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When the Facebook parent company rebranded as Meta, the Metaverse’s visibility was raised from an emerging but marginal technology to that of an established one. A fully developed metaverse is still a concept, despite the fact that there are already existing applications as well as public knowledge of AR or VR. It takes two key stages to realize that concept of the Metaverse: first, increasing awareness of the purpose and advantages of the Metaverse; second, having the information systems in place to guarantee efficient and trustworthy performance of Metaverse applications. For instance, latency might result in slow response times and interruptions that make the users’ experience unpleasant. Users may be frustrated by poor accessibility or reliability, and the developing party’s budget may be impacted by excessive bandwidth costs (Wolber, 2022). But one of the main enabling technologies, edge computing, is going through a period of rapid expansion, which will increase the availability of metaverse applications. The worldwide edge computing market is anticipated to increase from $40.84 billion in 2022 to $132.11 billion by 2028, representing a compound annual growth rate of 21.6 percent between 2022 and 2028 (The Insight Partners, 2022). Furthermore, about 10% of data provided by businesses is produced and handled outside of a classic, centralized data center, and is predicted to rise to 75% by 2025 (van der Meulen, 2018). 

Edge Computing

Edge computing is a type of decentralized computing. It addresses the issue of space by processing and storing application-related data as close as possible to the location required via local infrastructure, like servers (Cao, Liu, Meng, & Sun, 2020). This reduces latency and makes it possible for applications to respond instantly by removing the need to transport data to and from the cloud or other networks. Because it enables the seamless operation of metaverse apps at very high speeds, edge computing’s low latency is crucial to enhancing the user experience and satisfaction. At the same time, it also increases the dependability and sturdiness of applications by lowering the likelihood of connection losses, delays, interruptions, lags, etc.

Adopting edge computing can help with bandwidth issues as well. The volume of information or the amount of devices that can exchange data across a network is limiting due to a networks’ restricted bandwidth. One answer is to increase bandwidth, but doing so could have costs that surpass any possible gains, especially when Metaverse applications produce enormous amounts of data. When application data is processed and stored at the edge, it decreases requirements for data transmission. Only information on the result must be transferred to a centralized database in many applications, which reduces network traffic and frees up resources for other important operations. Another significant advantage of data processing and storage at the edge is that gathered data can offer insightful information about user behavior and performance improvements: when a lot of Metaverse applications are distributed throughout several locations, it may be simpler to spot regional trends or problems as a result (Cao et al., 2020).

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• Cao, K., Liu, Y., Meng, G., & Sun, Q. (2020, April 12). An overview on edge computing research. IEEE access, 8, 85714-85728.
• Meulen, R. van der (2018, October 3). What Edge Computing Means for Infrastructure and Operations Leaders. From Gartner: https://www.gartner.com/smarterwithgartner/what-edge-computing-means-for-infrastructure-and-operations-leaders
• The Insight Partners. (2022, August). Edge Computing Market Trends & Size Analysis 2028. From The Insight Partners: https://www.theinsightpartners.com/reports/edge-computing-market
• Wolber, A. (2022, October 4). How edge computing will support the metaverse. From TechRepublic: https://www.techrepublic.com/article/edge-computing-supports-metaverse/#:~:text=Edge%20computing%20supports%20the%20metaverse,a%20conventional%20cloud%20data%20center. 

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The use of digital twins in organizations is transforming decision-making and operational management: they allow organizations to anticipate real life situations, help investigate solutions at reduced risk, and improve customer experiences. 

Artificial intelligence (AI) is quickly becoming a necessity in businesses. AI and machine learning (ML) are crucial business technologies that assist innovative companies in improving process efficiency, customer experiences, and decision-making. Businesses have employed digital twins extensively over the past few years. A digital twin is an exact virtual copy of reality, so data from reality is also fed into the twin. By continuously entering real-time data, the model changes along with reality and it becomes possible not only to show reality, but also to make predictions (Batty, 2018). In other words: it is a combination of the elements of reality supplemented with the dynamics of reality. For greater operational knowledge, they may virtually replicate physical systems, environments and products (Batty, 2018). They also have enabled organizations to examine business strategies and creative business models. As AI and ML technologies have grown and as the cloud’s scalability has evolved, the use of digital twins has begun to accelerate. According to a recent survey by PwC, it is indicated that that 96% of US firms currently anticipate using AI simulations this year (PwC, 2022). 

Many businesses want to go beyond employing digital twins to predict real-time insights on existing performance. Instead, they want to simulate and anticipate human behavior to assess potential future scenarios. They are generating simulation knowledge that is already found in operating systems by merging scientific computing, simulations, and AI. The development of digital twins into simulations knowledge enables speedy and macro-scalable forecasting that is directly adopted into corporate analytics and IT structures. This enables the simulation of incredibly complicated systems. Using a digital twin, you may test every choice option to determine which outcomes are the best. The time or cost necessary for real-world experimentation are not a problem anymore (El Saddik, 2018). 

AI Simulations in Practice

Artificial intelligence simulations are revolutionizing how we conduct business these days through disruptive practical uses. According to the abovementioned survey, AI simulations are used to predict market dynamics (57%), enhance supply chains (54%) discover markets (54%), and improve recruitment (39%) (PwC, 2022). While digital twins can simulate real-life settings, AI simulations can go further than this. By simulating a vast number of alternative situations simultaneously and applying highly sophisticated “humanized” reasoning to them, AI simulation systems may project expected events and outplay real-life actions without taking the risks associated with them (Pettey, 2017). In the extremely rapidly changing and competitive landscape of today, this is a big advantage. The key to success is the ability to implement AI simulations at the scale necessary to provide advantage. In order to do this, organizations must incorporate simulations into their overall operations (El Saddik, 2018). 

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