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 and 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.
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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 and animals. Computer science defines AI research 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 used to describe machines that mimic "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving".[2]

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. 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, and 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 a 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 a field of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed.[2]

The name machine learning was coined in 1959 by Arthur Samuel.[1] Machine learning explores the study and construction of algorithms that can learn from and make predictions on data[3] – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions,[4]: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, 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,[5] where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning.[6][7]

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.[8]

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Daily Briefing: NUS Enterprise and MPA to invest over $600,000 in 13 maritime startups; E-scooter firm Lime to set up Singapore HQ
  –  April 15, 2019
The Maritime and Port Authority of Singapore (MPA) and NUS Enterprise, the entrepreneurial arm of ... “meet local safety and usage needs”. Artificial intelligence (AI) and predictive analytics provider AIDA Technologies has raised an undisclosed ...
Masters 2019: Tiger Woods finds a way to beat Artificial Intelligence, too
  –  April 15, 2019
not only beat Francesco Molinari and Brooks Koepka (and the storm that swept into Augusta on Sunday afternoon), but he beat the Artificial Intelligence of IBM Watson. This year for the first time ...
5G is a bigger deal and China is a bigger threat than you think, think tank says
  –  April 14, 2019
For consumers, 5G will bring lots of advancements, including the potential to replace home Wi-Fi networks, smarter artificial intelligence on phones and ... Huawei as “a functioning subservient enterprise to Chinese intelligence and defense services ...
Profits Can Wait As Freshworks Targets A Cloud Market Dominated By SAP And Salesforce
  –  April 12, 2019
Besides Salesforce and ServiceNow, Freshworks competes with more than 600 other companies—including Zoho, eGain, SugarCrm and Zendesk—in the crowded market for cloud-based enterprise ... incorporate additional artificial intelligence, machine learning ...
Hughes to support USAF Protected Tactical Enterprise Service programme
  –  April 12, 2019
These subsystems will support anti-jam satellite communications capability for the USAF’s Protected Tactical Enterprise Service (PTES ... architecture to enable the use of advanced artificial intelligence and machine learning techniques.”
Barbarians at the Gate: AI and Copyright on a Collision Course
  –  April 12, 2019
class="p1">While the steady advance of applied artificial intelligence (AI) technology promises to dramatically ... dilemma may be an “AI license” priced along the lines of an enterprise license. Such a model could help ensure fair value when the ...
Artificial Intelligence Is Helping Evaluate 1.1 Million Security Clearance Holders
  –  April 11, 2019
While artificial intelligence is key to the future of background investigations ... “We moved the NBIS architecture to an enterprise data broker concept and we moved the data sources to one place,” he explained. “This gave us the ability to focus ...
Juniper Closes a Gap in Its Connectivity Solution and Moves into Indoor Location
  –  April 11, 2019
In the beginning of March, Juniper Networks, a provider of products and solutions in enterprise networking and cybersecurity ... Besides offering wireless connectivity hardware and artificial intelligence (AI)-driven network management, Mist also includes ...
Global venture capital investment drops in Q1 2019, with Europe and the US remaining relatively strong: KPMG Enterprise Venture Pulse report
  –  April 11, 2019
Asia continued to attract considerable attention from VC investors in key areas including artificial intelligence, automation ... All figures quoted are in USD. About KPMG Enterprise You know KPMG, you might not know KPMG Enterprise.
Honeywell Integrates Intel® Vision Products to Add Artificial Intelligence Capabilities to Video Security and Surveillance
  –  April 10, 2019
Honeywell has announced a first-of-its-kind technology integration with Intel that will enable new artificial intelligence (AI ... sensitive environments including enterprise campuses, pharmaceutical companies, and banking and financial institutions.
Vendavo Introduces New Artificial Intelligence Deal Price Guidance Solution
  –  April 10, 2019
VDPG is an intelligent, cloud-native solution that delivers AI-enabled, deal-specific pricing guidance directly to a customer’s CRM and quotation workflows while continuously optimizing deal win rates, enterprise profitability targets, and the overall cu ...
