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Top 4 AI trends prone to shape our future

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Intelligent robots, intelligent virtual assistants, intelligent cars intelligently driving themselves, intelligent search systems learning and already knowing our browsing habits, interests, knowing what we are going to do online and even in real life. Siri and Alexa, Tesla, Amazon and Google, artificially intelligent algorithms that are everywhere, able to do many things instead of us. In the future, AI is going to change everything. As for now, there are lots of discussions about 4 main AI trends that are prone to shape the AI mechanized future of mankind. Here they are: deep learning, facial recognition, cloud, privacy and policy.


Microsoft shows off hybrid cloud management and cloud analytics tools at Ignite

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Microsoft's Ignite event traditionally attracts more from the developer ranks, but the technologies on display are increasingly of relevance to CIOs developing cloud strategies today. At Ignite 2019 in Orlando last week, Microsoft unveiled a new approach to analytics and data warehousing, Azure Synapse Analytics, and a new way to run Azure data services in anyone's cloud, Azure Arc. Get the latest cloud computing insights by signing up for our newsletter. With Azure Synapse Analytics Microsoft takes its Azure SQL Data Warehouse and turns up the volume to handle petabytes of data in its cloud. Some of the features -- such as dynamic data masking and column- and row-level security to provide granular access control -- are already generally available, while others -- notably integrations with Apache Spark, Power BI and Azure Machine Learning -- are still in preview.


How we can use Deep Learning with Small Data? – Thought Leaders

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When it comes to keeping up with emerging cybersecurity trends, the process of staying on top of any recent developments can get quite tedious since there's a lot of news to keep up with. These days, however, the situation has changed dramatically, since the cybersecurity realms seem to be revolving around two words- deep learning. Although we were initially taken aback by the massive coverage that deep learning was receiving, it quickly became apparent that the buzz generated by deep learning was well-earned. In a fashion similar to the human brain, deep learning enables an AI model to achieve highly accurate results, by performing tasks directly from the text, images, and audio cues. Up till this point, it was widely believed that deep learning relies on a huge set of data, quite similar to the magnitude of data housed by Silicon Valley giants Google and Facebook to meet the aim of solving the most complicated problems within an organization.


Now available: Batch Recommendations in Amazon Personalize Amazon Web Services

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Today, we're very happy to announce that Amazon Personalize now supports batch recommendations/ Launched at AWS re:Invent 2018, Personalize is a fully-managed service that allows you to create private, customized recommendations for your applications, with little to no machine learning experience required. With Personalize, you provide the unique signals in your activity data (page views, sign-ups, purchases, and so forth) along with optional customer demographic information (age, location, etc.). You then provide the inventory of the items you want to recommend, such as articles, products, videos, or music: as explained in previous blog posts, you can use both historical data stored in Amazon Simple Storage Service (S3) and streaming data sent in real-time from a JavaScript tracker or server-side. Then, entirely under the covers, Personalize will process and examine the data, identify what is meaningful, select the right algorithms, train and optimize a personalization model that is customized for your data, and is accessible via an API that can be easily invoked by your business application. However, some customers have told us that batch recommendations would be a better fit for their use cases.



Global Big Data Conference

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Qualified data providers include category-leading brands such as Reuters, who curate data from over 2.2 million unique news stories per year in multiple languages; Change Healthcare, who process and anonymize more than 14 billion healthcare transactions and $1 trillion in claims annually; Dun & Bradstreet, who maintain a database of more than 330 million global business records; and Foursquare, whose location data is derived from 220 million unique consumers and includes more than 60 million global commercial venues. For qualified data providers, AWS Data Exchange makes it easy to reach the millions of AWS customers migrating to the cloud by removing the need to build and maintain infrastructure for data storage, delivery, billing, and entitling. Enterprises, scientific researchers, and academic institutions have been using third-party data for decades to conduct research, power applications and analytics, train machine-learning models, and make data-driven decisions. But, as these customers subscribe to more third-party data, they often have to wait weeks to receive shipped physical media, manage sensitive credentials for multiple File Transfer Protocol (FTP) hosts and periodically check for updates, or code to several disparate application programming interfaces (APIs). These methods are inconsistent with the modern architectures customers are developing in the cloud.


Global Big Data Conference

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Qualified data providers include category-leading brands such as Reuters, who curate data from over 2.2 million unique news stories per year in multiple languages; Change Healthcare, who process and anonymize more than 14 billion healthcare transactions and $1 trillion in claims annually; Dun & Bradstreet, who maintain a database of more than 330 million global business records; and Foursquare, whose location data is derived from 220 million unique consumers and includes more than 60 million global commercial venues. For qualified data providers, AWS Data Exchange makes it easy to reach the millions of AWS customers migrating to the cloud by removing the need to build and maintain infrastructure for data storage, delivery, billing, and entitling. Enterprises, scientific researchers, and academic institutions have been using third-party data for decades to conduct research, power applications and analytics, train machine-learning models, and make data-driven decisions. But, as these customers subscribe to more third-party data, they often have to wait weeks to receive shipped physical media, manage sensitive credentials for multiple File Transfer Protocol (FTP) hosts and periodically check for updates, or code to several disparate application programming interfaces (APIs). These methods are inconsistent with the modern architectures customers are developing in the cloud.


Top Six Career Specializations That Will Drive Future Businesses - EconoTimes

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Considering the high salary and growing interest in this role, specialization as an ML engineer is a great career move. The tech world is certainly where the money is for employees because it is where businesses currently struggle. The biggest companies have already shown us the power of having specialists on hand working to do the impossible. Google's investment in quantum computing has just made a huge leap, for example, and the future of tech and specialization looks bright. Big firms, rather than tech giants, are not scrambling to catch up, and by investing your time and effort to return to higher education and specialize with a Masters of Science or even a Ph.D. will pay off and provide you with a secure, lucrative, and rewarding position in your chosen field. This article does not necessarily reflect the opinions of the editors or management of EconoTimes.


Artificial intelligence helps retailers anticipate customer needs

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Retailers are increasingly using artificial intelligence to manage their stores and monitor shopping behavior and offer better experiences, as we discover in the second installment of Mergers & Acquistions' 7-part series, Retail Tech M&A. There are 7 technologies retailers are investing in through M&A: The Internet of Things enables enhanced personalization, such as custom drive-thru menus. Artificial intelligence applications predict customers' needs. Modern data centers and warehouses fill orders quickly. Robots assist with sorting and packing consumer goods.


How Does AI is Bringing A Great Change in eCommerce?

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Artificial Intelligence is boldly walking across the corridors of eCommerce and steadily taking over the world. Don't you agree with this fact? Some people say, Artificial Intelligence is replacing human beings and will eat up their jobs. Furthermore, they can do the jobs that you could have ever imagined that robots will do one day in this real-world. Can we call AI, a real game-changer in the eCommerce Industry?