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intelligence systems

Results 1 - 25 of 63Sort Results By: Published Date | Title | Company Name
Published By: Cisco Umbrella EMEA     Published Date: Nov 15, 2019
The average company uses more than 50 security vendors: firewalls, web proxies, SIEM, appliances, intelligence - and the list goes on. Do you have more security tools than you can handle? There’s an easier way to make the most of your existing investments. We'll share ways you can reduce security alerts and extend your security off network, anywhere users go, with Cisco Umbrella. Built with a bidirectional API, Cisco Umbrella easily integrates with the other systems in your stack, so you can extend protection from on-premises security appliances to devices and sites beyond your perimeter — and amplify investments you’ve already made. Read the eBook to learn more about getting more bang for your security buck.
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Cisco Umbrella EMEA
Published By: Dell - NVIDIA     Published Date: Nov 04, 2019
Artificial intelligence (AI), machine learning (ML), and deep learning (DL) technologies are expected to permeate day-to-day business as well as customer activity. Industries such as healthcare (advanced diagnosis and treatment), transportation (advanced driver assistance systems and autonomous vehicles), and life sciences (rare disease treatment research) are some of the early adopters of AI. The goal for any organization adopting AI/ML/DL is to deliver meaningful insights and predictions that can significantly improve products, processes, or services across industries and use cases. Today, as AI becomes mainstream, many organizations find themselves in the initial proof-of-concept (POC) stage; only a few are in full production. IDC's 2019 Artificial Intelligence Global Adoption Trends and Strategies Survey found that 18% of organizations had AI models in production, 16% were in the POC stage, and 15% were experimenting with AI.
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Dell - NVIDIA
Published By: SDL     Published Date: Sep 20, 2019
Component Content Management: A New Paradigm in Intelligent Content Services While technology has changed the world, the way that companies manage information has inherently stayed the same. The advent of near-ubiquitous connectivity among applications and machines has resulted in a data deluge that will fundamentally alter the landscape of content management. From mobile devices to intelligent machines, the volume and sophistication of data have surpassed the ability of humans to manage it with outdated methods of collection, processing, storage, and analysis. The opportunity afforded by the advent of artificial intelligence (AI) has stimulated the market to search for a better way to capture, classify, and analyze this data in its journey to digital transformation (DX). The paradigm of document-based information management has proven to be a challenge in finding, reusing, protecting, and extracting value from data in real time. Legacy systems may struggle with fragmented information
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SDL
Published By: Cisco Umbrella EMEA     Published Date: Aug 08, 2019
"The average company uses more than 50 security vendors: firewalls, web proxies, SIEM, appliances, intelligence - and the list goes on. Do you have more security tools than you can handle? There’s an easier way to make the most of your existing investments. We'll share ways you can reduce security alerts and extend your security off network, anywhere users go, with Cisco Umbrella. Built with a bidirectional API, Cisco Umbrella easily integrates with the other systems in your stack, so you can extend protection from on-premises security appliances to devices and sites beyond your perimeter — and amplify investments you’ve already made. Read the eBook to learn more about getting more bang for your security buck."
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Cisco Umbrella EMEA
Published By: Pure Storage     Published Date: Jul 03, 2019
Deep learning opens up new worlds of possibility in artifi cial intelligence, enabled by advances in computational capacity, the explosion in data, and the advent of deep neural networks. But data is evolving quickly and legacy storage systems are not keeping up. Advanced AI applications require a modern all-fl ash storage infrastructure that is built specifi cally to work with high-powered analytics.
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Pure Storage
Published By: Pure Storage     Published Date: Jul 03, 2019
Deep learning opens up new worlds of possibility in artificial intelligence within the Financial Services industry, enabled by advances in computational capacity, the explosion in data, and the advent of deep neural networks. But data is evolving quickly and legacy storage systems are not keeping up. Advanced AI applications require a modern all-flash storage infrastructure that is built specifically to work with high-powered analytics.
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Pure Storage
Published By: Hewlett Packard Enterprise     Published Date: Jun 17, 2019
"Cloud-based predictive analytics platforms are a relatively new phenomenon, and they go far beyond the remote monitoring systems of a prior generation. Three key features differentiate cloud-based predictive analytics — data sharing, scope of monitoring, and use of artificial intelligence/machine learning (AI/ML) to drive autonomous operations. To help familiarize the uninitiated with specifically what types of value these systems can drive, IDC discusses them at some length in this white paper."
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Hewlett Packard Enterprise
Published By: Intel     Published Date: Apr 11, 2019
Intel teamed up with Philips to show that servers powered by Intel® Xeon® Scalable processors could be used to efficiently perform deep learning inference on patients’ X-rays and computed tomography (CT) scans, without the need for accelerators. The ultimate goal for Philips is to offer artificial intelligence (AI) to its end customers without significantly increasing the cost of the customers’ systems and without requiring modifications to the hardware deployed in the field.
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Intel
Published By: Pure Storage     Published Date: Apr 10, 2019
Deep learning opens up new worlds of possibility in artificial intelligence, enabled by advances in computational capacity, the explosion in data, and the advent of deep neural networks. But data is evolving quickly and legacy storage systems are not keeping up. Advanced AI applications require a modern all-flash storage infrastructure that is built specifically to work with high-powered analytics.
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Pure Storage
Published By: Pure Storage     Published Date: Apr 10, 2019
Deep learning opens up new worlds of possibility in artificial intelligence, enabled by advances in computational capacity, the explosion in data, and the advent of deep neural networks. But data is evolving quickly, and legacy storage systems are not keeping up. Advanced AI applications require a modern all-flash storage infrastructure that is built specifically to work with high-powered analytics.
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Pure Storage
Published By: Flexera     Published Date: Feb 19, 2019
Flexera’s Software Vulnerability Research allows effective reduction of the attack surface for cybercriminals, providing access to verified vulnerability intelligence from Secunia Research covering all applications and systems across all platforms. It drives a prioritized remediation process by handling vulnerability workflows, tickets and alerts, and describes the steps to mitigate the risk of costly breaches. You Don’t Know What You Don’t Know It’s hard for enterprise security analysts to get reliable and trusted information about software vulnerabilities and then identify and filter that data for just the products that matter to their organization. Those challenges lead to wasted time and effort. Learn more.
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Flexera
Published By: Pure Storage     Published Date: Dec 05, 2018
Deep learning opens up new worlds of possibility in artificial intelligence, enabled by advances in computational capacity, the explosion in data, and the advent of deep neural networks. But data is evolving quickly and legacy storage systems are not keeping up. Read this MIT Technology Review custom paper to learn how advanced AI applications require a modern all-flash storage infrastructure that is built specifically to work with high-powered analytics, helping to accelerate business outcomes for data driven organizations.
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Pure Storage
Published By: Cognizant     Published Date: Oct 23, 2018
Incorporating artificial intelligence into your business systems and processes is a journey unlike any other digital technology implementation. There is no utility AI. Every company must chart its own path to success. This white paper offers insights and recommendations to help organizations identify use cases, pilot and learn from them, and then repeat the process so AI becomes a key element in the organization’s continuous improvement.
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cognizant, artificial intelligence
    
