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Artificial Intelligence for the Metaverse: A Survey

Thien Huynh-The, Quoc-Viet Pham, Xuan-Qui Pham, Thanh Thi Nguyen, Zhu Han, Dong-Seong Kim

arXiv:2202.10336v1cs.CYcs.AIcs.LG

TL;DR

Metaverse environments require integrated AI capabilities across technical systems and applications, yet their role has not been comprehensively synthesized. This survey reviews AI methods and applications across six technical aspects and concludes that AI can strengthen infrastructure, immersive experience, and virtual-world services.

  • Problem

    The metaverse spans diverse technical and application domains, motivating a comprehensive examination of how AI contributes to its development and potential.

  • Method

    The paper surveys AI-based work across natural language processing, machine vision, blockchain, networking, digital twins, neural interfaces, and metaverse applications.

  • Results

    The reviewed AI solutions show potential to strengthen metaverse infrastructure, improve 3D immersive experience, and develop built-in virtual-world services.

  • Takeaways & Limitations

    AI is positioned as a foundational technology for enhancing immersive experiences and services across metaverse technical and application domains.

  • Takeaways & Limitations

    Many metaverse AI services rely on black-box models whose decision-making processes users and developers cannot completely understand, limiting interpretability and explainability.

Abstract

from arXiv · show

Along with the massive growth of the Internet from the 1990s until now, various innovative technologies have been created to bring users breathtaking experiences with more virtual interactions in cyberspace. Many virtual environments with thousands of services and applications, from social networks to virtual gaming worlds, have been developed with immersive experience and digital transformation, but most are incoherent instead of being integrated into a platform. In this context, metaverse, a term formed by combining meta and universe, has been introduced as a shared virtual world that is fueled by many emerging technologies, such as fifth-generation networks and beyond, virtual reality, and artificial intelligence (AI). Among such technologies, AI has shown the great importance of processing big data to enhance immersive experience and enable human-like intelligence of virtual agents. In this survey, we make a beneficial effort to explore the role of AI in the foundation and development of the metaverse. We first deliver a preliminary of AI, including machine learning algorithms and deep learning architectures, and its role in the metaverse. We then convey a comprehensive investigation of AI-based methods concerning six technical aspects that have potentials for the metaverse: natural language processing, machine vision, blockchain, networking, digital twin, and neural interface, and being potential for the metaverse. Subsequently, several AI-aided applications, such as healthcare, manufacturing, smart cities, and gaming, are studied to be deployed in the virtual worlds. Finally, we conclude the key contribution of this survey and open some future research directions in AI for the metaverse.

I. INTRODUCTION

The metaverse is presented as a shared, immersive 3D world or interconnected cross-platform worlds enabled by layered technologies and interactive collaboration. Although the concept predates current corporate attention, AI—particularly machine learning and deep learning—supports perception, interaction, and diverse metaverse functions.

  • Motivation and history: The concept has circulated for decades, while recent attention has been amplified by Meta’s rebranding and major technology-company investment and development activities.Examples include Microsoft’s acquisition of Activision Blizzard and virtual real-estate purchases in metaverse platforms.
  • Metaverse concept: The metaverse denotes a shared virtual 3D world, or multiple cross-platform worlds, supporting immersive, interactive, and collaborative activities.The term combines “Meta” and “Universe” and was associated with a virtual-reality world in Snow Crash.
  • AI foundations: AI appears throughout the metaverse layers through machine learning algorithms and deep learning architectures used across diversified aspects.The introduction specifically notes supervised and unsupervised learning for classification and regression.
  • AI foundations: Deep learning analyzes complicated patterns in large, messy, confusing data and enables systems to recognize human activity and interactions in virtual worlds.Sensor signals from mobile, smartwatch, and wearable devices support applications such as physical activity recognition.

A. Our Contributions

This survey examines how AI contributes to the metaverse by reviewing existing AI-based work across technical and application perspectives. It covers AI foundations, six technical aspects, diverse applications, existing projects, and future research directions.

