the main aspects of said intelligence is machine learning.

Machine learning can be found in many things today:

And, it is always with us. If you have ever allowed cookies on your device when visiting a website you have fed some sort of AI to engage in machine learning. For such a reason, this technology is bound to improve exponentially in the future.

What is Machine Learning?

Machine Learning is a subcategory of artificial intelligence whose goal is that the system itself. It is meant to recognize patterns, learn as much as possible from data, and do all that and provide solutions with minimal human intervention.

Like with everything else in life, there are certain steps that must be followed in order to perform a particular task. When it comes to this form of artificial intelligence, there are 5 basic steps:

And, unlike with human learning, the computer will cycle the steps tirelessly, providing sets of data for developers to know how to tweak performance or introduce new data.

Regretfully, without human input of contextualizing data and forming algorithms the AI itself wont know what to do with the resources it has gathered.

Forms of Machine Learning?

Generally, there are 2 main forms of machine learning.

Primarily, there is supervised learning. This is a subcategory of ML where the main goal for the algorithm is to provide adequate solutions for certain data. Most types of service software use this form to improve customer experience.

Then, there is unsupervised learning, where the algorithm is provided with only data and with no solutions. Such a system doesnt yet have direct market applications but is used to research AI and to understand how something can be developed natively.

Recommender Systems

This system functions by learning and collecting data, preferences, and interests of individual users, and thus offers services, ads, or products that are similar to what we have apparently liked before. Such automated action is saving us a lot of time and nerves, or at least is meant to.

For instance, Netflix is universally one of the most popular applications in the world for watching TV shows and movies. So if you are a user of this application, you have definitely seen a category called recommendations.

This category basically recommends you a variety of movies and shows that you would possibly enjoy based on what types of movies or shows you watched and liked. previously

Similarly, in the musical field ML is integrated by offering us new choices based on the genre we listened to before. With that information, it can recommend new songs and even entire playlists and mixes which we would potentially adore.

Another thing that we are all familiar with are games. Whether you are 7 or 77 years old, games will never go out of style.

Online gaming has been on such a rise lately and is one of the top activities in free time. So lets say you played wolfs treasure for a really long time. The machine learning algorithm will pick up on this and will recommend games of similar feature sets and volatility levels.

How is ML Present in Our Everyday Life?

There is no denying that artificial intelligence is basically everywhere and is one of the biggest innovations that made our lives better, happier, and more productive. Using machine learning is very popular in custom software development, as well as in a wide range of service software.

It is almost impossible to divide our smartphones from machine learning as it seems to be ever-present. We can find traces of the system from simple features like alarms and messaging, to very complex apps like navigation and entertainment recommendations.

Additionally, AI is also integral to both device and cybersecurity as well as for the use of AI assistants. Without the software knowing who we are and how we sound most wont be able to unlock our phones, let alone tell Siri to find us a burger joint.

With that being said, the chance that we use it in almost every aspect of our daily life without even realizing it is undeniable.

Read the rest here:
Machine Learning and where is it used? - Tech Guide

The global research report titledMachine Learning in Insurancemarket was published byMarket Research Inc. The study elucidates current market statistics, in addition to underlying future predictions of the market. The research report has been compiled by means of effective techniques such as primary and secondary research methodologies. Top level industries are enlisted in order to obtain penetrative business insights. The companies profiled in this research report include erudite information on product types, features, capacity, and productivity.

Request a sample copy of this report @:

https://www.marketresearchinc.com/request-sample.php?id=31501

Top key players:State Farm, Liberty Mutual, Allstate, Progressive, Accenture

This report provides a comprehensive analysis of(Market Size & Forecast, Different Demand Market by Region, Main Consumer Profile etc

The geographical segmentation includes study of global regions such asNorth America, Latin America, Asia-Pacific, Africa, and Europe. The report also draws attention to recent advancements in technologies and certain methodologies which further help to boost the outcome of the businesses. Furthermore, it also offers a comprehensive data of cost structure such as the cost of manpower, tools, technologies, and cost of raw material. The report is an expansive source of analytical information of different business verticals such as type, size, applications, and end-users.

Get a reasonable discount on this premium report @:https://www.marketresearchinc.com/ask-for-discount.php?id=31501

The study also elaborates on growing futuristic opportunities in order to get a clear idea about global opportunities for theMachine Learning in Insurancesector. The report focuses on some significant questions faced by different stakeholders in the businesses. The study also address various risks and challenges faced by businesses during the forecast period.

