machine learning

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Schneider's DCIM-Driven Cloud Service

Published By: Schneider Electric     Published Date: Aug 15, 2017
Schneider Electric is integrating datacenter infrastructure management (DCIM) software, big-data analytics and cloud services into the management of customers’ datacenters. Its recently launched StruxureOn cloud offering signals a new wave in datacenter operations, using a combination of machine learning, anomaly detection and event-stream playback to give operators real-time insights and alarming via their smartphones. More capabilities and features are planned, including predictive analysis and, eventually, automated action. Schneider’s long-term strategy is to build a partner ecosystem around StruxureOn, and provide digital services that span its traditional datacenter business.
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incident tracking, historical trending, troubleshooting, operational analysis, prediction model, schneider equipment, maintenance, firmware updates
    
Schneider Electric

Apply Artificial Intelligence to Information Security Problems

Published By: BlackBerry Cylance     Published Date: Jul 02, 2018
The information security world is rich with information. From reviewing logs to analyzing malware, information is everywhere and in vast quantities, more than the workforce can cover. Artificial intelligence (AI) is a field of study that is adept at applying intelligence to vast amounts of data and deriving meaningful results. In this book, we will cover machine learning techniques in practical situations to improve your ability to thrive in a data driven world. With clustering, we will explore grouping items and identifying anomalies. With classification, we’ll cover how to train a model to distinguish between classes of inputs. In probability, we’ll answer the question “What are the odds?” and make use of the results. With deep learning, we’ll dive into the powerful biology inspired realms of AI that power some of the most effective methods in machine learning today. Learn more about AI in this eBook.
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artificial, intelligence, enterprise
    
BlackBerry Cylance

The Practical Application of Artificial Intelligence and Machine Learning To Cybersecurity

Published By: BlackBerry Cylance     Published Date: Jul 02, 2018
Artificial intelligence (AI) technologies are rapidly moving beyond the realms of academia and speculative fiction to enter the commercial mainstream, with innovative products that utilize AI transforming how we access and leverage information. AI is also becoming strategically important to national defense and in securing our critical financial, energy, intelligence, and communications infrastructures against state-sponsored cyberattacks. According to an October 2016 report issued by the federal government’s National Science and Technology Council Committee on Technology (NSTCC), “AI has important applications in cybersecurity, and is expected to play an increasing role for both defensive and offensive cyber measures.” Based on this projection, the NSTCC has issued a National Artificial Intelligence Research and Development Strategic Plan to guide federally-funded research and development. The era of AI has most definitely arrived, but many still don’t understand the basics of this im
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artificial, intelligence, cybersecurity, machine
    
BlackBerry Cylance

Why Cylance Beats the Competition When It Comes to Endpoint Protection

Published By: BlackBerry Cylance     Published Date: Jul 02, 2018
The 21st century marks the rise of artificial intelligence (AI) and machine learning capabilities for mass consumption. A staggering surge of machine learning has been applied for myriad of uses — from self-driving cars to curing cancer. AI and machine learning have only recently entered the world of cybersecurity, but it’s occurring just in time. According to Gartner Research, the total market for all security will surpass $100B in 2019. Companies are looking to spend on innovation to secure against cyberthreats. As a result, more tech startups today tout AI to secure funding; and more established vendors now claim to embed machine learning in their products. Yet, the hype around AI and machine learning — what they are and how they work — has created confusion in the marketplace. How do you make sense of the claims? Can you test for yourself to know the truth? Cylance leads the cybersecurity world of AI. The company spearheaded an innovation revolution by replacing legacy antivirus software with predictive, preventative solutions and services that protect the endpoint — and the organization. Cylance stops zero-day threats and the most sophisticated known and unknown attacks. Read more in this analytical white paper.
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cylance, endpoint, protection, cyber, security
    
BlackBerry Cylance

451 Research: Expanding Machine Learning Applications on the Endpoint

Published By: BlackBerry Cylance     Published Date: Mar 12, 2019
A Pathfinder paper navigates decision-makers through the issues surrounding a specific technology or business case, explores the business value of adoption, and recommends the range of considerations and concrete next steps in the decision-making process.
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BlackBerry Cylance

Security Gets Smart with AI

Published By: BlackBerry Cylance     Published Date: Apr 26, 2019
The concept of artificial intelligence (AI) has been with us since the term was coined for the Dartmouth Summer Research Project on Artificial Intelligence in 1956. Today, while general AI strives for full cognitive abilities, there is a narrower scope—this better-defined AI is the domain of machine learning (ML) and other algorithm-driven solutions where cybersecurity has embraced AI. SANS recently conducted a survey of professionals working or active in cybersecurity, and involved with or interested in the use of AI for improving the security posture of their organization. Read their report to learn their survey findings, conclusions, and recommended considerations.
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BlackBerry Cylance