  • Survey scope: The paper surveys existing AI-based work and discusses its potential contributions to the metaverse from technical and application perspectives.AI’s role is important to the metaverse’s foundation and development, but its effects and contributions remain insufficiently understood.
  • AI foundations: It reviews conventional machine-learning algorithms, deep-learning architectures, and supervised, unsupervised, and reinforcement-learning strategies.This review initially reveals AI’s role in the metaverse.
  • Technical aspects: It surveys AI-powered approaches in natural language processing, machine vision, blockchain, networking, digital twins, and neural interfaces.These six technical aspects are identified as having strong potential for the metaverse platform.
  • Applications: It investigates AI-aided methods for healthcare, gaming, manufacturing, smart cities, E-commerce, real estate, and decentralized finance.These application areas are receiving increased interest for deployment in virtual worlds.
  • Projects and future directions: It introduces metaverse projects that apply AI to enhance immersive experiences and develop user-oriented services, while discussing future research directions.The projects illustrate practical AI use in metaverse environments.

B. Paper Organization … 1) Conventional Techniques:

The paper proceeds from blockchain, metaverse, and AI preliminaries to AI adoptions across six technical aspects and promising metaverse applications. Its AI preliminaries categorize machine-learning methods by technique and learning mechanism, covering conventional approaches for clustering, classification, and regression.

  • B. Paper Organization: The paper organization introduces blockchain, the metaverse, and blockchain’s metaverse role before examining AI across six technical aspects and applications.The technical aspects are natural language processing, machine vision, blockchain, networking, digital twin, and neural interface.
  • II. AI FOR THE METAVERSE: PRELIMINARIES: The preliminaries survey AI from traditional machine-learning algorithms to advanced deep-learning networks with different learning mechanisms.This section also explains AI’s role in the metaverse.
  • A. Categorization of AI: Existing AI/ML algorithms are categorized into conventional and advanced techniques for clustering, classification, and regression.These are identified as the three principal problems studied in the categorization.
  • 1) Conventional Techniques:: Conventional AI/ML techniques are grouped by available data into supervised, unsupervised, semi-supervised, and reinforcement learning.The grouping is based on the kinds of data available to the learning model.
  • 1) Conventional Techniques:: Supervised learning maps labeled inputs to outputs, enabling models to classify or predict outcomes for unforeseen samples.It commonly handles classification and regression problems.
  • 1) Conventional Techniques:: Unsupervised learning analyzes unlabeled data to cluster groups and model hidden patterns without human intervention.Examples include hierarchical clustering, k-means clustering, principal component analysis, and association rule.
  • 1) Conventional Techniques:: Semi-supervised learning combines labeled and unlabeled data to mitigate supervised learning’s labeling costs and unsupervised learning’s limited application spectrum.Its basic procedure begins by clustering similar data with an unsupervised-learning algorithm.
  • 1) Conventional Techniques:: Reinforcement learning trains an agent through trial and error to make sequential decisions in uncertain, complex environments while maximizing reward and minimizing penalty.The process progresses from random trials toward sophisticated tactics and nearly optimal solutions.

2) Advanced Techniques: · B. Role of AI in the Metaverse

The section introduces deep-learning architectures and explains how AI, combined with AR/VR, blockchain, and networking, supports reliable, scalable, realistic, and immersive metaverse experiences. It also enables intelligent avatars, multimodal interaction, and AI-assisted virtual content creation.

  • 2) Advanced Techniques:: Deep learning uses multilayered neural networks to achieve state-of-the-art accuracy while automatically learning features from unstructured data.Unlike traditional machine learning, deep learning can process raw data without human intervention or domain knowledge.
  • 2) Advanced Techniques:: RNNs use feedback connections to retain memory of preceding inputs, supporting architectures such as LSTM and GRU networks.These architectures extend foundational recurrent-network designs through structural improvements.
  • 2) Advanced Techniques:: CNNs apply matrix multiplication to identify complex patterns, progressively combining coarse-to-fine features into higher-level representations across images, videos, audio, and other data.Their distinguishing strength is performance across multiple data types, including images, videos, and audio signals.
  • 2) Advanced Techniques:: SOMs are unsupervised neural networks that discover clusters by reducing input dimensionality and selecting nodes according to Euclidean distance.Inputs are normalized before being fed into the network, whose weights characterize the nodes.
  • 2) Advanced Techniques:: Autoencoders compress inputs into a smaller hidden representation and reconstruct the original data through encoding and decoding functions.The hidden layer contains substantially fewer nodes than the input layer.
  • B. Role of AI in the Metaverse: AI combined with AR/VR, blockchain, and networking can support secure, scalable, realistic virtual worlds and improve metaverse infrastructure reliability and performance.The section identifies supervised and reinforcement learning as advanced machine-learning approaches for 5G and future 6G systems.
  • B. Role of AI in the Metaverse: Machine-learning and deep-learning models analyze real-world movements and complex actions, projecting them into virtual worlds so users can control avatars and interact comfortably.Avatars can also express facial expressions, emotions, body movement, physical interactions, speech recognition, and sentiment analysis.
  • B. Role of AI in the Metaverse: AI operates behind immersive XR/VR devices by supporting seamless virtual-reality experiences and assisting creators with virtual content generation.NVIDIA’s GANverse3D can transform object photographs into virtual replicas for developers and creators.