Furthermore, it emphasizes on drivers and restraints, impacting the progress of theMachine Learning in Insurancemarket. The current competitive scenario has also been studied by examining the market situations of global as well as domestic market. Finally, it also sheds light on manufacturers or service providers for a better understanding of the market.

Further information:

https://www.marketresearchinc.com/enquiry-before-buying.php?id=31501

Key Objectives of Machine Learning in Insurance Market Report:

Study of the annual revenues and market developments of the major players that supply Machine Learning in Insurance Analysis of the demand for Machine Learning in Insurance by component Assessment of future trends and growth of architecture in the Machine Learning in Insurance market Assessment of the Machine Learning in Insurance market with respect to the type of application Study of the market trends in various regions and countries, by component, of the Machine Learning in Insurance market Study of contracts and developments related to the Machine Learning in Insurance market by key players across different regions Finalization of overall market sizes by triangulating the supply-side data, which includes product developments, supply chain, and annual revenues of companies supplying Machine Learning in Insurance across the globe.

About Us

Market Research Inc is farsighted in its view and covers massive ground in global research. Local or global, we keep a close check on both markets. Trends and concurrent assessments sometimes overlap and influence the other. When we say market intelligence, we mean a deep and well-informed insight into your products, market, marketing, competitors, and customers. Market research companies are leading the way in nurturing global thought leadership. We help your product/service become the best they can with our informed approach.

Contact Us

Market Research Inc

Kevin

51 Yerba Buena Lane, Ground Suite,

Inner Sunset San Francisco, CA 94103, USA

Call Us:+1 (628) 225-1818

Write Us@sales@marketresearchinc.com

https://www.marketresearchinc.com

Go here to read the rest:
Machine Learning in Insurance Market: Indoor Applications Projected to be the Most Attractive Segment during 2021-2029 KSU | The Sentinel Newspaper -...

In this special guest feature, Aviran Yaacov, CEO, and Co-founder of EcoPlant, believes that both AI and ML technologies are making impactful strides in manufacturing, and there is no time like the present for manufacturers to get on board and explore ways to transform their processes to benefit across all fronts. Aviran has over ten years of experience and expertise in operations, finance, sales, and people management in the IT industry. Before his current role, Aviran was a Senior Sales Executive for a SAP Business One integration firm. He is part of the management team in Ecoplant since it was established in 2016. From the bootstrapping stage, he oversaw business development in the company. He generated partnerships with Ecoplants solution with large corporations including Ecolab, Dannon, Nestle, Unilever, and Hill-Rom.

Robots and machines are already everywhere, especially in manufacturing. However, many experts predicted they would have advanced faster than they have. The truth is bringing automation and dynamic controlling into the physical world turned out to be much more challenging than was previously assumed. But with state-of-the-art AI and machine learning (ML) available today, the leaps are getting larger by the day. The technology might be new, but its implementation will have various effects on manufacturing.

Better than ever before

Thanks to AI and ML technologies, machines can now learn to handle a wide range of objects and tasks on their own. These enhancements are a far cry from the robots of yesteryear, which simply performed monotonous tasks. Machines are now capable of being endowed with greater levels of intelligence to acquire new skills autonomously, and to generalize unseen situations. Its a true game-changer for the manufacturing industry as a whole in the following ways:

Newer machines can now handle a much wider range of objects and tasks like never before. For instance, 3D industrial cameras are taken to new heights with the backing of AI, as it can help machines determine depth and distance, and general image recognition in a way that was formerly exclusive to the human eye.

Since ML closely resembles human learning, the need for human intervention (such as for the creation of new programs or updates) becomes reduced as the machines are capable of handling new parts on their own. Since information is generally stored on the cloud, robots can learn from each other through shared knowledge. As more data is gathered through operation, accuracy also increases and becomes more enhanced. This translates to less of a need for surrounding equipment (such as shaker tables and feeders) to be needed for each robot, which plays a major role in savings and scalability for manufacturers.

In addition to scalability, manufacturers can also enjoy the benefits of energy efficiency with machines that are optimized accordingly. Through the usage of predictive AI algorithms to conduct ongoing energy surveys and dynamically control each air compressor, and the whole system, manufacturers can dramatically reduce the carbon footprint of their facilities.

