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Communications and artificial intelligence can change the view of our technological thinking aspects and revolutionize the modern lifestyle.
[How 5G+ change the society, 2020]
[6G from connected People and Things to connected Intelligence, 2021]
AIcon = convergence {5G, IoT, CC, AI}
5G AIoT = Artificial Intelligence of Things
The integration of Artificial Intelligence (AI) and Internet of Things (IoT) has resulted in the development of AIoT (AI+IoT) technology. AIoT has the potential to revolutionize the way we live and work, but it also brings significant challenges.
What are the best practices for standardizing IoT devices and communication protocols to improve interoperability and data sharing between different systems and platforms?
How can we address the skills gap and knowledge required to design, develop, and maintain AIoT systems, particularly for small businesses and organizations with limited resources?
Transition from Smart to Intelligent:
AIcon Smarter multimedia communication [2022]
Cint Connected intelligence [2022]
Econ Extreme connectivity [2024]
3Dcon 3D connectivity [2024]
Mcon Ultra-massive connectivity [2024]
xURLLC Faster (responses&movement), higher (throughput&density), stronger (connectivity&security) - together!
Ubiquitous connectivityUbiquitous connectivity is critical for delivering a wide range of services such as access to, inter alia, education, health, agriculture, transport, logistics, and business opportunities. IMT-2030 is expected to contribute to achieving the UN SDGs, bridging digital divide, by connecting the unconnected and under-connected areas in an efficient manner, by addressing the challenges of connectivity, coverage, capacity, data rate and the mobility of terminals. IMT-2030 is therefore expected to continue to support further development of ubiquitous connectivity that would provide digital inclusion for all by meaningfully connecting the rural and remote communities, further extending into sparsely populated areas, and maintaining the consistency of user experience between different locations including deep indoor coverage.
CONVERGENCE BETWEEN CONNECTIVITY AND INTELLIGENCEIt is important to understand that the technology refresh within the telecommunication industry starts with the international standardisation stage, driven by the ITU defining a series of desired solutions for the upcoming decades. The ITU has approved and published the global 6G vision framework and has begun the technical standardisation stages that follow this(7). The delivery of 6G technology will then be specified by ETSI and 3GPP, working together and taking their lead from the ITU definition for technology readiness by 2030. Also UK’s international technology strategy(8) considers six strategic priorities which identifies AI among the priority technologies alongside Data.The ITU 6G framework goes beyond connectivity and aspires to make this new digital experience accessible to every individual, at every corner of our future, including how we are educated, how we work, how we live our day to day lives, and how we interact with other people and machines.As of now, 6G remains in its early stages. The technological landscape is filled with many potential opportunities. One of the trends we observe is a surge in transformative use cases that emerge as a result of the convergence among connectivity, sensing, and intelligence. For example, AI-enabled Telco services are enabling smart cities and connecting IoT devices. Technology trends, such as digital twins and smart infrastructures, are revolutionising industries such as healthcare with remote diagnosis, optimising logistics with autonomous delivery vehicles, and enhancing entertainment experiences with seamless augmented and virtual reality. The convergence of connectivity and intelligence is driving innovation in the wider society across sectors, including Telco.
Ubiquitous computingIn addition to ubiquitous intelligence, it is expected that ubiquitous use of data computing resources would also expand throughout the IMT-2030. Emerging trends in this regard include expansion of data processing in the network infrastructure to the network cloud and devices that are closer to the origin of the data and support for proliferation of ubiquitous intelligence throughout the IMT-2030. This trend also contributes to the improvements for applications requiring real-time responses and data transport. Ubiquitous computing disseminated across IMT 2030 is expected to enable efficient utilization of resources and optimal placement of workloads, as well as scales and manages the infrastructure to run the applications.