Cognizant
Published By: HERE Technologies     Published Date: Sep 27, 2018
Location has become paramount to building new apps, services, experiences and business models. If data is the new oil, then location is the crude oil. This is why most of the top location platform players have been developing technologies to power next-generation autonomous mobility systems. And the “richness” of location data and real-time intelligence are becoming strong monetization opportunities. The 2018 Counterpoint Research Location Ecosystems Update compared 16 location platform vendors, including Google, TomTom and Mapbox. Learn why the HERE Open Location Platform – described as super-rich, always up-to-date, and a neutral offering – is a leader in the location data arena.
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HERE Technologies
Published By: Group M_IBM Q418     Published Date: Sep 10, 2018
Businesses are struggling with numerous variables to determine what their stance should be regarding artificial intelligence (AI) applications that deliver new insights using deep learning. The business opportunities are exceptionally promising. Not acting could potentially be a business disaster as competitors gain a wealth of previously unavailable data to grow their customer base. Most organizations are aware of the challenge, and their lines of business (LOBs), IT staff, data scientists, and developers are working to define an AI strategy. IDC believes that this emerging environment is to date still highly undefined, even as businesses must make critical decisions. Should businesses develop in-house or use VARs, systems integrators, or consultants? Should they deploy on-premise, in the cloud, or in some hybrid form? Can they use existing infrastructure, or do AI applications and deep learning require new servers with new capabilities? We believe that many of these questions can be
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Group M_IBM Q418
Published By: BlackBerry Cylance     Published Date: Jul 02, 2018
SE Labs tested CylancePROTECT® in an offline environment against major threats that subsequently appeared in the wild. The test explores the product’s ability to prevent new threats from attacking endpoint systems successfully. CylancePROTECT contains technology designed to identify and block malware using what it claims to be an “artificial intelligence” (AI) model. This model can be updated over time. However, in this test we used the model created in May 2015 and did not permit further updates so that the software was unable to receive new models or edit the existing one. The test exposed systems protected by this older version of CylancePROTECT to very impactful threats discovered and reported widely after May 2015. In this way, the test shows to what extent the product was able to predict how future threats would appear. This “Predictive Advantage” (PA), the advantage that users of the product have against future adversaries, is presented in this report.
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malware, predictive, test, response
    