Humans and robots joining forces

Robots are now capable of doing far more than grasp and assemble objects. They can make their own decisions and solve problems based on their skill sets, while human operators solely focus on high-level commands. While these developments, paired with sci-fi movies, may make it appear as though robots are going to take over the world and take jobs away from humans, that isnt necessarily the case.

They simply help humans do their jobs better.

The best results come from the pairing of human intelligence with machine intelligence. Humans bring creativity and ingenuity, while industrial robots bring speed, strength, and accuracy. As summed up by Patrick Sobalvarro on WeForum, The idea of a fully automated lights-out factory with no production workersone requiring only machine programming and maintenancehas proven to be a dead end. So much of what happens in a factory requires human ingenuity, learning, and adaptability. As products have become more varied and customized to local markets and customer needs, the economics of full automation make no sense. With the support of necessary regulatory oversight, machines with AI-based components can also enable sustainable development, thereby helping manufacturers dramatically reduce the carbon footprint of their facilities.

The post-pandemic world sparked many changes in manufacturing, not only for the health and safety of workers, but also to ramp things up in supply chains in response to ever-changing market needs. In order to stay relevant and compete in the evolving global market, manufacturers need to transform the way they produce their products. The most complex challenges stem from demands for higher product variability, mass customization, quality expectations, and faster product cycles. This is all the more reason why manufacturing processes are faster, more efficient, and more cost-effective when humans and robots work together.

While the advantages of humans working together with robots were known well before the pandemic, the crisis made the pairing crucial as manufacturers began to reopen their facilities, for improved productivity, quality of output, and working conditions.

Both AI and ML technologies are making impactful strides in manufacturing, and there is no time like the present for manufacturers to get on board and explore ways to transform their processes to benefit across all fronts.

Sign up for the free insideBIGDATAnewsletter.

Join us on Twitter:@InsideBigData1 https://twitter.com/InsideBigData1

Visit link:
My Robot Brings All the Boys to the Yard, Its AI is Better than Yours - insideBIGDATA

From Android 9.0 Pie, Google has taken important steps in the way it is possible to navigate the Android OS. Starting with Android 12, Google offers a limited amount of machine learning to adapt gesture navigation to the way someone uses their phone.

Since the introduction of Gesture Navigation in Android to replace the buttons of outdated software, complaints have persisted about how the flow navigation method works. In many applications, the usual navigation actions within applications are seen as navigation actions in the system, as a result of which you are suddenly kicked out of the application, or unintentionally returning to the previous page. From Android 10, a solution was presented: developers can Manually set exclusion areas.

With these exclusion areas, gesture navigation has been prohibited within a certain area. Moreover, Android has been equipped with sensitivity settings. From the settings it was possible to determine how quickly the system responds to the navigation action. For Android 12, Google is working on solutions that will tailor the operation of gesture navigation according to users desires Dew On XDA Developers. The developer found two of his apps in a list of 43,000 apps being monitored in the new OS for navigation actions.

Current Customization Options for Android 12 Navigation, Photo: Android Police.

Google uses the TensorFlow Lite model for this purpose, through which machine learning can be done on the phone. According to Quinny899, Google offers a specific reference in EdgeBackGestureHandler, which deals with gesture navigation in Android 12, for a file in which the background gesture data is saved. With a machine learning model, it is possible to recognize specific behavior and adjust gesture navigation based on the models results.

Google also made another change to gesture navigation in Android 12 Android Police Described. In Android 12, the gesture navigation actions to return to the previous screen or home screen work from full screen in one go. In Android 11, you will first have to tap the screen once and then perform the navigation action. Looks like it requires an Android app tweak: this tweak doesnt work for Twitter.

From the last modification to the gestures, you can expect that Google will actually work on the stable version of Android 12. Whether Google will also develop a machine learning model for the stable release which will likely be towards the end of the third quarter launch is a different story. Currently, the flag has to be changed in Android 12 to enable machine learning: thus the changes cannot be noticed automatically.

Are you hoping that Google continues the machine learning features of Android 12, or are you satisfied with the way the gesture navigation works on your phone? Let us know for sure in the comments, and dont forget to mention which Android version you are using.

Go here to see the original:
Android 12 improves gesture navigation using machine learning - Dividend Wealth