Ubiquitous intelligenceWith the steady progress and fast spread of technologies in AI and particularly machine learning (ML), it is expected that intelligence would be present in every part of the communication system to support the building of smart cities and communities. Future connected devices may become fully context-aware for more intuitive and efficient interactions among humans, machines, and the environment. Possible autonomous management of networks by AI/ML could also be capable of performing self-monitoring, self-organization, self-optimization and self-healing without human intervention. It is expected that the air interface would be enhanced by AI models. IMT-2030 could serve as an AI-enabling infrastructure capable of providing services for intelligent applications. AI-enabled air interface along with distributed computing and intelligence could allow for end-to-end AI applications and the convergence of communication and computing. These systems would have functions of inferences, model training, model deployment, as well as computing distributed across networks and devices.
BIG DATA ANALYTICS AND CLOUD COMPUTINGConnecting a large number of physical objects like humans, animals, plants, smart phones, PCs, etc. equipped with sensors to the Internet generates what is called big data. Big data needs smart and efficient storage. Obviously, connected devicesneed mechanisms to store, process, and retrieve data. But big data is so huge such that it exceeds the capability of commonly used hardware environments and software tools to capture, manage, and process them within an acceptable slot of time. The emerging and developing technology of cloud computing is defined by NIST as an access model to an on-demand network of shared configurable computing sources such as networks, servers, warehouses, applications, and services. Cloud services allow individuals and companies to use remote third-party software and hardware components. Cloud computing enables researchers and businesses to use and maintain many resources remotely, reliably and at a low cost. The IoT employs a large number of embedded devices, like sensors and actuators that generate big data which in turn requires complex computations to extract knowledge. Therefore, the storage and computing resources of the cloud present the best choice for the IoT to store and process big data. In the following subsections, we discuss the relation between the IoT and big data analytics, cloud and fog computing.Big Data. Whatmakes big data an important asset to businesses is that it makes it possible to extract analytics and consequently knowledge, by which a business can achieve competitive advantage. There are some platforms for big data analytics like Apache Hadoop and SciDB. However, these tools are hardly strongenough for big data needs of IoT. The amount of IoT data generally is too huge to be fed and processed by the available tools. In support of the IoT, these platforms should work in realtime to serve the users efficiently. For example, Facebook has used an improved version of Hadoop to analyze billions of messages per day and offer real-time statistics of user actions. In terms of resources, besides the powerful servers in data centers a lot of smart devices around us offer computing capabilities that can be used to perform parallel IoT data analytic tasks.Instead of providing application specific analytics, IoT needs a common big data analytic platform which can be delivered as a service to IoT applications. Such analytic service should not impose a considerable overhead on the overall IoT ecosystem.One viable solution for IoT big data is to keep track of just the interesting data only. Existing approaches can help in this field like principle component analysis (PCA), pattern reduction, dimensionality reduction, feature selection, and distributed computing methods.Cloud computing. CC offers a new management mechanism for big data that enables the processing of data and the extraction of valuable knowledge from it.Employing CC for the IoT is not an easy task due to the following challenges:
Immersive multimedia and multi-sensory interactionsThe future of multimedia and human-centric communication enabled by IMT-2030 is expected to give an immersive experience through multi-sensory interactions and in-depth integration between physical and digital worlds. This is expected to provide a real time interactive video experience. XR is expected to be personalized and developed into an immersive experience for users. In addition to these trends, holographic telepresence might become common for work, social interactions, entertainment, tele-education, remote live performances, etc. It is expected that new human-machine interfaces would enable immersive and intelligent interactions, where control is maintained remotely, e.g., remote operations of machines, robots, devices leveraging edge cloud computing resources and AI to deliver tactile internet and ambient awareness.Digital twin and virtual worldIMT-2030 is expected to be used to replicate the physical world into a digital virtual world as precise real-time representations or digital twins. Digital twins have the potential to provide ubiquitous tools and knowledge platforms for the modelling, monitoring, managing, analysing and simulating of physical assets, resources, environments and situations.Using advanced technologies such as integration of communication with AI, sensing, and computing, digital twins could also synchronize the digital world to the physical world and provide connections between the digital replica components. Digital twins are expected not only replicate but also affect the physical world by providing digital maps for virtual experiences to humans and computed control to machines. Digital twins are envisaged to become a powerful tool in the evolution of multiple industries including health care, agriculture, construction, etc.
