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Metaverse: Facebook's new avatar - Part 1

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Recently Facebook CEO Mark Zuckerberg introduced Meta, which brings together their apps and technologies under one new company brand. Facebook will still continue to be known as Facebook but the governing company will be titled as Meta. Meta’s focus will be to bring the metaverse to life and help people connect, find communities and grow businesses. But what is metaverse? Author Neal Stephenson is credited with coining the term "metaverse" in his 1992 science fiction novel "Snow Crash," in which he envisioned lifelike avatars who met in realistic 3D buildings and other virtual reality environments. Since then, various developments have made mileposts on the way toward a real metaverse, an online virtual world which incorporates augmented reality, virtual reality, 3D holographic avatars, video and other means of communication. As the metaverse expands, it will offer a hyper-real alternative world for you to coexist in. Inklings of the metaverse already exist in

AIOps - Artificial Intelligence in IT Operations - Part 2

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  In first part of the article , we looked at what is AIOps, how it works and what benefits you have using AIOps in your IT Operations. Now let us look at some AIOps strategies and go through some tools that have come up in the last 5+ years. AI Ops Strategies Don’t wait. Become familiar with AI and ML vocabulary and capabilities today, even if an AIOps project isn’t imminent. Priorities and capabilities change, so you may need it sooner than you expect. Choose initial test cases wisely. Transformation initiatives benefit from starting small, capturing knowledge and iterating from there. Take the same approach to incorporating AIOps for success. Develop and demonstrate your proficiency. Demystify AIOps for your colleagues and leadership by demonstrating simple techniques. Experiment freely. Although AIOps platforms are often products of substantial cost and complexity, a great deal of open-source and low-cost ML software is available to enable you to evaluate AIOps Standardize

AIOps - Artificial Intelligence in IT Operations - Part 1

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  What's AI Ops? AIOps means Artificial Intelligence in IT Operations. The term was coined by Gartner. AIOps is the application of machine learning (ML) and data science to IT operations problems.  AIOps platforms combine big data and ML functionality to enhance and partially replace all primary IT operations functions, including availability and performance monitoring, event correlation and analysis, and IT service management and automation. AIOps platforms consume and analyze the ever-increasing volume, variety and velocity of data generated by IT and present it in a useful way. How AI Ops work AIOps performs IT operations through self learning and self correcting systems. AIOps deploys: Machine Learning techniques to understand the ever changing IT environment Artificial Intelligence to detect abnormalities Intelligent automation to remediate abnormalities before it impacts It learns from the kind of events and way those events are happening rather than a knowled

Artificial Intelligence in Retail - Part 3

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  In part 1 and part 2 of the article, we identified the use of Artificial Intelligence in Retail industry and provided some examples of how AI is helping some of the big brands in Retail industry. In this part, let us discuss some use cases where AI will be useful in reshaping the way Retail industry works. AI technologies have been integrated into retail and marketing by using big data analytics to develop customer-specific profiles and their anticipated buying habits. Knowing and forecasting consumer’s demand across interconnected supply chains is more important than ever, and AI technology is expected as an essential component. Here are a few use cases  of using Artificial Intelligence in the retail industry: AI Is the New Personal Salesperson Retailers can use AI to discover new sources of revenue to improve performance, as well as to identify and execute strategies for successful customer relationship management (CRM). AI automates the repetitive sales activities, acts as a

Artificial Intelligence in Retail - Part 2

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  In part 1 of the article on Artificial Intelligence in Retail, we saw how AI is transforming the retail industry and helping the industry grow in sales and profits. Let us dive deeper into the topic with some live examples. AI refers predominantly to computational technology driven by ways in which people use their brains’ neurons and nervous systems to reason and make conclusions and decisions, although they usually work very differently. There are  several ways through which retail can benefit from the robotic system. For instance, the attractiveness of robotic technology will encourage customers to engage more in shopping activities, and this will help the retailers boost their sales. Customers will benefit from faster and intelligent guidance during shopping and thus make more effective and efficient purchase decisions. Robotic technology will support retailers’ endeavours towards minimizing the personnel costs and enhancing the staff well-being. But consultant jargon and t

Artificial Intelligence in Retail Industry - Part 1

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  Retailing has always been called a combination of art and science but for much of its existence there’s usually been too much of the former and not enough of the latter. Traditional retailers' business models arc facing disruption by new entrants who can deliver greater value to customers more efficiently. In recent years, authors have argued that the traditional value chain drives inefficiencies and that the value chain is shortening as manufacturers, third parties and customers are increasingly engaging with customers directly. These inefficiencies combined with the inability to adapt to a changing competitive landscape leaves traditional retailers vulnerable to disruption from market entrants. To remain competitive and survive in an ever-changing and diversified customer market, retailers need to become leaner , more agile, and innovate their value chain by adopting new technologies. Of the new technologies that are impacting the retail industry, AI has been earmarked as

Artificial Intelligence in Law Enforcement - Part 2

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  In the first part of the article on AI use in Law Enforcement, we introduced how Machine Learning and Artificial Intelligence is being used by the law enforcing agencies around the world and what are the pros and cons of using this emerging technology. Now let us discuss different ways by which the police can use machine learning to achieve better results: 1) Pattern recognition One of the most powerful applications of machine learning in policing is in the field of pattern recognition. Crimes can be related and may either be carried out by the same person(s) or may use the same modus operandi. The police can benefit if they are able to spot patterns in crimes. The data that the police get from crimes is essentially unstructured data. This data needs to be organized and sifted through to find patterns. Machine learning tools can compare various crimes easily and generate a similarity score. These scores can then be used by the software to try and determine if there are common patter