BlackBerry Cylance
Published By: IBM     Published Date: Jul 02, 2018
Businesses are struggling with numerous variables to determine what their stance should be regarding artificial intelligence (AI) applications that deliver new insights using deep learning. The business opportunities are exceptionally promising. Not acting could potentially be a business disaster as competitors gain a wealth of previously unavailable data to grow their customer base. Most organizations are aware of the challenge, and their lines of business (LOBs), IT staff, data scientists, and developers are working to define an AI strategy. IDC believes that this emerging environment is to date still highly undefined, even as businesses must make critical decisions. Should businesses develop in-house or use VARs, systems integrators, or consultants? Should they deploy on-premise, in the cloud, or in some hybrid form? Can they use existing infrastructure, or do AI applications and deep learning require new servers with new capabilities? We believe that many of these questions can be
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IBM
Published By: Pure Storage     Published Date: Apr 18, 2018
Deep learning opens up new worlds of possibility in artifi cial intelligence, enabled by advances in computational capacity, the explosion in data, and the advent of deep neural networks. But data is evolving quickly and legacy storage systems are not keeping up. Advanced AI applications require a modern all-fl ash storage infrastructure that is built specifically to work with high-powered analytics.
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Pure Storage
Published By: SAS     Published Date: Mar 06, 2018
Today's artifical intelligence (AI) solutions are not sentient in the manner popularized in science fiction by scores of self-aware and typically nefarious androids. Even so, the ability to arm such systems with the ability to directly sense and respond to their in situ environment is critical. Why? In the future, our experiences will be smart, intuitive and informed by analytics that are not seen but felt via new business, personal and operational engagement models. Enabling this interaction requires AI applications that can sense, analyze and respond to their environment in an intelligent and interactive manner. Without requiring the end user to write, understand or interpret code. “Sensitive” artificial intelligence enables: • More productive use of expanded (big, often unstructured) information sources • Intuitive man-machine interactions (no code-speak here!) • Adaptive, immersive experiences and environments As frequently touted on the nightly news, AI’s popularity is clear. Ho
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SAS
Published By: NetApp     Published Date: Mar 05, 2018
Did you know that by 2020, 50% of analytic queries will be generated using search, natural-language processing or voice, or will be automatically generated? Read the Gartner report Technology Insight for Modern Analytics and Business Intelligence Platforms and find out how to meet the time-to-insight demands of today's competitive business environment. Learn how to: • Determine when to use existing, traditional BI technologies versus modern analytics and BI • Broaden data access beyond relational systems • Adopt new approaches to data modeling
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netapp, database performance, flash storage, data management, cost challenges
    
NetApp
Published By: Pure Storage     Published Date: Jan 12, 2018
Deep learning opens up new worlds of possibility in artificial intelligence, enabled by advances in computational capacity, the explosion in data, and the advent of deep neural networks. But data is evolving quickly and legacy storage systems are not keeping up. Advanced AI applications require a modern all-fl ash storage infrastructure that is built specifically to work with high-powered analytics.
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data, deep learning, automated, intelligence, pure storage
    
Pure Storage
Published By: IBM     Published Date: Oct 17, 2017
Every day, torrents of data inundate IT organizations and overwhelm the business managers who must sift through it all to glean insights that help them grow revenues and optimize profits. Yet, after investing hundreds of millions of dollars into new enterprise resource planning (ERP), customer relationship management (CRM), master data management systems (MDM), business intelligence (BI) data warehousing systems or big data environments, many companies are still plagued with disconnected, “dysfunctional” data—a massive, expensive sprawl of disparate silos and unconnected, redundant systems that fail to deliver the desired single view of the business. To meet the business imperative for enterprise integration and stay competitive, companies must manage the increasing variety, volume and velocity of new data pouring into their systems from an ever-expanding number of sources. They need to bring all their corporate data together, deliver it to end users as quickly as possible to maximize
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IBM
Published By: IBM     Published Date: Oct 03, 2017
Every day, torrents of data inundate IT organizations and overwhelm the business managers who must sift through it all to glean insights that help them grow revenues and optimize profits. Yet, after investing hundreds of millions of dollars into new enterprise resource planning (ERP), customer relationship management (CRM), master data management systems (MDM), business intelligence (BI) data warehousing systems or big data environments, many companies are still plagued with disconnected, “dysfunctional” data—a massive, expensive sprawl of disparate silos and unconnected, redundant systems that fail to deliver the desired single view of the business.
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data, enterprise resource planning, customer relationship management, business intelligence, data management systems
    
IBM
Published By: IBM     Published Date: Sep 11, 2017
This book is written for readers who have varying levels of familiarity with ODM. It doesn’t focus on any particular vendor’s offering; instead, it talks about the features of ODM as a model for managing operational decision-making. This book isn’t about offline business intelligence systems. While those systems are very valuable, the focus of this book is on automated decisions that can be executed in real time in conjunction with your business applications and processes.
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odm, management, operation-decision making, intelligence systems
    
IBM
Published By: IBM     Published Date: Jul 26, 2017
Every day, torrents of data inundate IT organizations and overwhelm the business managers who must sift through it all to glean insights that help them grow revenues and optimize profits. Yet, after investing hundreds of millions of dollars into new enterprise resource planning (ERP), customer relationship management (CRM), master data management systems (MDM), business intelligence (BI) data warehousing systems or big data environments, many companies are still plagued with disconnected, “dysfunctional” data—a massive, expensive sprawl of disparate silos and unconnected, redundant systems that fail to deliver the desired single view of the business. To meet the business imperative for enterprise integration and stay competitive, companies must manage the increasing variety, volume and velocity of new data pouring into their systems from an ever-expanding number of sources. They need to bring all their corporate data together, deliver it to end users as quickly as possible to maximize
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scalability, data warehousing, resource planning
    
IBM
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