CONVERGENCE BETWEEN CONNECTIVITY AND INTELLIGENCEIt is important to understand that the technology refresh within the telecommunication industry starts with the international standardisation stage, driven by the ITU defining a series of desired solutions for the upcoming decades. The ITU has approved and published the global 6G vision framework and has begun the technical standardisation stages that follow this(7). The delivery of 6G technology will then be specified by ETSI and 3GPP, working together and taking their lead from the ITU definition for technology readiness by 2030. Also UK’s international technology strategy(8) considers six strategic priorities which identifies AI among the priority technologies alongside Data.The ITU 6G framework goes beyond connectivity and aspires to make this new digital experience accessible to every individual, at every corner of our future, including how we are educated, how we work, how we live our day to day lives, and how we interact with other people and machines.As of now, 6G remains in its early stages. The technological landscape is filled with many potential opportunities. One of the trends we observe is a surge in transformative use cases that emerge as a result of the convergence among connectivity, sensing, and intelligence. For example, AI-enabled Telco services are enabling smart cities and connecting IoT devices. Technology trends, such as digital twins and smart infrastructures, are revolutionising industries such as healthcare with remote diagnosis, optimising logistics with autonomous delivery vehicles, and enhancing entertainment experiences with seamless augmented and virtual reality. The convergence of connectivity and intelligence is driving innovation in the wider society across sectors, including Telco.
Ubiquitous computingIn addition to ubiquitous intelligence, it is expected that ubiquitous use of data computing resources would also expand throughout the IMT-2030. Emerging trends in this regard include expansion of data processing in the network infrastructure to the network cloud and devices that are closer to the origin of the data and support for proliferation of ubiquitous intelligence throughout the IMT-2030. This trend also contributes to the improvements for applications requiring real-time responses and data transport. Ubiquitous computing disseminated across IMT 2030 is expected to enable efficient utilization of resources and optimal placement of workloads, as well as scales and manages the infrastructure to run the applications.
Ubiquitous intelligenceWith the steady progress and fast spread of technologies in AI and particularly machine learning (ML), it is expected that intelligence would be present in every part of the communication system to support the building of smart cities and communities. Future connected devices may become fully context-aware for more intuitive and efficient interactions among humans, machines, and the environment. Possible autonomous management of networks by AI/ML could also be capable of performing self-monitoring, self-organization, self-optimization and self-healing without human intervention. It is expected that the air interface would be enhanced by AI models. IMT-2030 could serve as an AI-enabling infrastructure capable of providing services for intelligent applications. AI-enabled air interface along with distributed computing and intelligence could allow for end-to-end AI applications and the convergence of communication and computing. These systems would have functions of inferences, model training, model deployment, as well as computing distributed across networks and devices.
BIG DATA ANALYTICS AND CLOUD COMPUTINGConnecting a large number of physical objects like humans, animals, plants, smart phones, PCs, etc. equipped with sensors to the Internet generates what is called big data. Big data needs smart and efficient storage. Obviously, connected devicesneed mechanisms to store, process, and retrieve data. But big data is so huge such that it exceeds the capability of commonly used hardware environments and software tools to capture, manage, and process them within an acceptable slot of time. The emerging and developing technology of cloud computing is defined by NIST as an access model to an on-demand network of shared configurable computing sources such as networks, servers, warehouses, applications, and services. Cloud services allow individuals and companies to use remote third-party software and hardware components. Cloud computing enables researchers and businesses to use and maintain many resources remotely, reliably and at a low cost. The IoT employs a large number of embedded devices, like sensors and actuators that generate big data which in turn requires complex computations to extract knowledge. Therefore, the storage and computing resources of the cloud present the best choice for the IoT to store and process big data. In the following subsections, we discuss the relation between the IoT and big data analytics, cloud and fog computing.Big Data. Whatmakes big data an important asset to businesses is that it makes it possible to extract analytics and consequently knowledge, by which a business can achieve competitive advantage. There are some platforms for big data analytics like Apache Hadoop and SciDB. However, these tools are hardly strongenough for big data needs of IoT. The amount of IoT data generally is too huge to be fed and processed by the available tools. In support of the IoT, these platforms should work in realtime to serve the users efficiently. For example, Facebook has used an improved version of Hadoop to analyze billions of messages per day and offer real-time statistics of user actions. In terms of resources, besides the powerful servers in data centers a lot of smart devices around us offer computing capabilities that can be used to perform parallel IoT data analytic tasks.Instead of providing application specific analytics, IoT needs a common big data analytic platform which can be delivered as a service to IoT applications. Such analytic service should not impose a considerable overhead on the overall IoT ecosystem.One viable solution for IoT big data is to keep track of just the interesting data only. Existing approaches can help in this field like principle component analysis (PCA), pattern reduction, dimensionality reduction, feature selection, and distributed computing methods.Cloud computing. CC offers a new management mechanism for big data that enables the processing of data and the extraction of valuable knowledge from it.Employing CC for the IoT is not an easy task due to the following challenges:
- Synchronization:Synchronizationbetweendifferentcloud vendors presents a challenge to provide real-time services since services are built on top of various cloud platforms.