Statistics and Machine Learning at Scale: New Technologies Apply Machine Learning to Big Data

Published By: SAS     Published Date: Mar 06, 2018
Imagine getting into your car and saying, “Take me to work,” and then enjoying an automated drive as you read the morning news. We are getting very close to that kind of scenario, and companies like Ford expect to have production vehicles in the latter part of 2020. Driverless cars are just one popular example of machine learning. It’s also used in countless applications such as predicting fraud, identifying terrorists, recommending the right products to customers at the right time, and correctly identifying medical symptoms to prescribe appropriate treatments. The concept of machine learning has been around for decades. What’s new is that it can now be applied to huge quantities of data. Cheaper data storage, distributed processing, more powerful computers and new analytical opportunities have dramatically increased interest in machine learning systems. Other reasons for the increased momentum include: maturing capabilities with methods and algorithms refactored to run in memory; the
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SAS

TDWI Best Practices Report Q3 2017: Advanced Analytics: Moving Toward AI, Machine Learning and Natur

Published By: SAS     Published Date: Mar 06, 2018
There is a lot of excitement in the market about artificial intelligence (AI), machine learning (ML), and natural language processing (NLP). Although many of these technologies have been available for decades, new advancements in compute power along with new algorithmic developments are making these technologies more attractive to early adopter companies. These organizations are embracing advanced analytics technologies for a number of reasons including improving operational efficiencies, better understanding behaviors, and gaining competitive advantage.
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SAS

The Machine Learning Primer

Published By: SAS     Published Date: Mar 06, 2018
Machines learn by studying data to detect patterns or by applying known rules to: • Categorize or catalog like people or things • Predict likely outcomes or actions based on identified patterns • Identify hitherto unknown patterns and relationships • Detect anomalous or unexpected behaviors The processes machines use to learn are known as algorithms. Different algorithms learn in different ways. As new data regarding observed responses or changes to the environment are provided to the “machine” the algorithm’s performance improves. Thereby resulting in increasing “intelligence” over time.
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SAS

MIT Sloan Management Review. Analytics as a Source of Business Innovation

Published By: SAS     Published Date: Jun 06, 2018
Competitive advantage from analytics is changing, and for the better. For the first time in four years, MIT Sloan Management Review found an increasing ability to strategically innovate with analytics based on interviews with more than 2,600 practitioners and scholars globally. Learn more about key findings, including: Wider use of analytics, better knowledge of its benefits and greater focus on applications have reversed a trend on the benefits of analytics. Return on investment for analytics stems from the governing and sharing of data throughout the organization. Machine learning enables organizations to discover more insight from their data, allowing employees to focus on other critical responsibilities.
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SAS

Magic Quadrant For Data Science Platforms

Published By: IBM     Published Date: Apr 07, 2017
Data science platforms are engines for creating machine-learning solutions. Innovation in this market focuses on cloud, Apache Spark, automation, collaboration and artificial-intelligence capabilities. We evaluate 16 vendors to help you make the best choice for your organization. This Magic Quadrant evaluates vendors of data science platforms. These are products that organizations use to build machine-learning solutions themselves, as opposed to outsourcing their creation or buying ready-made solutions.
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data analytics, product refinement, business exploration, advanced prototyping, analytics, data preparation, customer support, sales relations, market research, model management
    
IBM

Data Management for Artificial Intelligence

Published By: SAS     Published Date: Aug 28, 2018
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

TDWI: Data Preparation Challenges facing every organization

Published 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

Machine Learning Use Cases in Financial Crimes White Paper

Published 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

Fight Fraud and Financial Crimes with Analytics and AI

Published 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

Out in the open with analytics

Published 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

Data Management for Artificial Intelligence

Published 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

Improving The Customer Experience: How Order-to-Cash Automation Unites Your Most Strategic Teams

Published 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!
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order-to-cash, customer experience, machine learning, order management, collections management
    
Esker

A New Software Architecture for a Hyper-Connected World

Published 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

Cylance® vs. Traditional Security Approaches

Published 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

Artificial Intelligence: The Platform of Choice White Paper

Published 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

The Next Wave of Intelligent Applications, Powered by Apache Spark

Published 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.
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intelligent applications, machine learning, prescriptive analytics, real-time, natural language processing, automation, application integration, application performance management, business analytics, business intelligence, business management
    
IBM

OReilly: The Evolution of Analytics: Opportunities and Challenges for Machine Learning in Business

Published 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.
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oreilly, evolution of analytics, sas, machine learning, analytics landscape, networking, it management, data management, business technology
    
SAS

OReilly: The Evolution of Analytics: Opportunities and Challenges for Machine Learning in Business

Published 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

Statistics and Machine Learning at Scale: New Technologies Apply Machine Learning to Big Data

Published 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
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