Introduction to Weave

Introduction to Weave

 
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Weave is a modern, visual and intelligent information medium that empowers users with seamless information discovery. With Weave, highly visual and engaging information intelligently and contextually comes to users where and when it makes sense, rather than forcing users to know to search and then to repeatedly search. And businesses gain a new publishing platform and format that makes their content more discoverable, usable, engaging and measurable – thereby reducing their costs of customer acquisition, engagement and retention, and providing strong returns on their fast-growing publishing investments.

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Artificial intelligence
 
In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans.
Discoverability
 
Discoverability is the degree to which of something, especially a piece of content or information, can be found in a search of a file, database, or other information system.
 
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Brand awareness
 
Brand awareness refers to the extent to which customers are able to recall or recognise a brand.
 
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Customer engagement
 
Customer engagement is a business communication connection between an external stakeholder (consumer) and an organization (company or brand) through various channels of correspondence.

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Artificial Intelligence

In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans. Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving".[1]

As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the AI effect.[2] A quip in Tesler's Theorem says "AI is whatever hasn't been done yet."[3] For instance, optical character recognition is frequently excluded from things considered to be AI, having become a routine technology.[4] Modern machine capabilities generally classified as AI include successfully understanding human speech,[5] competing at the highest level in strategic game systems (such as chess and Go),[6] autonomously operating cars, intelligent routing in content delivery networks, and military simulations.

Artificial intelligence can be classified into three different types of systems: analytical, human-inspired, and humanized artificial intelligence.[7] Analytical AI has only characteristics consistent with cognitive intelligence; generating cognitive representation of the world and using learning based on past experience to inform future decisions. Human-inspired AI has elements from cognitive and emotional intelligence; understanding human emotions, in addition to cognitive elements, and considering them in their decision making. Humanized AI shows characteristics of all types of competencies (i.e., cognitive, emotional, and social intelligence), is able to be self-conscious and is self-aware in interactions with others.

Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism,[8][9] followed by disappointment and the loss of funding (known as an "AI winter"),[10][11] followed by new approaches, success and renewed funding.[9][12] For most of its history, AI research has been divided into subfields that often fail to communicate with each other.[13] These sub-fields are based on technical considerations, such as particular goals (e.g. "robotics" or "machine learning"),[14] the use of particular tools ("logic" or artificial neural networks), or deep philosophical differences.[15][16][17] Subfields have also been based on social factors (particular institutions or the work of particular researchers).[13]

The traditional problems (or goals) of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects.[14] General intelligence is among the field's long-term goals.[18] Approaches include statistical methods, computational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, artificial neural networks, and methods based on statistics, probability and economics. The AI field draws upon computer science, information engineering, mathematics, psychology, linguistics, philosophy, and many other fields.

The field was founded on the claim that human intelligence "can be so precisely described that a machine can be made to simulate it".[19] This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence which are issues that have been explored by myth, fiction and philosophy since antiquity.[20] Some people also consider AI to be a danger to humanity if it progresses unabated.[21] Others believe that AI, unlike previous technological revolutions, will create a risk of mass unemployment.[22]

In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science, software engineering and operations research.[23][12]

Machine Learning

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on models and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model of sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task.[1][2]:2 Machine learning algorithms are used in the applications of email filtering, detection of network intruders, and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning, and focuses on exploratory data analysis through unsupervised learning.[3][4] In its application across business problems, machine learning is also referred to as predictive analytics.

