big data


[video] @Cloudistics Public Cloud @CloudExpo #CloudNative #AI #DataCenter

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"As we've gone out into the public cloud we've seen that over time we may have lost a few things - we've lost control, we've given up cost to a certain extent, and then security, flexibility," explained Steve Conner, VP of Sales at Cloudistics,in this SYS-CON.tv With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend 21st Cloud Expo, October 31 - November 2, 2017, at the Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation. With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend @CloudExpo @ThingsExpo, October 31 - November 2, 2017, at the Santa Clara Convention Center, CA, and June 12-4, 2018, at the Javits Center in New York City, NY, and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation. Join Cloud Expo @ThingsExpo conference chair Roger Strukhoff (@IoT2040), October 31 - November 2, 2017, Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, for three days of intense Enterprise Cloud and'Digital Transformation' discussion and focus, including Big Data's indispensable role in IoT, Smart Grids and (IIoT) Industrial Internet of Things, Wearables and Consumer IoT, as well as (new) Digital Transformation in Vertical Markets. Accordingly, attendees at the upcoming 21st Cloud Expo @ThingsExpo October 31 - November 2, 2017, Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, will find fresh new content in a new track called FinTech, which will incorporate machine learning, artificial intelligence, deep learning, and blockchain into one track.


[video] Full Potential of Cloud @CloudExpo @Ocean9Inc #SAP #AI #DataCenter

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With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend 21st Cloud Expo, October 31 - November 2, 2017, at the Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation. With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend @CloudExpo @ThingsExpo, October 31 - November 2, 2017, at the Santa Clara Convention Center, CA, and June 12-4, 2018, at the Javits Center in New York City, NY, and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation. Join Cloud Expo @ThingsExpo conference chair Roger Strukhoff (@IoT2040), October 31 - November 2, 2017, Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, for three days of intense Enterprise Cloud and'Digital Transformation' discussion and focus, including Big Data's indispensable role in IoT, Smart Grids and (IIoT) Industrial Internet of Things, Wearables and Consumer IoT, as well as (new) Digital Transformation in Vertical Markets. Accordingly, attendees at the upcoming 21st Cloud Expo @ThingsExpo October 31 - November 2, 2017, Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, will find fresh new content in a new track called FinTech, which will incorporate machine learning, artificial intelligence, deep learning, and blockchain into one track.


Core Differences between Artificial intelligence and Machine Learning -Big Data Analytics News

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It is among the major fields of Computer Science that cover robotics, machine learning, expert systems, general intelligence and natural language processing. The national security system uses data on AI systems, which then presents accurate problems that the nation might face. Reading texts and deciding whether it's a compliment or a complaint, finding out how the genre of music would affect the mood of the listener or composing themes of its own are offered by systems working around Machine Learning and Neural Networks. This has lead to the innovative prospect of Natural Language Processing (NLP), on which work begun and still is being done.


How Big Data Drives AI - DZone AI

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Essentially, big data empowers machine learning and artificial intelligence (AI), and the greater amount of data available, the easier it will be for these systems to learn and function. Artificial intelligence (AI) is referred to as intelligence exhibited by machines that mimic cognitive functions normally exhibited by humans, including learning and problem-solving. For several years, machine learning has been used to devise a series of complex algorithms that learn and make predictions from data, also known as predictive analytics. These learning algorithms are commonly associated with a neural network (NN) because they operate similarly to the human biological neural network, having several connections and layers between nodes.


How AI Is Crunching Big Data To Improve Healthcare Outcomes

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Machine support, patient information from medical records and conversations with doctors are combined with the latest medical literature to help form a diagnosis without detracting from doctor-patient relations. By utilizing deep learning algorithms and software, healthcare providers can connect various libraries of medical information and scan databases of medical records, spotting patterns that lead to more accurate detection and greater breadth of efficiency in medical diagnosis and research. IBM Watson, which has previously been used to help identify genetic markers and develop drugs, is applying its neural learning networks to help doctors correctly diagnose heart abnormalities from medical imaging tests. Powered by Baidu's deep learning and natural language processing networks, Melody improves her communication and diagnostic skills by learning from conversations with Baidu's hundreds of millions of users.


Cloud Security -- Role of Artificial Intelligence – Rank Software – Medium

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In the Shared Responsibility Model for Cloud Security, Visibility across the diverse IaaS & SaaS application set is key for customers peace of mind. Not only do they help provide Visibility across the diverse IaaS & SaaS setups and enable Rapid Response, the Behavioral models also allow for a proactive approach to Security. Sophisticated software based tools can detect anomalous behavior by detecting unusually large data transfers and leakage of sensitive data to cloud; as well as provide full visibility for secure usage and operations of enterprise cloud services such as Office 365. Combining Big Data Technologies, Machine Learning and patented Algorithms, RANK's User & Entity Behavior platform helps discover insightful information and actionable intelligence around insider threats, targeted attacks and more.


Data Science Simplified: Principles and Process – Becoming Human

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In this article, I will begin by covering fundamental principles, general process and types of problems in Data Science. In this article, I will begin by covering principles, general process and types of problems in Data Science. If the organization needs to grow our the customer base by targeting new segments and reducing customer churn, how can we decompose it into machine learning problems? Once we have defined the business problem and decomposed into machine learning problems, we need to dive deeper into the data.


Technical Debt in Machine Learning – Towards Data Science – Medium

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Experienced teams know when to back up seeing a piling debt, but technical debt in machine learning piles extremely fast. Here are three fantastic papers that explore this issue: Machine Learning: The High Interest Credit Card of Technical Debt NIPS'14 Hidden Technical Debt in Machine Learning Systems NIPS'15 What's your ML test score? In small companies, it is relatively easy to control the feedback loops, but in large companies with dozens of teams working on dozens of complex systems piped into each other some of the feedback loops are very likely to be missed. With the feedback loops, your metrics won't reflect the real quality of the system and your ML model will learn to exploit these feedback loops instead of learning useful things.


Putting Machine Learning to Work on Customer Data

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Businesses large and small are being lured in by the potential of artificial intelligence (AI), machine learning (ML), deep learning and cognitive computing, while others are still trying to figure out how to tell them apart. Utilizing machine learning within a data management platform can help generate match rules automatically from data, and provide active learning training for data stewards. ML can provide recommendations that improve data quality by suggesting better matching rules, finding potential matches as new data sources are onboarded and determining profiles with poor data quality and wrong addresses. Combining reliable data, relevant insights and intelligent recommendations into one, single platform helps deliver deeper understanding into customer behavior and needs.


Deep Learning and the Future of Auditing - The CPA Journal

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This article discusses how the cognitive capabilities of deep learning could be applied to various audit procedures to enable audit automation and improve decision making. Although the idea of artificial neural networks dates back to the 1950s, such networks could not be called real artificial intelligence until recent advances in computational power and data storage enabled the development of deep neural networks that model the structure and thinking process of the brain. The hidden layers of a deep neural network automatically "learn" from massive amounts of data (especially semi-structured or unstructured data) received by the input layer (e.g., millions of images, years' worth of speeches, tera-bytes of text files), recognize data patterns in more and more abstract representations as the data is processed and transmitted from one hidden layer to the next, and classify the data into predefined categories in the output layer. While the challenges of big data analysis require a willingness to adopt more advanced data analytical technologies, such as deep learning, the availability of massive amounts of financial data facilitates the implementation and improvement of this technology in auditing.