- Standardization: Standardizing CC also presents a significant challenge for IoT cloud-based services due having to interoperate with the various vendors.
- Balancing:Making a balance between general cloud service environments and IoT requirements presents another challenge due to the differences in infrastructure.
- Reliability: Security of IoT cloud-based services presents another challenge due to the differences in the security mechanisms between the IoT devices and the cloud platforms.
- Management: Managing CC and IoT systems is also a challenging factor due to the fact that both have different resources and components.
- Enhancement: Validating IoT cloud-based services is necessary to ensure providing good services that meet the customers’ expectations.
Immersive multimedia and multi-sensory interactionsThe future of multimedia and human-centric communication enabled by IMT-2030 is expected to give an immersive experience through multi-sensory interactions and in-depth integration between physical and digital worlds. This is expected to provide a real time interactive video experience. XR is expected to be personalized and developed into an immersive experience for users. In addition to these trends, holographic telepresence might become common for work, social interactions, entertainment, tele-education, remote live performances, etc. It is expected that new human-machine interfaces would enable immersive and intelligent interactions, where control is maintained remotely, e.g., remote operations of machines, robots, devices leveraging edge cloud computing resources and AI to deliver tactile internet and ambient awareness.Digital twin and virtual worldIMT-2030 is expected to be used to replicate the physical world into a digital virtual world as precise real-time representations or digital twins. Digital twins have the potential to provide ubiquitous tools and knowledge platforms for the modelling, monitoring, managing, analysing and simulating of physical assets, resources, environments and situations.Using advanced technologies such as integration of communication with AI, sensing, and computing, digital twins could also synchronize the digital world to the physical world and provide connections between the digital replica components. Digital twins are expected not only replicate but also affect the physical world by providing digital maps for virtual experiences to humans and computed control to machines. Digital twins are envisaged to become a powerful tool in the evolution of multiple industries including health care, agriculture, construction, etc.
Beyond transmitting bits:
Context, semantics, and task-oriented communicationsCommunication systems to date primarily aim at reliably communicating bit sequences. Such an approach provides efficient engineering designs that are agnostic to the meanings of the messages or to the goal that the message exchange aims to achieve. Next generation systems, however, can be potentially enriched by folding message semantics and goals of communication into their design. Further, these systems can be made cognizant of the context in which communication exchange takes place, providing avenues for novel design insights.There is a growing interest in semantic and goal-oriented communication systems in the recent years. This interest is mainly driven by new verticals that are foreseen to dominate the data traffic in future communication networks. Current communication networks are designed to serve data packets in a reliable and efficient manner without paying attention to the contents of these packets or the impact they would have at the receiver side. However, there is a growing understanding that many of the emerging applications can benefit from going beyond the current paradigm that completely separates the design of the communication networks from the source and destination of the information that flows through the network.