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Artificial Intelligence in Computer Networks Market 2019 Global Trend, Segmentation and Opportunities Forecast To 2025
  –  August 16, 2019
The Artificial Intelligence in Computer Networks Market report also ... The key players covered in this study Cisco Systems Hewlett Packard Enterprise (HPE) IBM Corporation Samsung Electronics Co Ltd Baidu Nvidia Google Microsoft Corporation Dell Nokia ...
The Gap Between Large and Small Companies Is Growing. Why?
  –  August 16, 2019
When we examine the main driver of enterprise performance and growth ... firms and banks are capitalizing on advancements in artificial intelligence to aid their legacy operations.
Enterprise Artificial Intelligence Market 2019 Development Strategy, Future Trends and Industry Growth With 46% of CAGR by Forecast 2023
  –  August 16, 2019
The rate of changes in the industrial technology is largely driven by the inception of artificial intelligence. Artificial intelligence is not considered as a single entity but an amalgamation of a different set of technologies and building blocks that ...
Nvidia CEO: Enterprise AI Is Driving Growth In The Data Center
  –  August 15, 2019
"We are seeing that the wave of AI is going from the cloud to the enterprise to the edge to the autonomous systems," Huang said. Nvidia's data center business, which revolves around server GPUs optimized for artificial intelligence and other workloads ...
Nvidia reports $2.58 billion in Q2 revenue as AI and graphics demand strengthens
  –  August 15, 2019
Nvidia reported earnings and revenues that beat analysts’ expectations as demand for graphics and artificial intelligence chips ... increase was due to enterprise revenue growth driven by ...
AntWorks and Everest Group Unveil Intelligent Document Processing Playbook
  –  August 15, 2019
About AntWorks: AntWorks™ is a global artificial intelligence and intelligent automation company, creating new possibilities with data through digitisation, automation and enterprise intelligence.
How artificial intelligence drives genuine ROI from real customer feedback
  –  August 15, 2019
Artificial intelligence is transforming the effectiveness of marketing ... By next year (2020) Gartner predicts that 85 per cent of interactions with an enterprise will not involve direct contact with a human. This means when customers do speak to customer ...
Enterprise Artificial Intelligence Market Industry Outlook, Market Dynamics and Forecast 2019-2024 - MRE Report
  –  August 14, 2019
Aug 14, 2019 (Heraldkeepers) -- New York, August 14, 2019: The global Enterprise Artificial Intelligence market is expected to exceed more than US$ 12 Billion by 2024 at a CAGR of 42% in the given forecast period. The global Enterprise Artificial ...
What is big data and how important is it to deploy in the enterprise?
  –  August 8, 2019
That is why we are and are increasingly dependent on Big Data tools, which through Artificial Intelligence and machine learning have led us to a new pattern of data analysis. These technologies enable analysts to be able to work with a large data stream wi ...
Why Big Data, IoT, AI and Cloud Are Converging in the Enterprise
  –  August 8, 2019
It has been abundantly clear for quite some time that enterprise technology development has been ... cloud computing, big data, artificial intelligence (AI) and Internet of Things (IoT). While these systems are making work more ‘intelligent’ they ...
Vectra: Ransomware attacks are spreading to cloud, datacenter, and enterprise infrastructure
  –  August 7, 2019
Vectra said artificial intelligence can detect subtle indicators ... workloads and devices in customer clouds, datacenters, and enterprise environments. The analysis of this metadata provides ...
$4.79 Billion Artificial Intelligence in Accounting Market by Component, Deployment Mode, Technology, Enterprise Size, Application, and Region - Globa
  –  August 7, 2019
DUBLIN--(BUSINESS WIRE)--The "Artificial Intelligence in Accounting Market by Component, Deployment Mode, Technology, Enterprise Size, Application (Automated Bookkeeping, Fraud and Risk Management, and Invoice Classification and Approvals), and Region ...
Planview and Tasktop Establish Strategic Partnership Bringing Enterprise-Wide Visibility to Organizations Scaling Agile
  –  August 6, 2019
Enterprise-Class Lean Portfolio Management becomes real ... Yellowfin Named Leader in the Use of Artificial Intelligence.. Amazing Carapelli takes Amazon by storm KONKA Established Joint Venture for the Middle East and Afri.. [Information]Formula E Korea ...
Patriot One and Xtract Technologies Sign Artificial Intelligence Collaboration Agreement
  –  August 1, 2019
Xtract develops and commercializes artificial intelligence, machine learning architectures and deep neural network and predictive solutions utilizing its proprietary technology for public institutions and private enterprise across a variety industry ...
Capitol Watch: New York to take on artificial intelligence
  –  July 27, 2019
ALBANY, N.Y. (AP) — In New York government news, state officials are examining the opportunities — and risks — posed by artificial intelligence. Gov. Andrew Cuomo, a Democrat, signed legislation this month that creates a 13-member commission tasked ...
Enterprise AI: A Look at Three Fundamental Deep Learning Approaches
  –  July 19, 2019
As with other machine learning techniques, deep learning is an important building block for artificial intelligence in the enterprise. First, let’s quickly review what machine learning is. Machine learning refers to the process of training a model ...
Investor Jocelyn Goldfein to join us on AI panel at TechCrunch Sessions: Enterprise
  –  July 18, 2019
Artificial intelligence is quickly becoming a foundational technology for enterprise software development and startups have begun addressing a variety of issues around using AI to make software and processes much more efficient. To that end, we are ...
Artificial Intelligence Is Making Increasing Headway In The Enterprise Back Office
  –  July 10, 2019
Artificial intelligence is making some of the most remarkable progress in back offices of enterprises of all types. The back office is where business operations that support the main customer-facing parts of the organization operate. It handles finance and ...
Cisco introducing artificial intelligence and machine learning capabilities
  –  July 8, 2019
"Artificial intelligence and machine learning can enable businesses ... This allows IT teams to dynamically elevate application performance across the enterprise and branch.Pervasive security: As an industry leader in cybersecurity, Cisco is leveraging ...
How artificial intelligence and machine learning can help financial institutions with fraud prevention
  –  July 4, 2019
Fraud prevention methods need to adopt new age technologies like artificial intelligence and machine learning to stop ... can be actioned upon – in real-time. Especially in the enterprise context, it’s vital that a robust fraud detection tool can ...

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