Virtual Intelligence: Fundamentals and Applications

By Fouad Sabry

What Is Virtual Intelligence

Virtual intelligence, often known as VI, is a phrase that refers to artificial intelligence that is contained within a virtual environment. There are numerous virtual worlds that offer alternatives for persistent avatars that allow users to obtain knowledge, training, perform roles, and communicate with other people.

How You Will Benefit

(I) Insights, and validations about the following topics:

Chapter 1: Virtual Intelligence

Chapter 2: Distributed Artificial Intelligence

Chapter 3: Intelligent Agent

Chapter 4: Multi-agent System

Chapter 5: Immersion (Virtual Reality)

Chapter 6: Virtual Reality Applications

Chapter 7: Digital Immortality

Chapter 8: Smart Object

Chapter 9: Life Simulation Game

Chapter 10: Virtual World Framework

(II) Answering the public top questions about virtual intelligence.

(III) Real world examples for the usage of virtual intelligence in many fields.

(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of virtual intelligence' technologies.

Who This Book Is For

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of virtual intelligence.

• Language: English
• Publisher: One Billion Knowledgeable
• Release date: Jul 4, 2023

Book preview

Chapter 7: Virtual intelligence

When talking about AI that takes place in a digital setting, we refer to it as virtual intelligence (VI). Persistent avatars are available in many MMORPGs, allowing users to access content, participate in activities, and interact with others.

Beyond the conventional paradigm of previous user interfaces, virtual immersion provides a novel platform for VI (UIs). What Alan Turing did to establish the bar for distinguishing between human and artificial intelligence was done in the dark. Virtual intelligence (VI) bots of the present day have advanced to a new level, surpassing the limitations of earlier tests. These environments' immersive qualities, which include nonverbal elements, influence the realism provided by artificial intelligence simulations.

When these two systems are combined, we get artificial intelligence:

Collaborating, simulating, and role-playing in interactive 3D spaces called virtual environments are all possible in this setting. Second Life, VastPark, Olive, OpenSim, Outerra, Oracle's Open Wonderland, Open Cobalt at Duke University, and many others are all being used for government and academic projects right now. The high-definition virtual world Blue Mars is just one example of a commercial virtual world pushing this technology in exciting new directions.

Artificial intelligence

AI, or artificial intelligence, is a type of computer program that attempts to simulate human intelligence and behavior. Both the VI's credibility and its practicality can be affected by the visual and nonverbal cues provided by the virtual environment. For the simple reason that the question of does it work? is often secondary to the question of how we feel about it working? in the realm of technology. The unique context in which Virtual Intelligence functions is a key differentiator between it and other forms of artificial intelligence.

Due to the high cost and risk involved in actual bomb disposal, Northrop Grumman created a virtual training opportunity using their Cutlass bomb disposal robot. The virtual robot eliminates the risks of physical damage, trainee safety hazards, and accessibility restrictions by simulating a complex system without requiring the knowledge of advanced code.

MyCyberTwin: NASA is just one of many businesses that have benefited from MyCyberTwin's artificial intelligence systems. In Second Life, it was put to use for the Phoenix rover. In order to inform the public about the Phoenix rover and its mission, their MyCyberTwin followed a predetermined script.

The University of Florida's Second China project was designed to provide students with a comprehensive introduction to Chinese culture and language. During diplomatic missions to other countries, times of potential conflict, or times of regional destabilization, students are immersed in an environment that presents role-playing challenges alongside language and cultural sensitivities. Information, guided learning scenarios, communication, collaboration, and role-playing are all made available to participants in the virtual training. China served as the inspiration for this model, but it can easily be adapted for use with any culture to help people gain insight into the motivations and perspectives of others in social situations.

Extreme Reality, in collaboration with the Duke School of Nursing, created a virtual training simulation for the testing of critical thinking, in which a nurse follows a series of predetermined steps in order to collect and analyze data necessary to make decisions and carry them out. A nurse's actions can have the same effect on a bot patient as they would on a real one, with the patient's condition improving if the nurse takes the right steps.

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Chapter 2: Distributed artificial intelligence

Research in the area of artificial intelligence known as Distributed Artificial Intelligence (DAI), which is often referred to as Decentralized Artificial Intelligence, is a branch that focuses on the creation of distributed solutions for many types of challenges. The area of multi-agent systems is closely connected to and developed out of DAI, which was its forerunner.

The two most prominent DAI techniques are known as multi-agent systems and distributed problem solving. There are many different programs and tools available.

The term distributed artificial intelligence (DAI) refers to a strategy for resolving complicated issues including learning, planning, and decision-making. It is so parallel that it is humiliating, and as a result, it can take advantage of large-scale processing and the geographical distribution of computer resources. Because of these qualities, it is able to tackle issues that need the processing of very extensive data sets. DAI systems are made up of autonomous learning processing nodes, also known as agents, which are spread out over a network, often on a very large scale. Nodes in a DAI system are able to function on their own, and communication between the nodes allows for the integration of incomplete answers in a manner that is often asynchronous. DAI systems are characterized as resilient and elastic, and they are required to be loosely connected because of their size. In addition, given to the complexity of the challenge and the difficulties involved in redeployment, DAI systems are designed to be able to adapt to changes in the problem description or the underlying data sets.

In contrast to monolithic or centralized Artificial Intelligence systems, which feature processing nodes that are closely connected and located in close proximity to one another, Distributed Artificial Intelligence (DAI) systems do not need all of the important data to be gathered in a single area. As a result, deep learning artificial intelligence systems often run on subsamples or hashed representations of extremely large datasets. In addition, the dataset that serves as the source may undergo modifications