Introduction to Weave

Introduction to Weave

Contact Us


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.

More Info


New videos
Artificial intelligence
Artificial intelligence (AI) is intelligence exhibited by machines.
Discoverability is the ability of something, especially a piece of content or information, to be found.
Brand awareness
Brand awareness refers to the extent to which customers are able to recall or recognise a brand.
New videos
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.

Photos - Product Photos12

Artificial Intelligence

Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, the field of AI research defines itself as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of success at some goal.[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]

As machines become increasingly capable, mental facilities once thought to require intelligence are removed from the definition. For instance, optical character recognition is no longer perceived as an example of "artificial intelligence", having become a routine technology.[3] Capabilities currently classified as AI include successfully understanding human speech,[4] competing at a high level in strategic game systems (such as chess and Go[5]), self-driving cars, intelligent routing in content delivery networks, military simulations, and interpreting complex data.

AI research is divided into subfields[6] that focus on specific problems, approaches, the use of a particular tool, or towards satisfying particular applications.

The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects.[7] General intelligence is among the field's long-term goals.[8] Approaches include statistical methods, computational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics. The AI field draws upon computer science, mathematics, psychology, linguistics, philosophy, neuroscience, artificial psychology 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".[9] This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence, issues which have been explored by myth, fiction and philosophy since antiquity.[10] Some people also consider AI a danger to humanity if it progresses unabatedly.[11] Attempts to create artificial intelligence have experienced many setbacks, including the ALPAC report of 1966, the abandonment of perceptrons in 1970, the Lighthill Report of 1973, the second AI winter 1987–1993 and the collapse of the Lisp machine market in 1987.

In the twenty-first century, AI techniques, both hard (using a symbolic approach) and soft (sub-symbolic), have experienced a resurgence following concurrent advances in computer power, sizes of training sets, and theoretical understanding, and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science.[12] Recent advancements in AI, and specifically in machine learning, have contributed to the growth of Autonomous Things such as drones and self-driving cars, becoming the main driver of innovation in the automotive industry.

Machine Learning

Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed.[1][2][verify]

Arthur Samuel, an American pioneer in the field of computer gaming and artificial intelligence, coined the term "Machine Learning" in 1959 while at IBM[3]. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence,[4] machine learning explores the study and construction of algorithms that can learn from and make predictions on data[5] – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions,[6]: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,[7] optical character recognition (OCR),[8] 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,[9] where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning.[6]:vii[10] Machine learning can also be unsupervised[11] and be used to learn and establish baseline behavioral profiles for various entities[12] 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.[13]

According to the Gartner hype cycle of 2016, machine learning is at its peak of inflated expectations.[14] Effective machine learning is difficult because finding patterns is hard and often not enough training data is available; as a result, machine-learning programs often fail to deliver.[15][16]

