machine learning

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By: SAS     Published Date: Aug 28, 2018
With the widespread adoption of predictive analytics, organizations have a number of solutions at their fingertips. From machine learning capabilities to open platform architectures, the resources available to innovate with growing amounts of data are vast. In this TDWI Navigator Report for Predictive Analytics, researcher Fern Halper outlines market opportunities, challenges, forces, status and landscape to help organizations adopt technology for managing and using their data. As highlighted in this report, TDWI shares some key differentiators for SAS, including the breadth and depth of functionality when it comes to advanced analytics that supports multiple personas including executives, IT, data scientists and developers.
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     SAS
By: SAS     Published Date: Oct 03, 2018
Unlike rules-based systems, which are fairly easy for fraudsters to test and circumvent, machine learning adapts to changing behaviors in a population through automated model building. With every iteration, the algorithms get smarter and more accurately find activities that represent risk to the firm.
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     SAS
By: SAS     Published Date: Oct 03, 2018
Fraudsters are only becoming smarter. How is your organization keeping pace and staying ahead of fraud schemes and regulatory mandates to monitor for them? Technology is redefining what’s possible in fighting fraud and financial crimes, and SAS is at the forefront, offering solutions to: • Protect from reputational, regulatory and financial risks. • Reduce the cost of fraud and financial crimes prevention. • Gain a holistic view of risk across functions. • Include cyber events in regulatory report filings. In this e-book, learn the basics in how to prevent fraud, achieve compliance and preserve security. SAS fraud solutions use advanced analytics and artificial intelligence to help your organization better detect and prevent fraud. By applying analytics and powerful machine learning on a unifying platform, SAS helps organizations around the globe detect more financial offenses, reduce false positives and run more efficient investigations.
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     SAS
By: SAS     Published Date: Jan 04, 2019
How can you open your analytics program to all types of programming languages and all levels of users? And how can you ensure consistency across your models and your resulting actions no matter where they initiate in the company? With today’s analytics technologies, the conversation about open analytics and commerical analytics is no longer an either/or discussion. You can now combine the benefits of SAS and open source analytics technology systems within your organization. As we think about the entire analytics life cycle, it’s important to consider data preparation, deployment, performance, scalability and governance, in addition to algorithms. Within that cycle, there’s a role for open source and commercial analytics. For example, machine learning algorithms can be developed in SAS or Python, then deployed in real-time data streams within SAS Event Stream Processing, while also integrating with open systems through Java and C APIs, RESTful web services, Apache Kafka, HDFS and more.
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     SAS
By: SAS     Published Date: Jan 30, 2019
Machine learning systems don’t just extract insights from the data they are fed, as traditional analytics do. They actually change the underlying algorithm based on what they learn from the data. So the “garbage in, garbage out” truism that applies to all analytic pursuits is truer than ever. Few companies are already using AI, but 72 percent of business leaders responding to a PWC survey say it will be fundamental in the future. Now is the time for executives, particularly the chief data officer, to decide on data management strategy, technology and best practices that will be essential for continued success.
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     SAS
By: Esker     Published Date: Jun 29, 2017
Efficient O2C processes play a large role in the customer experience and company success — unfortunately, they can be a challenge to attain when you have different teams working towards different goals. In this eBook, you’ll explore how O2C automation not only improves efficiency, but the entire customer experience, by uniting your five most strategic teams: 1. Order Management 2. E-Commerce 3. Logistics & Distribution 4. Account Receivable 5. Sales Start creating a positive customer experience with a proactive solution. Download your copy of the eBook now!
Tags : order-to-cash, customer experience, machine learning, order management, collections management
     Esker
By: Cisco     Published Date: Dec 21, 2016
Technology’s role in business and society has shifted away from largely driving efficiencies to innovating and creating engaging experiences that attract and retain customers. Innovations and business outcomes are fueled by a perfect storm of technology trends in cloud, analytics, machine learning, IoT and the emerging API Economy. The convergence of these technologies has created new opportunities for enterprises to improve business performance by acquiring customers faster while creating brand loyalty. The role of technology expands the interaction with customers beyond the core of the enterprise – away from 100% dependencies on systems of records – and towards real-time, contextual interactions. Businesses are a digital business or they are evolving to become one. This requires enterprises to re-think how they build software architectures.
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     Cisco
By: BlackBerry Cylance     Published Date: Mar 12, 2019
Today’s advanced cyber threats target every computer and mobile device, including enterprise endpoints, especially those that make up critical infrastructure like industrial control systems and embedded devices that control much of our physical world. The modern computing landscape consists of a complex array of physical, mobile, cloud, and virtual computing, creating a vast attack surface. Meanwhile, the cybersecurity industry is prolific with defense-in-depth security technologies, despite a threat landscape that remains highly dynamic, sophisticated, and automated. Cylance, however, takes a unique and innovative approach of using real-time, mathematical, and machine learning threat analysis to solve this problem at the endpoint for organizations, governments, and end-users worldwide.