III. AI FOR THE METAVERSE: TECHNICAL ASPECT · A. Natural Language Processing

AI supports the metaverse through six technical aspects, including natural language processing, which enables computational analysis and understanding of human speech and text. NLP methods address language modeling, language understanding, and sentence-level tasks relevant to virtual-world interactions.

  • III. AI FOR THE METAVERSE: TECHNICAL ASPECT: Six AI-based technical aspects—natural language processing, machine vision, blockchain, networking, digital twins, and neural interfaces—are identified as promising for the metaverse.These technologies are presented as ways to enhance user experience and reduce boundaries between virtual and real worlds.
  • A. Natural Language Processing: NLP uses computational models and learning processes to automatically analyze and understand human languages, including speech and text.The field includes speech-to-text, text-to-speech, conversation design, voice branding, and multilingual and multicultural voice topics.
  • A. Natural Language Processing: Language modeling predicts words or linguistic units by capturing syntactic and semantic relations among preceding words, supporting machine translation and text recommendation.The passage identifies language modeling as one of NLP’s most important tasks.
  • A. Natural Language Processing: RNNs and LSTM networks with key-value attention mechanisms outperformed large-scale networks on Wikipedia corpus evaluation while reducing memory use.The cited study built and evaluated multiple neural networks with attention mechanisms.
  • A. Natural Language Processing: Character-aware and word-aware modeling techniques help language-understanding systems extract syntactic and semantic information for part-of-speech tagging, named-entity recognition, and semantic role labeling.These techniques were also effective for identifying prefixes and suffixes, recognizing hyphenated words, and detecting misspellings.
  • A. Natural Language Processing: Deep learning addresses limitations of conventional machine-learning algorithms in NLP, with CNNs applied to sentence-based tasks such as sentiment prediction and question-type classification.Sentiment analysis and recognition can require extracting aspects and sentiment polarities to improve reliability.

B. Machine Vision · 1) Extended Reality:

Machine vision and extended reality provide core metaverse capabilities by interpreting visual environments and supporting immersive interaction with virtual content. XR spans VR, AR, MR, and intermediate experiences, with AI enabling content understanding, gesture manipulation, and VR quality assessment.

  • B. Machine Vision: Machine vision captures and processes visual-environment data to infer high-level information for display through head-mounted devices, smart glasses, and smartphones.Computer vision also analyzes user activities from visual input.
  • B. Machine Vision: Environment-aware 2D/3D content is overlaid onto physical space, while users can move freely in 3D maps and interact with virtual objects.These capabilities connect visual understanding with metaverse interaction.
  • 1) Extended Reality:: XR is an umbrella term encompassing VR, AR, mixed reality, and experiences between these modalities.VR provides fully immersive digital-world viewing, AR places graphics, video streams, and holograms in the physical world, and MR offers a transition between them.
  • 1) Extended Reality:: 360-degree videos provide all-direction viewpoints suited to VR, overcoming the limited field of view of conventional 2D videos.Commercial VR headsets also target performance and comfort requirements.
  • 1) Extended Reality:: Triboelectric sensory gloves and display components recognize multidimensional gestures, enabling real-time manipulation of ML/DL-recognized virtual objects in VR/AR spaces.Other interaction devices include AR headsets, hand-held touchscreen devices, and tabletops.
  • 1) Extended Reality:: Deep learning supports quantitative and qualitative VR video quality assessment, including a high-performance method based on a 3D CNN architecture.Its video predictions were validated using common image-quality-assessment metrics.

