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
 
Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals.
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 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

Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. In computer science AI research is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.[1] Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving".[2]

The scope of AI is disputed: as machines become increasingly capable, tasks considered as requiring "intelligence" are often removed from the definition, a phenomenon known as the AI effect, leading to the quip in Tesler's Theorem, "AI is whatever hasn't been done yet."[3] For instance, optical character recognition is frequently excluded from "artificial intelligence", 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, and intelligent routing in content delivery networks and military simulations.

Borrowing from the management literature, Kaplan and Haenlein classify artificial intelligence into three different types of AI 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 as well as emotional intelligence, understanding, in addition to cognitive elements, also human emotions considering them in their decision making. Humanized AI shows characteristics of all types of competencies (i.e., cognitive, emotional, and social intelligence), able to be self-conscious and 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 others.

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 is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.[1]

The name machine learning was coined in 1959 by Arthur Samuel.[2] Evolved from the study of pattern recognition and computational learning theory in artificial intelligence,[3] machine learning explores the study and construction of algorithms that can learn from and make predictions on data[4] – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions,[5]:2 through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders or malicious insiders working towards a data breach,[6] optical character recognition (OCR),[7] learning to rank, and computer vision.

Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining,[8] where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning.[5]:vii[9] Machine learning can also be unsupervised[10] and be used to learn and establish baseline behavioral profiles for various entities[11] and then used to find meaningful anomalies.

Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to "produce reliable, repeatable decisions and results" and uncover "hidden insights" through learning from historical relationships and trends in the data.[12]