Value Proposition6

Key Applications18

Key Technologies11


News Articles24

Navis, ZPMC, Microsoft China and Moffatt & Nichol to Explore Potential Business Opportunities, Turnkey Solution for Automation
  –  November 17, 2017
and artificial intelligence,” said Yen Tseng, General Manager of Microsoft China Enterprise & Partner Group in Greater China Region. “As a leading technology provider, Microsoft is cooperating with leading partners in bringing the best-in-class ...
Futurist, visionary, leader and evangelist — what's the difference?
  –  November 17, 2017
He has written books on health, artificial intelligence (AI), transhumanism ... For example, Industry specific digital evangelist will predict enterprise level changes that digital transformation will bring and he will help redefine the relationship ...
IT Decision-Makers Expect ROI From Artificial Intelligence Within 2 Years
  –  November 17, 2017
The survey, which polled 652 IT decision makers in the U.S., UK, Germany, and France, found that optimism about the value of artificial intelligence (AI)-powered solutions in the enterprise is high and plans to continue investing in the technology are ...
2018 30 Under 30 Includes Millennial Innovators in Learning and Development
  –  November 17, 2017
Artificial intelligence is one of the trends for 2018 explored by ... Building Comprehensive Training Programs Michael Garland and Percia Safar founded Prelude, an enterprise commerce platform backed by Y Combinator, in 2015. The startup was acquired ...
Reply Launches Comprehensive Industrie 4.0 Solutions for Flexible and Connected Manufacturing
  –  November 17, 2017
Brick Reply plays a crucial role for the extended industrial enterprise, enabling planning and management ... components and competencies of Reply as cloud computing, Artificial Intelligence, Internet of Things (IoT) reality solutions as well as additive ...
Salesforce Opens Dreamforce with Huge Google Surprise and More
  –  November 17, 2017
Salesforce customers that haven't previously used Google's paid enterprise services will be able to use ... Financial Services, B2C Marketing, Artificial Intelligence, the Internet of Things, and more. Image credit: Google/Artist's concept.
Oracle Announces Oracle Cloud Infrastructure Options for Enterprise, AI and HPC Applications
  –  November 17, 2017
and artificial intelligence (AI) faster and more cost-effectively. Unlike competitive offerings, Oracle Cloud Infrastructure is built to meet the unique requirements of enterprises, offering predictable performance for enterprise applications while ...
How to use IoT, machine learning and AI bots to grow revenue
  –  November 17, 2017
Artificial intelligence, while not new ... they offer exciting and limitless opportunities for the enterprise. It’s no secret that the internet of things, where all machines are “smart” and connected to one another, is becoming a reality in the ...
Lastline Reveals Predictions and Trends For the 2018 Cyberthreat Landscape
  –  November 16, 2017
EIN News/ -- However, technological advances in artificial intelligence (AI) and machine learning (ML ... We deliver the visibility, context, analysis, and integrations enterprise security teams need to quickly and completely eradicate malware-based ...
PubMatic and Adelphic Partner for Supply Path Optimization
  –  November 16, 2017
Over the past two years, in response to the rise of inventory processing costs for DSPs with the proliferation of header bidding, PubMatic has been developing machine learning and artificial intelligence ... for publishers and enterprise-grade programmatic ...
30 Under 30 In Enterprise Tech: Reinventing Business With Artificial Intelligence
  –  November 16, 2017
In 2016 Alexandr Wang, 20, took time off from his graduate computer science studies at MIT. He headed to Silicon Valley to join his friend and fellow developer Lucy Guo, 23. Two years earlier, Guo – a 2014 Thiel Fellow – had left her senior year at ...
Techonomy: 5G, AI in Healthcare, and American Competitiveness
  –  November 16, 2017
Some of the big topics in the technology industry these days are artificial intelligence, 5G, and American competitiveness ... Security and Innovation’s Promise AI for decision support in the enterprise is a $2 trillion opportunity, according to John ...
AI, Combined Actions and Digital Strategies | @ExpoDX #AI #Cloud #DigitalTransformation
  –  November 16, 2017
Improvements in our thinking capabilities today can be found in the computing power and speed of artificial intelligence, big data analytics ... He has over 30 years of experience working with enterprise applications, and he is a veteran mobile industry ...
Oracle Public Cloud Upgrades Target AI, Enterprise, and HPC Applications
  –  November 16, 2017
Oracle added capabilities to its public cloud geared toward enterprises running infrastructure-heavy workloads including high-performance computing (HPC), big data, and artificial intelligence (AI). The new compute instances — virtual machine (VM ...
IBM Watson needs a makeover, and other predictions in cloud, AI and IoT for 2018
  –  November 16, 2017
The edge is not homogeneous either, requiring a primary layer where the data is generated and then a secondary layer where processing functions, such as artificial intelligence ... has created pain points for the enterprise, starting with AI.
FirstAlign Announces Artificial Intelligence for Finance
  –  November 16, 2017
No matter if you're a Fortune 500 organization or a small to medium enterprise, each will spend significant ... Senior Advisor. "We focus on how Artificial Intelligence has the unprecedented ability to analyze financial data and power leadership to make ...
Algorithmia Launches AI Layer for the Enterprise
  –  November 16, 2017
With the Enterprise AI Layer, Algorithmia’s team deploys the ... that want to deploy AI at any scale,” says Anna Patterson, VP of Engineering, Artificial Intelligence at Google. “Every company with an AI strategy is going to need to build their ...
CRN Applications and OS News
  –  November 16, 2017
Intel is making strong investments in artificial intelligence through acquisitions ... based MSP First National Technology Solutions has provided enterprise-level infrastructure and cloud services for the past decade, but president Kim Whittaker sees ...
CCS Insight reveal 2018 predictions for the security, internet, mobile and enterprise sectors
  –  November 16, 2017
Internet and enterprise and mobile sectors for 2018 and beyond. This year, the predictions cover a broader array of technology areas than before, with references to the impact of artificial intelligence, the rise of internet players and their move towards ...
Microsoft releases Azure Databricks, new AI, IoT and machine learning tools
  –  November 16, 2017
He also touched on key application scenarios and ways developers can use built-in artificial intelligence (AI ... self-service analytics and machine learning over all data with enterprise-grade performance. A preview of Visual Studio Tools for AI was ...
IDG Enterprise's Digital Edge 50: Pushing Digital Boundaries
  –  November 15, 2017
To do that, leading organizations — in both the for-profit commercial and nonprofit sectors — are turning to cutting-edge technologies such as artificial intelligence ... conference in March 2017, IDG Enterprise recognized 50 organizations that ...
Using Dynatrace to Monitor RPA (Robotic Process Automation) Robots and BotFarms.
  –  November 15, 2017
There has been an industry rush to automate the enterprise and the burden to do this has fallen ... Dynatrace uses deterministic Artificial Intelligence to automatically create an entity model of a BotFarm and then uses machine learning algorithms to ...
Factories of the Future: 3 Industrial Internet of Things and analytics predictions
  –  November 15, 2017
Our goal is to deliver this integrated IT and OT platform capability for the enterprise, especially where a platform ... (using everything from text analytics and statistics to Artificial Intelligence and Deep Learning) against the same data.
Transformation, social and big, fat profits
  –  November 15, 2017
“There is a real opportunity here for enterprise leaders to accelerate digital transformation ... Are we ensuring that our technology is accessible? On artificial intelligence, are we building responsible algorithms?” Microsoft also used the Summit ...




Featured Locations4