2) Computer vision: · C. Blockchain

The paper presents computer vision as a foundation for smooth physical–virtual interaction in the metaverse, covering scene understanding, image quality, and avatar activity recognition. It also describes blockchain, combined with AI and federated learning, as supporting secure, private, transparent, and efficient data sharing.

  • 2) Computer vision:: AI-powered computer vision can improve visual-system accuracy and enable smooth interaction between physical-world users and virtual environments.The survey highlights deep learning architectures and high-performance GPUs as key enablers.
  • 2) Computer vision:: Semantic segmentation assigns pixels to predefined classes, while object detection localizes image objects using bounding boxes and associated information.These are presented as two fundamental computer-vision tasks.
  • 2) Computer vision:: Image restoration and enhancement address noise, blur, low resolution, haze, color, texture, contrast, and super-resolution problems affecting virtual-world visual perception.AI-based restoration methods must also meet real-time processing requirements, commonly measured in frames per second (FPS).
  • 2) Computer vision:: Scene understanding, object detection, and human action or activity recognition are identified as central computer-vision capabilities for the metaverse.The figure summarizes these capabilities as core computer-vision functions in metaverse environments.
  • 2) Computer vision:: Pose estimation identifies and tracks body joints, while action recognition interprets single actions and complex human–machine or human–human activities.These capabilities support avatar control and interaction with users or nonplayer characters through motion-sensing devices.
  • C. Blockchain: Blockchain provides a cryptographically linked ledger with immediate, shared, transparent, immutable, and permissioned information for tracking metaverse transactions and assets.Typical tracked records include orders, payments, accounts, and other transactions.
  • C. Blockchain: Combining blockchain with machine learning and deep learning has produced data-acquisition, storage, sharing, and analytics methods aimed at stronger security and privacy.Investigated methods include clustering, SVM, bagging, CNN, and LSTM approaches for cyberattack detection and classification, alongside blockchain-based IoT security frameworks.
  • C. Blockchain: Federated learning enables users to train on local data and aggregate a global model, while blockchain integration addresses untrusted-party privacy and reduces computing and communication costs.The cited approach uses a proof of training quality as a consensus mechanism in permissioned blockchain networks.

D. Networking · E. Digital Twins

The networking section presents AI as a multi-layer enabler of reliable, high-throughput, low-latency wireless services for the metaverse. The digital-twins section describes AI-enabled virtual replicas that synchronize with physical entities and support remote analysis, monitoring, prediction, and industrial or vehicle applications.

  • D. Networking: AI is applied across multiple wireless-network layers to support metaverse services requiring reliable connections, high throughput, and low latency.The metaverse serves massive numbers of users through pervasive wireless access, while real-time multimedia services impose demanding communication requirements.
  • D. Networking: Reinforcement learning collaboratively learns network states and channel conditions for resource allocation and scheduling in eMBB and uRLLC.RL also supports joint subcarrier-power management and allocation, reducing latency and improving reliability in the Internet of controllable things.
  • D. Networking: Deep learning supports uRLLC tasks including spectrum management, channel prediction, traffic estimation, and mobility prediction.MCNet and SCGNet identify incoming-signal modulation types at the physical layer, enabling accurate demodulation and improved spectrum-utilization efficiency.
  • E. Digital Twins: A digital twin synchronizes operational assets, processes, and systems with the real world while enabling monitoring, visualization, analysis, and prediction.DTs mediate interaction between physical and virtual worlds through IoT connections, so real-world changes are reflected in the digital representation.
  • E. Digital Twins: AI-enabled digital twins let developers and service providers remotely reconstruct and analyze virtual replicas of machines and processes.The passage identifies technical training and commercial customization as example purposes.
  • E. Digital Twins: Industrial digital-twin frameworks use machine learning for sensor-fault detection, isolation, accommodation, sensory-data validation, and fault-condition estimation.A VR- and AI-enabled twin also monitors and analyzes human-robot welding behaviors.
  • E. Digital Twins: Deep learning is applied in digital-twin architectures to learn features from high-dimensional unstructured data and model spatiotemporal behavior.Applications include edge-computing-aided Internet of Vehicles twins that improve vehicle computational-resource utilization, with a deep Q-network addressing edge-device overload.

F. Neural Interface

Neural interfaces, including brain-machine and brain-computer interfaces, extend metaverse interaction beyond VR headsets by reading and controlling neural activity. AI and machine-learning methods support accurate analysis and classification of complex neural signals, especially EEG inputs.

