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 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.
Brand awareness
Brand awareness refers to the extent to which customers are able to recall or recognise a brand.
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, "AI is whatever hasn't been done yet."[3][citation not found] 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.

Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism,[7][8] followed by disappointment and the loss of funding (known as an "AI winter"),[9][10] followed by new approaches, success and renewed funding.[8][11] For most of its history, AI research has been divided into subfields that often fail to communicate with each other.[12] These sub-fields are based on technical considerations, such as particular goals (e.g. "robotics" or "machine learning"),[13] the use of particular tools ("logic" or artificial neural networks), or deep philosophical differences.[14][15][16] Subfields have also been based on social factors (particular institutions or the work of particular researchers).[12]

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.[13] General intelligence is among the field's long-term goals.[17] 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".[18] 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.[19] Some people also consider AI to be a danger to humanity if it progresses unabated.[20] Others believe that AI, unlike previous technological revolutions, will create a risk of mass unemployment.[21]

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.[22][11]

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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New Research Uncovers $500 Million Enterprise Value Opportunity with Data Literacy
  –  October 12, 2018
Despite a clear correlation between enterprise value and data literacy ... “With the greater presence of automation, robotics and artificial intelligence, the fourth industrial revolution is looming. Data will be its universal language and those ...
AI in the enterprise: Are you a trailblazer or laggard?
  –  October 11, 2018
You’ve heard it from McKinsey, MIT, our own coverage, and elsewhere: Artificial Intelligence is a must-have capability for your business. These reports don’t provide much data yet on the areas ...
MapR to Present on Cloud, AI, Kubernetes and Deep Learning at Upcoming Industry Conferences
  –  October 11, 2018
When: Tuesday, October 23 at 2:30 PM Where: Ronald Reagan Building and International Trade Center, Washington, D.C. What: Jim Scott, vice president, enterprise architecture ... disruptions in data, artificial intelligence, cloud, containers, and edge ...
AirAsia Working with Google Cloud to Enhance Business with AI and Machine Learning
  –  October 11, 2018
AirAsia is working with Google Cloud to integrate machine learning and artificial intelligence (ML/AI ... transform its way of work and culture by deploying G Suite and Chrome Enterprise to create a more agile digital experience that will provide access ...
C3 and Amazon Web Services Expand Collaboration to Drive Enterprise Artificial Intelligence Adoption
  –  October 10, 2018
REDWOOD CITY, Calif.--(BUSINESS WIRE)--Oct 10, 2018--C3 and Amazon Web Services (AWS) today announced an expanded collaboration focused on accelerating customers’ time to value with complex and strategic artificial intelligence (AI) applications across ...
MapR Announces Complementary Data Management and Logistics for RAPIDS Open-Source Software From NVIDIA
  –  October 10, 2018
--(BUSINESS WIRE)--Oct 10, 2018--MapR Technologies, Inc., provider of the industry’s leading data platform for Artificial Intelligence (AI ... the ability to coordinate data flows from across the enterprise and, through a pre-built MapR container ...
More Than 86% of Businesses And Organizations to Use A.I by 2025
  –  October 10, 2018
By 2025, more than 86% of businesses and organizations will by using Artificial Intelligence (A.I), and ... Chen Zhijun, President of Huawei Enterprise Southern Africa Region, said, human society ...
Chatbot Market to Reach US$0.94 Billion by 2024 at a CAGR of 27.8%; Rising Advancements in Artificial Intelligence to Boost Demand - TMR
  –  October 10, 2018
Furthermore, artificial intelligence has also progressed the way in which chatbot ... titled "Chatbot Market (Platform - Web-based, Mobile, and Standalone; Enterprise Size - Small and Medium Enterprises and Large Enterprises) - Global Industry Analysis ... 7 Global 5,000 Corporate Enterprise Practitioners Speaking on AI EMEA LIVE, 5th-7th November
  –  October 10, 2018
TOPICS INCLUDE: Editor-in-Chief, Seth Adler noted the importance of the gathering, "AI & Intelligent Automation corporate enterprise practitioners are setting their sites on a future that allows artificial intelligence to learn and assist in decision-making.
Chinese tech giant Huawei unveils A.I. chips, taking aim at giants like Qualcomm and Nvidia
  –  October 10, 2018
Huawei unveils two new artificial intelligence (AI) chips called the Ascend 910 and ... Huawei is hoping to drive further growth to its enterprise business. That business accounted for just over 9 percent of Huawei's revenues in 2017 and grew around ...
AI tools could push RPA to the forefront of enterprise automation
  –  October 9, 2018
Yet, companies in the RPA field, such as UiPath Inc., are gaining more visibility, in part because the field’s artificial intelligence ... technology and further enterprise adoption.
USC Chooses Univa to Manage Growing Infrastructure and Accelerate Machine Learning Research
  –  October 9, 2018
based on Univa’s enterprise-class performance and capabilities, built-in advanced GPU support, detailed documentation and ongoing product upgrades. “The basis for artificial intelligence and machine learning research is to create neural networks to ...
Breach and Attack Simulation: Find Vulnerabilities before the Bad Guys Do
  –  October 8, 2018
Some rely on artificial intelligence and machine learning ... Thanks to the rise of cloud computing and the Internet of Things (IoT), enterprise networks themselves are evolving all the time. Large enterprises may be using computing resources that are ...
Io-Tahoe Webinar: AI, ML and Smart Data Discovery; Getting Developers…
  –  October 8, 2018
Getting developers the most from advanced technologies to drive business results; preparing for the future with artificial intelligence ... Io-Tahoe ( is an enterprise AI-driven data discovery and catalog product that enables ...
Defusing The Perils Of Enterprise AI
  –  October 8, 2018
As enterprises race to unlock the potential benefits of artificial intelligence (AI), they are focused on vetting ... The performance of traditional enterprise software, once implemented, is typically measured by a simple question: “Is the system up?”
IBM to Present CIO's Guide to Enterprise #DigitalTransformation | @IBMcloud @CloudEXPO @SmarterFitz #Cloud #CIO #SmartCities
  –  October 6, 2018
As your enterprise creates a vision and strategy that enables ... which will incorporate FinTech and Blockchain, as well as machine learning, artificial intelligence and deep learning in these two distinct tracks. FinTech brings efficiency as well ...
The Most Important Skills for the 4th Industrial Revolution? Try Ethics and Philosophy.
  –  October 6, 2018
The push to develop and apply artificial intelligence technologies has also naturally raised ... It is also a sort of social enterprise where we engage with other people,” he said. “When we educate people in AI and the technologies of the Fourth ...
Frustum Generate: A New Take On Generative Design and Topology Optimization?
  –  October 5, 2018
It combines the creativity of the engineer with artificial intelligence to significantly shorten the ... Adequacy of information across the enterprise can be checked by software and humans. MBD software tools automate and resolve execution deficiencies ...
Top VC deals: Elastic and Upwork IPO; cybersecurity, AI and biotech start-ups close rounds
  –  October 5, 2018
Online freelancing platform Upwork and enterprise search company Elastic debuted on the public markets this week. Start-ups specializing in software, artificial intelligence and biotech closed funding rounds. Benchmark opened a new fund, without two of its ...
Restoring the balance between virtualization and bare metal through composable infrastructure
  –  October 4, 2018
With continued innovation in artificial intelligence and the internet of things, enterprise need for big data processing is expected to increase exponentially. DriveScale aims to offer all ...




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