Goal-oriented, semantic, and goal-oriented semantic communicationFuture wireless generations are envisioned to enable more intelligent services, particularly concerning machines/artificial intelligence (AI) agents communications, while coping with the available network resources, including energy, spectrum, and computing resources. We strongly believe such a vision cannot be realized solely by relying on higher frequency bands or conventional energy-efficient mechanisms. Within this context, upcoming wireless generations are anticipated to allow machines to communicate meaningfully while satisfying particular rate and energy constraints, by extracting the useful semantics to be communicated with the receiver instead of transmitting the whole message (which lacks in most scenarios the semantic aspect that allows the receiver to understand the purpose/meaning of the message). It is further envisaged that machines will exploit extracted semantics in order to perform particular predefined goals pertinent to parameters’ estimation, optimization, classification, etc., in order to allow selfoptimizing and self-configuring networks. Motivated by this, in this subsection, we shed lights on the essential role of goaloriented, semantic, and goal-oriented semantic communication paradigms in enabling immersive multimedia communications.
Context, semantics, and task-oriented communicationsCommunication systems to date primarily aim at reliably communicating bit sequences. Such an approach provides efficient engineering designs that are agnostic to the meanings of the messages or to the goal that the message exchange aims to achieve. Next generation systems, however, can be potentially enriched by folding message semantics and goals of communication into their design. Further, these systems can be made cognizant of the context in which communication exchange takes place, providing avenues for novel design insights.There is a growing interest in semantic and goal-oriented communication systems in the recent years. This interest is mainly driven by new verticals that are foreseen to dominate the data traffic in future communication networks. Current communication networks are designed to serve data packets in a reliable and efficient manner without paying attention to the contents of these packets or the impact they would have at the receiver side. However, there is a growing understanding that many of the emerging applications can benefit from going beyond the current paradigm that completely separates the design of the communication networks from the source and destination of the information that flows through the network.
Goal-oriented, semantic, and goal-oriented semantic communicationFuture wireless generations are envisioned to enable more intelligent services, particularly concerning machines/artificial intelligence (AI) agents communications, while coping with the available network resources, including energy, spectrum, and computing resources. We strongly believe such a vision cannot be realized solely by relying on higher frequency bands or conventional energy-efficient mechanisms. Within this context, upcoming wireless generations are anticipated to allow machines to communicate meaningfully while satisfying particular rate and energy constraints, by extracting the useful semantics to be communicated with the receiver instead of transmitting the whole message (which lacks in most scenarios the semantic aspect that allows the receiver to understand the purpose/meaning of the message). It is further envisaged that machines will exploit extracted semantics in order to perform particular predefined goals pertinent to parameters’ estimation, optimization, classification, etc., in order to allow selfoptimizing and self-configuring networks. Motivated by this, in this subsection, we shed lights on the essential role of goaloriented, semantic, and goal-oriented semantic communication paradigms in enabling immersive multimedia communications.
Explainable XAIFairness and debiasing: Manage and monitor fairness. Scan your deployment for potential biases.
Responsible AINowadays, Articial Intelligence (AI) is democratized in our everyday life. Responsible AI is an AI that takes into acount societal values, moral and ethical consideration. The main pillars are:Accountability referes to the need to explain and justify one's decision and actions to its partners, users and others with whom the system interacts.Responsibility referes to the role of people themselves and to the capability of AI systems answer for one's decision and identify errors or unexpected results.
- Model drift mitigation: Analyze your model and make recommendations based on the most logical outcome. Alert when models deviate from the intended outcomes.
- Model risk management: Quantify and mitigate model risk. Get alerted when a model performs inadequately. Understand what happened when deviations persist.
- Lifecycle automation: Build, run and manage models as part of integrated data and AI services. Unify the tools and processes on a platform to monitor models and share outcomes. Explain the dependencies of machine learning models.
- Multicloud-ready: Deploy AI projects across hybrid clouds including public clouds, private clouds and on premises. Promote trust and confidence with explainable AI.
Responsible AINowadays, Articial Intelligence (AI) is democratized in our everyday life. Responsible AI is an AI that takes into acount societal values, moral and ethical consideration. The main pillars are:Accountability referes to the need to explain and justify one's decision and actions to its partners, users and others with whom the system interacts.Responsibility referes to the role of people themselves and to the capability of AI systems answer for one's decision and identify errors or unexpected results.
- Transparency referes to the need to describe, inspect and reproduce the mechanisms through governance of the data used created.