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     BlackBerry Cylance
By: BlackBerry Cylance     Published Date: Apr 15, 2019
Artificial intelligence (AI) leads the charge in the current wave of digital transformation underway at many global companies. Organizations large and small are actively expanding their AI footprints as executives try to comprehend more fully what AI is and how they can use it to capitalize on business opportunities by gaining insight to the data they collect that enables them to engage with customers and hone a competitive edge. But, while AI may indeed be the frontier of enterprise technology, there remain many misconceptions about it. Part of the confusion stems from the fact that AI is an umbrella term that covers a range of technologies — including machine learning, computer vision, natural language processing, deep learning, and more — that are in various stages of development and deployment. The use of AI for dynamic pricing and targeted marketing has been in use for a while, but actual AI computing where machines think like humans is still many years from becoming mainstream. T
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     BlackBerry Cylance
By: BlackBerry Cylance     Published Date: Sep 18, 2019
There will be a ransomware attack on businesses every 14 seconds by the end of 2019 . Every 40 seconds, one of those attacks will prove successful , with devastating effects ranging from permanent loss of irreplaceable data to life-threatening interruptions to patient care. In years past, expert malware authors packaged up their know-how into costly exploit kits sold on the underground market. Cyber criminals had to recover high upfront costs before launching a campaign and realizing a profit. Today, ransomware-as a-service groups like Satan make it easier than ever before for would-be cyber criminals with minimal technical skills to launch attacks, offering free ransomware toolkits and hands-on help to manage campaigns and extort payments. Read our white paper to learn how CylancePROTECT® prevents Petya, Goldeneye, WannaCry, Satan, and many more from executing, with machine learning models dating back to September 2015, long before the ransomware first appeared in the wild.
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     BlackBerry Cylance
By: BlackBerry Cylance     Published Date: Sep 18, 2019
There is now broad consensus among security professionals that artificial intelligence (AI) technologies can play an important role in reducing cyber risks. Exactly what that role is, however, and how it will evolve over time remains unclear for respondents to a new SANS Institute research study sponsored by BlackBerry Cylance. Opinions varied about the maturity of AI, its benefits and risks, and the baseline requirements for an AI-enabled security solution. Download the report today for the complete survey results and learn: 1) How perceptions of AI vary across industry sectors and organizational roles and responsibilities; 2) How AI technologies compare and contrast with human intelligence; 3) How machine learning is driving advances in the field; 4) What respondents believe to be the greatest risks and benefits of AI; and, 5) The most significant barriers to broader AI adoption.
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     BlackBerry Cylance
By: IBM     Published Date: Jul 07, 2015
Life revolves around prediction—for example, the route you take to get to work, whether to go on a second date, or whether or not to keep reading this sentence are all forms of prediction. We are already seeing machine learning powered by Apache Spark changing the face of innovation at IBM. Learn more.
Tags : intelligent applications, machine learning, prescriptive analytics, real-time, natural language processing, automation
     IBM
By: SAS     Published Date: May 17, 2016
This report provides a guide to some of the opportunities that are available for using machine learning in business, and how to overcome some of the key challenges of incorporating machine learning into an analytics strategy. We will discuss the momentum of machine learning in the current analytics landscape, the growing number of modern applications for machine learning, as well as the organizational and technological challenges businesses face when adopting machine learning. We will also look at how two specific organizations are exploiting the opportunities and overcoming the challenges of machine learning as they’ve embarked on their own analytic evolution.
Tags : oreilly, evolution of analytics, sas, machine learning, analytics landscape, networking, it management, data management
     SAS
By: SAS     Published Date: Jun 05, 2017
Analytics is now an expected part of the bottom line. The irony is that as more companies become adept at analytics, it becomes less of a competitive advantage. Enter machine learning. Recent advances have led to increased interest in adopting this technology as part of a larger, more comprehensive analytics strategy. But incorporating modern machine learning techniques into production data infrastructures is not easy.Businesses are now being forced to look deeper into their data to increase efficiency and competitiveness. Read this report to learn more about modern applications for machine learning, including recommendation systems, streaming analytics, deep learning and cognitive computing. And learn from the experiences of two companies that have successfully navigated both organizational and technological challenges to adopt machine learning and embark on their own analytics evolution.
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     SAS
By: SAS     Published Date: Oct 18, 2017
Machine learning uses algorithms to build analytical models, helping computers “learn” from data. It can now be applied to huge quantities of data to create exciting new applications such as driverless cars. This paper, based on presentations by SAS Data Scientist Wayne Thompson, introduces key machine learning concepts and describes SAS solutions that enable data scientists and other analytical professionals to perform machine learning at scale. It tells how a SAS customer is using digital images and machine learning techniques to reduce defects in the semiconductor manufacturing process.