Akon Enlists Artificial Intelligence In Mission To Extend His Green Energy Vision In Africa And Beyond
  –  April 10, 2019
About MondoBrain MondoBrain is the global leader in Enterprise Augmented Intelligence. The company’s advanced AI-enabled decision support suite is the first to integrate human, collective and artificial intelligence. The solution leverages a unique ...
CYFIRMA Named in Enterprise Security Magazine's 'Top 10 Artificial Intelligence Solution Providers - 2019'
  –  April 9, 2019
CYFIRMA's proprietary Artificial Intelligence-enabled Cyber Intelligence Analytics Platform (CAP), offers real-time insights into emerging threats, active hackers – their attack motives and methods, allowing organizations to have complete visibility into ...
MemVerge Launches Memory-Converged Infrastructure to Power the Most Demanding AI and Data Science Enterprise Workloads
  –  April 7, 2019
Companies are using MemVerge to train artificial intelligence (AI) models faster and to successfully ... “Data-intensive applications are pushing traditional enterprise infrastructures beyond their limits, especially as organizations scramble to ...
15 Pakistani Startups Thundering The World Of Artificial Intelligence in Asia And Beyond
  –  April 5, 2019
and development and operation of big data and artificial intelligence models to connect enterprise outcomes with AI. ADDO AI mainly focuses on developing smart cities with a centralized operating ...
How enterprise AI will change brick and mortar retail
  –  April 5, 2019
According to Deloitte’s State of AI in the Enterprise 2018 report, 82% of early adopters of AI saw positive ROI. Revenues from the artificial intelligence for enterprise applications market worldwide is set to grow exponentially through 2025. One ...
Artificial Intelligence and Machine Learning in Practice: Anomaly Detection in Army ERP Data
  –  April 3, 2019
With this priority in mind, the Army recently launched a project to enhance its supply chain data environments by leveraging the power of artificial intelligence (AI) and machine learning (ML).
Enterprise Artificial Intelligence Market 2019 Global Industry - Key Players, Size, Trends, Application and Growth- Analysis Forecast to 2025
  –  April 2, 2019
Apr 02, 2019 (AB Digital via COMTEX) -- Enterprise Artificial Intelligence Market 2019 This report analyzes the global Enterprise Artificial Intelligence market by solution (business intelligence, customer management, sales & marketing), service ...
Enterprise Artificial Intelligence Market 2019 Global Share, Trends, Segmentation, Analysis and Forecast to 2025
  –  April 2, 2019
Apr 02, 2019 (Heraldkeeper via COMTEX) -- Enterprise Artificial Intelligence Market 2019 This report analyzes the global Enterprise Artificial Intelligence market by solution (business intelligence, customer management, sales & marketing), service ...
Survey: Tech leaders cautiously approach artificial intelligence and machine learning projects
  –  April 1, 2019
Read More Enthusiasm for artificial intelligence (AI) and machine learning (ML ... SEE: Special report: Managing AI and ML in the enterprise (free PDF) Staff readiness wasn't the only concern respondents noted about imminent AI/ML projects.
Free PDF download: Managing AI and ML in the enterprise
  –  April 1, 2019
Read More As the enterprise's enthusiasm for artificial intelligence (AI) and machine learning (ML) continues to gain momentum, tech leaders must understand, manage, and support new initiatives focused on these emerging technologies. ZDNet and TechRepublic ...
Without JEDI, Pentagon’s Artificial Intelligence Efforts May Be Hindered
  –  March 29, 2019
The Pentagon won’t be able to maximize artificial intelligence’s true capabilities without an enterprise cloud solution, something it’s currently trying to contract out. The Defense Department needs enterprise cloud computing to make the most of its ...
Singapore to invest $700m in food, medicine and digital tech research for long-term competitiveness
  –  March 27, 2019
Giving an update on the Research, Innovation and Enterprise (RIE) 2020 plan for Singapore's science and technology research, Mr Heng said more than $500 million will be set aside to build up artificial intelligence systems and meet national cyber security ...
Hacker AI vs. Enterprise AI: A New Threat
  –  March 21, 2019
Artificial intelligence and machine learning are being weaponized ... They could be politically motivated incursions, nation-state attacks, enterprise attacks to exfiltrate intellectual property, or financial services attacks to steal funds — the list ...
Amazon and Nvidia bring artificial intelligence to the cloud with T4 GPUs
  –  March 19, 2019
Artificial intelligence and machine learning aren’t new concepts ... Companies offering the new servers include Cisco, Dell EMC, Fujitsu, HP Enterprise, Inspur, Lenovo, and Sugon. For businesses interested in the deployment of Nvidia T4 GPUs on AWS ...




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