- Fairness refers to the equitable treatment of individuals, or groups of individuals, by an AI system. Bias occurs when an AI system has been designed, intentionally or not, in a way that may make the system's output unfair. Bias can be present both in the algorithm of the AI system and in the data used to train and test it. It can emerge as a result of cultural, social, or institutional expectations; because of technical limitations of its design; or when the system is used in unanticipated contexts or to make decisions about communities that are not considered in the initial design.
AI for IoTThe proliferation of IoT devices and sensors, coupled with advancements in AI algorithms and computing technologies, has paved the way for a new era of intelligent IoT systems. AI techniques are increasingly integrated into IoT architectures to enable advanced analytics, autonomous decision-making, and adaptive behaviors. From smart homes and cities to industrial automation and healthcare, AI-powered IoT solutions are revolutionizing the way we interact with and leverage data from connected devices, driving innovation, efficiency, and sustainability. This mini-series aims to explore the intersection of AI and IoT, covering cutting-edge research, real-world applications, and best practices in leveraging AI to enhance IoT systems and services.
- AI-enabled IoT applications and use cases: Explore innovative applications and use cases where AI enhances IoT functionalities and capabilities, spanning smart healthcare, intelligent transportation, precision agriculture, industrial automation, environmental monitoring, and more.
- AI-driven data analytics and decision-making: Investigate AI techniques for processing, analyzing, and deriving actionable insights from IoT-generated data streams, enabling predictive maintenance, anomaly detection, personalized recommendations, and intelligent automation.
- Generative AI and Large Language Models (LLMs) for IoT: Study the applications of generative AI in IoT environments; explore how LLMs, such as generative pre-trained transformer (GPT) models, can be utilized to generate synthetic data, enhance natural language understanding, and support human-machine interaction in IoT systems.
- Edge AI and distributed intelligence: Discuss the integration of AI algorithms and models at the edge of IoT networks, enabling real-time inference, adaptive learning, and autonomous decision-making closer to data sources, and minimizing latency and bandwidth requirements.
- AI-empowered robotics and sensing for IoT: Explore the integration of AI with robotics and sensing technologies in IoT systems. Introduce advancements in sensor technologies and data fusion techniques that enable intelligent data collection, processing, and analysis in dynamic environments.
- Privacy, security, and trustworthiness: Address the privacy and security implications of AI-enabled IoT systems, including data privacy, confidentiality, integrity, and authenticity. Investigate trustworthiness in AI-enabled IoT systems, emphasizing the need for reliability, transparency, explainability, accountability, and fairness.
- Standardization and interoperability: Discuss the challenges and opportunities in standardizing AI-enabled IoT technologies to ensure interoperability, compatibility, and seamless integration across heterogeneous IoT ecosystems. Explore emerging standards, protocols, and frameworks for facilitating collaboration and interoperability among AI and IoT technologies.
- Demonstrations, Proof-of-Concepts, and deployments: Present innovative demonstrations and proof-of-concepts showcasing the integration of AI technologies with IoT systems. Share insights and best practices for deploying AI-powered IoT solutions in diverse environments, including smart cities, healthcare, agriculture, manufacturing, transportation, and energy.
- Regulation and policy: Explore the regulatory and policy landscape surrounding AI and IoT technologies, including data governance, consumer protection, liability, accountability, and ethical considerations. Discuss the role of regulatory bodies, industry consortia, and international organizations in shaping ethical, legal, and policy frameworks to ensure the responsible development, deployment, and use of AI-powered IoT systems.
Merging of artificial intelligence (AI), artificial life (AL) and virtual realityTechnology has been rightly called the wave of the future. Artificial intelligence (AI), artificial life (AL) and virtual reality (VR) are not exactly new terms, and it has been a while since we have been experiencing how these technologies are leading us to convergence. VR lets us produce simulated environments that we can then submerge ourselves into, AI is working towards outfitting technical devices and services with the help of insight and perception of a responsive being and thought, while AL is the art of examining natural life and humans through simulations with computer models, robotics and biochemistry. Major advances can be made to merge these three technologies to bring about a revolution in the world we live in. Bringing AI, AL and VR together can provide us with incredible opportunities in various domains like travel and tourism, lifestyle, healthcare, BFSI, retail, entertainment and many more. This chapter revolves around the various cases of using AI to augment the existing applications and the many areas where AI, AL and VR can be applied.
