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Ai in ‘Metaverse’



  1. Extended Reality: VR headsets enable the viewing of 360-degree videos providing unlimited view-point in all directions that are suitable for VR performance. Some AI algorithms have been utilized in VR devices to improve the human-machine interaction experience based on visual-based information. For the prediction of user’s eye fixations in some gaze-based applications, such as content design and rendering, a DL framework with multiple CNNs was built to deal with various kinds of input data, e.g., VR image, gaze data, and head data. Neural networks are also adopted for human identification and authentication by analyzing periodic behaviors between users and VR gears (e.g., controllers and head-mounted display).
  2. Computer Vision: In the metaverse, players can control their avatars (virtual characters) and interact with other players or nonplayer characters (NPCs), with the help of motion-sensing interactive devices like controllers, gloves, and cameras, the posture and action of avatars should be estimated and recognized automatically. Some studies use the depth information received by depth cameras that has been learned together with colour information by sophisticated ML and DL models to increase the accuracy of body part localization and deal with different viewpoints. Picture quality reduction issues, such as noise, blurring, and low resolution, should be addressed in the virtual world to enhance users’ visual perception. Several image restoration projects use CNN architectures to remove image compression artifacts and recover clean pictures from a variety of sources.


The blockchain mechanism has been widely accepted as a very secure way of storing data. This includes not only eliminating the concept of trust from systems via decentralisation, but also includes proper coordination in a decentralised network tracking changes to data. The large amount of data in the metaverse can be stored on a blockchain over a centralised data warehouse. The unique features of blockchain provide a promising solution for security and privacy. Introducing AI only empowers this system more.

Establishing Utility

Various traditional ML techniques (e.g., clustering, SVM, and bagging) as well as unique DL structures (e.g., CNN and LSTM) have been examined for data analytics in blockchain-based networks to detect and categorize cyberattacks. This includes evaluating smart contracts, and cost-efficient model learning in an on-chain environment. The use case of blockchain even extends to IOT frameworks which have also benefited from implementing AI for security. The framework below shows a high performance system on fraud detection and threat prediction, and can be extended for dealing with security and privacy problems in data storage and sharing instead of data collection.

With the ever increasing popularity of deep learning, traditional machine learning algorithms aren’t all we see in the blockchain space. A CNN based blockchain framework named Deepchain was recently developed to ensure the privacy and integrity of data contributed by the network participants.

Incorporating AI

One of the ways in which we can practically incorporate AI in blockchain is using Federated Learning. FL is a learning model in which multiple users learn a model based on their local data store and then collaborate to learn a global model. This collaborative learning is possible due to a parameter aggregation mechanism. While FL can offload the trained intrusion detection model to distributed edge devices, reducing the central server’s computational requirements, blockchain can ensure the aggregation model’s security in both the model storage and sharing processes. Interoperability, in addition to data security and privacy, is a key topic in blockchain since it allows multiple parties to interact utilising diverse data infrastructures. Using FL further increases the reliance on decentralised systems which is the primary way in which centralised trust is answered.


A. Healthcare:

B. Manufacturing

C. Smart Cities

D. Gaming

Article: AI in Metaverse

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