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     SAS
By: SAS     Published Date: Oct 18, 2017
What management and leadership challenges will the next wave of analytic technology bring? This SAS-sponsored Harvard Business Review Insight Center on HBR.org went beyond the buzz of what machine learning can do, to talk about how it will change companies and the way we manage them. Articles include: How to Make Your Company Machine Learning Ready, by James Hodson Machine Learning Is No Longer Just for Experts, by Josh Schwartz Teaching an Algorithm to Understand Right and Wrong, by Greg Satell The Simple Economics of Machine Intelligence, by Ajay Agrawal, Joshua Gans, and Avi Goldfarb Robots and Automation May Not Take Your Desk Job After All, by Dan Finnigan How to Tell If Machine Learning Can Solve Your Business Problem, by Anastassia Fedyk
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     SAS
By: SAS     Published Date: Oct 18, 2017
With all of the attention on machine learning, many are seeking a better understanding of this hot topic and the benefits that it could provide to their organizations. Machine learning – as well as deep learning, natural language processing and cognitive computing – are driving innovations in identifying images, personalizing marketing campaigns, genomics, and navigating the self-driving car. This e-book provides a primer on these innovative techniques as well as 10 best practices and a checklist for machine learning readiness.
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     SAS
By: IBM     Published Date: Oct 26, 2015
Machine learning can help us plan our lives so we can increase our likelihood of success. We are already seeing machine learning powered by Apache Spark changing the face of innovation at IBM. Learn more.
Tags : ibm, machine learning, apache spark, business intelligence, intelligence, intelligence applications, big data, data
     IBM
By: CrowdTwist     Published Date: Apr 16, 2018
In order for brands to compete and provide the level of personalization consumers have already come to expect, marketers need to work quickly to develop competencies around their abilities to collect contextual and anticipatory insight and meet customers in the moments that matter most to them. Now is the time for marketers to invest in technology that supports data capture, segmentation, predictive analytics, and machine learning. With these capabilities in place, brands should be on track to build rich first party profiles of customers across all channels and maximize customer lifetime value by creating relevant experiences at all stages of the customer lifecycle.
Tags : customers, predictive, branding, consumers, competition, lifecycle
     CrowdTwist
By: Inside HPC Media LLC     Published Date: Sep 24, 2019
The 2019 version of High Performance Computing (HPC) has changed considerably from previous years’. Most notably, advancements in Artificial Intelligence (AI) has given rise to data-centric applications in machine learning. Machine learning often relies on HPC technologies, particularly in the early training phase, and in many cases, machine learning initiatives share infrastructure, budgets, and personnel with established HPC installations. A 2018 Intersect360 Research study found that the majority of experienced HPC users had also embarked on machine learning applications, usually overlapping HPC and AI.
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     Inside HPC Media LLC
By: Inside HPC Media LLC     Published Date: Sep 24, 2019
Artificial Intelligence (AI) is rapidly becoming an essential business and research tool, providing valuable new insights into corporate data and delivering those insights with high velocity and accuracy. Enterprises, universities, and government organizations are investing tremendous resources to develop a wide array of future-focused Deep Learning (DL) and Machine Learning (ML) solutions such as: • Autonomous vehicles that circulate unassisted in our cities • Real-time fraud detection that protects shopping and internet transactions • Natural language translators that remove language barriers • Augmented reality that delivers a far richer entertainment experience • Accelerated drug discovery • Fully enabled personalized medicine and remote health diagnostics
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     Inside HPC Media LLC
By: Boomtrain     Published Date: Mar 29, 2016
Learn how to send exceptionally relevant emails and boost engagement with the most powerful technology for email marketers.
Tags : machine learning for marketers, marketing automation, email marketing, content analytics, content personalization, email personalization, dynamic email content, predictive content, predictive marketing, contextual marketing
     Boomtrain
By: SAS     Published Date: Aug 04, 2016
Machine learning and the Internet of Things (IoT) are two of the hottest terms out there today for utilities. Both have the power to create an increasingly autonomous grid that can eventually handle billions of endpoints on utility networks, but the industry may not be maximizing the benefit of these disruptive innovations, nor adequately leveraging the connection between the two of them.
Tags : best practices, networks, utility network, autonomous grid, innovation, competitive advantage
     SAS
By: Workday     Published Date: Oct 11, 2018
Artificial intelligence (AI) and machine learning are redefining business analytics. But for HR, use cases can be much more complex. Learn five key steps to build a strong foundation for answering HCM questions today and position yourself to use AI in HR going forward.
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     Workday
By: AWS     Published Date: Jun 11, 2019
Join us to learn why Human-in-the-Loop training data should be powering your machine learning (ML) projects and how to make it happen. If you’re curious about what human-in-the-loop machine learning actually looks like, join Figure Eight CTO Robert Munro and AWS machine learning experts to learn how to effectively incorporate active learning and human-in-the-loop practices in your ML projects to achieve better results. You'll learn: When to use human-in-the-loop as an effective strategy for machine learning projects How to set up an effective interface to get the most out of human intelligence How to ensure high-quality, accurate data sets When: Available On Demand (please register to view) Who Should Attend: IT leaders and professionals, line-of-business managers, business decision makers, data scientists, developers, and other experts interested in implementing AI/ML on the cloud are encouraged to attend this webinar. AWS Speaker: Chris Burns, Solutions Architect Figure Eight Spea
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     AWS
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