Quite easily, intelligence demonstrated by machines can be termed as AI. However, VR is a simulated experience which may or may not be like the real world. Basically, it spots the user inside a 3D environment without it all really happening. All in all, AI and VR are two technologies that are set to merge, considering the pace with which such advances are taking over our lives. Therefore, there are is no second opinion needed for the fact that combining two or more technologies is only going to make everything better and more efficient. Considering this statement, AI and VR are one of the best combinations anyone can imagine.AI in VR offers a perspective that is seemingly endless, and that is the reason why about eight out of 10 biggest tech firms of the world have invested in this field. Some applications of this integration include: Travel and Tourism: wherein hotels, airlines, tourist places, etc. give their customers a prospective of the experience they are about to live, Engaging Entertainment: it immerses a gamer, for instance, into a simulated environment, which allows the user to feel as if they’re in the game, and one can only imagine the thrill this would bring, Immersive Shopping; this would enable a customer to test out whatever they wish to purchase, before placing an order. This not only helps a consumer but also the businesses, as it helps one understand more about their customer and, thereby, boost their sales. The COVID pandemic has made people understand this concept even better because one may shop according to their convenience, without having to visit a certain place. To conclude, it is not wrong to say that AI in VR presents amazing opportunities and their convergence is on its way to change our lives indefinitely. This is what we call The Future.Together, AI and VR bring out the reality of virtual AI. With time, VR-based systems are increasing, and by layering these systems with AI, unreal experiences have been developed.
Quite easily, intelligence demonstrated by machines can be termed as AI. However, VR is a simulated experience which may or may not be like the real world. Basically, it spots the user inside a 3D environment without it all really happening. All in all, AI and VR are two technologies that are set to merge, considering the pace with which such advances are taking over our lives. Therefore, there are is no second opinion needed for the fact that combining two or more technologies is only going to make everything better and more efficient. Considering this statement, AI and VR are one of the best combinations anyone can imagine.AI in VR offers a perspective that is seemingly endless, and that is the reason why about eight out of 10 biggest tech firms of the world have invested in this field. Some applications of this integration include: Travel and Tourism: wherein hotels, airlines, tourist places, etc. give their customers a prospective of the experience they are about to live, Engaging Entertainment: it immerses a gamer, for instance, into a simulated environment, which allows the user to feel as if they’re in the game, and one can only imagine the thrill this would bring, Immersive Shopping; this would enable a customer to test out whatever they wish to purchase, before placing an order. This not only helps a consumer but also the businesses, as it helps one understand more about their customer and, thereby, boost their sales. The COVID pandemic has made people understand this concept even better because one may shop according to their convenience, without having to visit a certain place. To conclude, it is not wrong to say that AI in VR presents amazing opportunities and their convergence is on its way to change our lives indefinitely. This is what we call The Future.Together, AI and VR bring out the reality of virtual AI. With time, VR-based systems are increasing, and by layering these systems with AI, unreal experiences have been developed.
Convergence of AI and ALArtificial Life (AL) is a narrative technical search that aims at analyzing man-made structures displaying behaviors that are instinctive to the living organisms and their environment. AL complements the traditional biological sciences that are focused on analyzing and studying the living organisms by trying to create life-like behaviors within computers or other artificial media. Exploration in the area of basic artificial intelligence (AI) focuses at replicating the most intricate faculties of the human brain like problem solving, natural language understanding, and logical reasoning.