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Automation Anywhere and DataRobot Partner on AI Solutions for Digital Transformation
  –  December 11, 2018
The integration with DataRobot's platform extends Automation Anywhere's cognitive automation capabilities to include decision-making using automated machine learning and artificial intelligence ... including New Enterprise Associates, Sapphire Ventures ...
JDA Launches Luminate Control Tower - 'Digital Nerve Center' that Predicts and Prevents Supply Chain Disruptions and Maximizes Opportunities
  –  December 11, 2018
This allows companies to sense unexpected events across their cross-enterprise supply chains ... the Internet of Things (IoT), artificial intelligence, advanced analytics and cross-platform integration. JDA is highlighting JDA Luminate Control Tower ...
4iQ Secures $18 Million in Series B Funding from C5 and ForgePoint; Adds Board Members
  –  December 11, 2018
About C5 Capital C5 Capital Limited (C5) is as specialist venture capital firm, focusing on Innovative Technologies in Cyber Security, Artificial Intelligence ... focus on digital transformation of enterprise with a cross-border innovation theme.
NewYork-Presbyterian Hospital Selects full suite of Philips Health IT and Clinical Informatics Solutions
  –  December 11, 2018
By implementing Philips IntelliSpace Enterprise Edition, the health system has a reliable ... "As a national leader in the use of artificial intelligence and telemedicine, NewYork-Presbyterian is committed to harnessing technology to put patients first."
Data Democratization And AI: A Vote For Wider Accessibility
  –  December 11, 2018
This is where artificial intelligence (AI) comes into play ... of course, beneficial for the enterprise as a whole. However, we must also consider the risk from within the enterprise that comes with increased access to business data.
Kofax RPA Wins DM Award for Artificial Intelligence / Robotic Process Automation Product of the Year
  –  December 11, 2018
“By enabling organisations to easily and cost effectively scale their RPA deployments across the enterprise, we’ve raised the bar with Intelligent Automation – the next generation of RP
An Action Plan for Artificial Intelligence in 2019
  –  December 10, 2018
Last year, PricewaterhouseCoopers gave us predictions for how artificial intelligence would be used ... and establish enterprise-wide data policies. It also should determine technology standards ...
Forces and trends shaping the future of enterprise analytics in 2019
  –  December 10, 2018
The future of enterprise analytics is inextricably woven with artificial intelligence and machine learning. From automating the analytics pipeline to making the quick inferences a human is just not capable of, AI and ML will revolutionize what business can ...
Intelligent automation brings benefits for petroleum refining, healthcare, finance and more
  –  December 10, 2018
https://intelligentautomationevent.iqpc.com/) brought together representatives from many varied industries to discuss practical applications for artificial intelligence (AI ... the director of enterprise business improvement at Marathon Petroleum Corp ...
TransPerfect Introduces TransCEND 11 Secure Enterprise Document Management Platform
  –  December 7, 2018
Built on TransPerfect’s longstanding document collaboration technology, TransCEND provides enterprise clients with an enhanced ... multi-jurisdictional hosting capabilities, and powerful artificial intelligence services, Microsoft Cloud is the ideal ...
10 trends impacting infrastructure and operations for 2019
  –  December 7, 2018
“The question is already becoming ‘How can we use capabilities like artificial intelligence (AI), network automation ... of assets that are “out there” in any given modern digital enterprise. “There has been huge growth in the range and quantity ...
Why Huawei arrest deepens conflict between US and China
  –  December 6, 2018
The Trump administration has tightened regulations on high-tech exports to China and made it harder for Chinese firms to invest in U.S. companies or to buy American technology in cutting-edge areas like robotics, artificial intelligence and virtual reality.
Enterprise AI: The Ongoing Quest for Insight and Foresight
  –  December 6, 2018
Artificial intelligence — specifically machine learning and deep ... and revolutionary (not exactly), but integrating AI into enterprise processes is also the next natural step in how companies innovate, operate, and compete. The time to start is now.
Lessons from IBM and Wang: Startups will write next chapter for John Chambers after Cisco
  –  December 6, 2018
The gift will form a philanthropic venture capital fund to support startup innovation and create a new Center for Artificial Intelligence ... tool for running the enterprise, and “Big Blue ...
CRN Exclusive: Google Channel Chief Tells Partners To 'Put On Your Seat Belt And Jump In Now'
  –  November 20, 2018
Google is giving its competition a run for its money in the artificial intelligence and machine-learning space ... Google Cloud CEO Diane Greene, who was critical in helping Google land more enterprise customers, last week said she would be stepping ...
Blackberry buys artificial intelligence firm Cylance for $1.4bn
  –  November 16, 2018
and embedded systems will make Blackberry Spark indispensable to realising the Enterprise of Things (EoT)." Founded in 2012, Cylance uses artificial intelligence and machine learning in cyber security software which can predict and prevent both known and ...
Veritas Predictive Insights Uses Artificial Intelligence and Machine Learning to Predict and Prevent Unplanned Service
  –  November 16, 2018
the worldwide leader in enterprise data protection and the software-defined storage market, today announced the launch of Veritas Predictive Insights, a new solution that utilizes artificial intelligence (AI) and machine learning (ML) algorithms to deliver ...
7 Questions Boards Need to Ask about Artificial Intelligence and Other Transformative Technologies
  –  November 13, 2018
If you are affiliated with this page and would like it removed please contact pressreleases@franklyinc.com SOURCE WomenCorporateDirectors Education and Development Foundation, Inc. "Why Boards Need to Understand the Impact of Artificial Intelligence (AI ...
Machine Learning and Artificial Intelligence
  –  November 5, 2018
It is a field that uses algorithms to learn from data and make predictions. It is also an application of Artificial Intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Startup CYR3CON’s Artificial Intelligence Predicts Enterprise…
  –  November 5, 2018
Peer-reviewed studies on the company’s new DarkMention technology show how it can predict and prevent cyberattacks against the enterprise as well as counter cryptocurrency threats Recent peer-reviewed studies by artificial-intelligence powered ...
Artificial Intelligence Faces Age-Old IT Challenge: Data Standardization
  –  October 23, 2018
NEW YORK — Artificial intelligence, among its many roles in the enterprise, has the potential to wholly transform a large company’s information technology infrastructure -- from automating mundane and repetitive IT tasks to predicting capacity and ...
Red Hat, NVIDIA regulate on open source offerings to accelerate emerging workloads like artificial intelligence
  –  October 23, 2018
Red Hat announced on Tuesday that it is collaborating with NVIDIA to bring a new wave of open innovation around emerging workloads like artificial intelligence (AI), deep learning and data science to enterprise data centers around the world. Driving this ...
Artificial Intelligence as a Service (AIaaS) Market Capacity, Generation, Investment Trends, Regulations and Company Profiles report to 2018
  –  September 28, 2018
owing to increase in demand for the artificial intelligence industry solutions. Based on organization size, the market is categorized into small & medium enterprise and large enterprise. By industry vertical, the market is classified into BFSI, retail ...

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