Merging the three technologies
AL, AI and VR are two letter acronyms that are changing the World. The mix of all these technologies is set to transform our lives to an easier way of life and make the studies in the field of science more effective. They hold the potential to reinvent the way we presently process things. These three technological tools have collaborated to achieve major milestones in innumerable aspects of life. Be it education, travel, science, culture etc. – every field has tasted their flavor, at least, once. There are not many things to name which cannot be done with the help of this merging. When humans touch, experience, live and interact with world, they tend to learn better and comprehend more. Seeing the possibilities that AI, AL and VR can offer, there is no looking back on using them. Also, with the Covid-19 pandemic taking over our lives, the demand and need for them have only risen. The situations we could not have imagined to have handled without stepping out of home has now been dealt with, sitting inside. Though, it is quite true that AI/VR/AL systems, headsets, cameras, glasses, etc. are expensive, in the not-too-distant future, these may become as accessible to us as any basic computer or phone that people are familiar with. With the introduction of 5G network, one may expect it to be a complete game changer that commits to raising technologies and bring together the physical and virtual world, beyond our expectations. The opportunities that the merging of AI, AL and VR offer are endless and the advancements that are coming forward are breaking borders left, right and center.
Application of Artificial Intelligence in Virtual Reality
Virtual reality is a technology that uses a combination of imaging and computer processing to create an immersive world that represents the real world.
The role of these virtual worlds is to create real and dynamic simulations to mimic the real world with a high degree of accuracy and allow users to interact with elements as if they were in the real world.
The use of 3D modeling techniques is a major source that contributes to the creation of these immersive environments and creates a very high sense of reality that allows the user to be more engaged and interact in a positive way.
VR refers to the computer-generated reconstruction of a three-dimensional image or picture that allows interaction that is like that which occurs in the real world. This becomes achievable by the use of particular electronics, such as sensor-equipped gloves or headgear with a built-in display. The notion of producing a virtual scene or a 3D scene that corresponds to a real environment while allowing real-time interaction is what we find in virtual reality these days. Thanks to the integration of hardware components and specialist elements in the world of VR, the creation of these virtual environments has become increasingly achievable. Gloves containing sensors, helmets with integrated screens, and motion-detection gadgets are examples of this advanced technology, enabling users to create a link between the real and virtual worlds.
VR applications use cutting-edge technology to present users with realistic, interactive experiences that have the potential to transform the way we learn and explore the real world in a virtual way.
AI is the extraordinary attempt to simulate cognitive capabilities like human intellect inside the world of computers, specifically computer systems. This diverse topic encompasses a wide range of applications, each of which uses AI technology to solve tasks that generally need human-like comprehension and reasoning abilities. The adaptability of AI has resulted in the creation of a wide range of applica-tions, each with its own set of capabilities and contributions to the advancement of human–computer interaction and problem-solving.
Expert systems play an important role in the field of AI. They are designed to ingest and analyze knowledge repositories, enabling them to solve problems and provide expert-level information. Expert systems find applications in fields such as health care, diagnostic support and finance, investment decision support. By exploiting AI’s ability to evaluate datasets and draw conclusions based on specialized knowledge, these systems significantly improve decision-making processes.
Together, AI and VR create a more enveloping and invigorating experience. The combination of AI and VR creates a synergy that transforms the virtual world into a realistic arena. Here are some important links between VR and AI:
Realistic environments: By building credible virtual environment models that mimic nature, physics, and human behavior, AI can simulate reality.
Immersive interactions: With VR, machine learning methods from computer vision and natural language processing are used to make virtual characters more engaging.
Personalization: If AI makes information available for consumption, adjusts the learning level or special conditions desired by the user, personalization is likely to occur.
In this era of science and technology, the development of AI is rapidly advancing. Though machines’ supremacy over humans is undeniable in chess, a historical investigation of human advancement emphasizes multiple areas where AI lags the human mind. Machines cannot replace, surpass, or replicate specific facets of human cognition and comprehension.
Today, AI is a significant technological advance that promotes an overall improve-ment in skills. It enables computers to perform cognitive tasks such as perception, logic, learning, and interaction. The convergence of three advanced and intercon-nected technological developments—improved algorithmic intelligence, huge data warehouses, and an abundance of affordable processing and storage resources—has led to the rapid adoption of AI in our daily lives.
In conclusion, the fusion of AI and VR offers immense potential for the medical field. In addition to improving medical education and diagnostics, it also offers cutting-edge solutions for telemedicine, rehabilitation, exposure therapy, and pain management. As it develops, this integration has the potential to change healthcare procedures, improve treatment optimization and, ultimately, improve people’s well-being and outcomes on a global scale.
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