  • F. Neural Interface: Brain-machine and brain-computer interfaces aim to extend immersive metaverse interaction beyond VR headsets and controllers.These noninvasive devices currently read and control the mind only at a rudimentary level.
  • F. Neural Interface: AI and machine-learning algorithms enable accurate pattern recognition of complicated and sensitive neural signals within the BMI processing cycle.They operate in the pattern-recognition stage alongside data-engineering techniques used during preprocessing.
  • F. Neural Interface: EEG-based BCI research has examined both offline unsupervised and simulated online supervised brain-signal classification approaches.EEG is described as one of the most popular inputs for BCI systems.
  • F. Neural Interface: An improved common spatial pattern algorithm combined with transfer learning achieved high accuracy for classifying left- and right-hand imaginary movements and supported related recognition tasks.Knowledge transfer enabled the trained model to be reused for other classification and recognition tasks in the same domain.
  • F. Neural Interface: CapsNet was applied to BCI ERP detection because its capsule layers extract discriminative spatial features and key temporal correlations from EEG signals.The reported model outperformed some state-of-the-art approaches, including linear discriminant analysis and CNN.

IV. AI FOR THE METAVERSE: APPLICATION ASPECT · A. Healthcare

The survey examines AI-aided applications for the metaverse, focusing on healthcare, manufacturing, smart cities, and gaming alongside other potential domains. In healthcare, AI combined with VR, big data, wearable sensing, and deep learning can improve medical services, analysis, education, and virtual treatment environments.

  • IV. AI FOR THE METAVERSE: APPLICATION ASPECT: The application aspect covers healthcare, manufacturing, smart cities, and gaming, with brief discussion of e-commerce, human resources, real estate, and decentralized finance.These domains are presented as potential specialized services in the metaverse.
  • A. Healthcare: AI, VR, and big data are being incorporated into healthcare software and hardware to increase medical-device proficiency, reduce service costs, improve operations, and expand services.
  • A. Healthcare: AI supports healthcare diagnosis, faster and more accurate medical decisions, real-time medical imaging and radiology, and simulated environments for educating interns and medical students.
  • A. Healthcare: Wearable healthcare devices use AI to recognize complex sensory-data patterns and support daily assistance and early healthy-risk awareness.One physical-activity recognition method combines globally handcrafted features with locally deep features from multiple wearable devices.
  • A. Healthcare: Deep learning, particularly CNN architectures, has emerged for challenging medical-image-analysis tasks that require more specialized expertise than natural-image analysis.
  • A. Healthcare: VR environments can support healthcare applications such as motor rehabilitation and magnetic-resonance-imaging neurofeedback for collaborative treatment planning and educative training.
  • A. Healthcare: The metaverse can provide interactive virtual medical education, enabling students to practice and improve surgical skills through virtual-world lessons.

B. Manufacturing · C. Smart Cities

The paper presents AI-enabled metaverse applications in manufacturing and smart cities, linking virtual-physical integration, production optimization, intelligent services, and data-driven administration. Manufacturing emphasizes adaptive systems and production-line performance, while smart cities emphasize interactive services, AI automation, and governance challenges.

  • B. Manufacturing: Manufacturing metaverse systems translate a persistent virtual world onto the physical world through reality interaction and digital connections among machines and systems.This extends digital transformation by connecting virtual and physical entities with emerging technologies.
  • B. Manufacturing: Shortening product lifecycles and increasing product variants drive costly production-system reconfigurations and upgrades in manufacturing.ML-based systems further require time and computing resources for new data collection, preprocessing, and model learning.
  • B. Manufacturing: A symbiotic human-ML framework was leveraged to address manufacturing reconfiguration challenges.The framework is presented as a response to the costs associated with frequent production-system changes.
  • B. Manufacturing: A CNN-based transfer-learning method addresses intelligent fault diagnosis when labeled data are scarce and training and testing data distributions differ.The method targets both the lack of labeled learning data and distribution discrepancy between training and testing sets.
  • B. Manufacturing: AI is applied to manufacturing sectors to optimize production systems while improving production-line performance, scalability, and compatibility.One example develops a prediction model to estimate optimal buffer size in production lines by combining an ANN and another method, although the supplied passage truncates the latter.
  • C. Smart Cities: Smart cities collect citizen-needs information through IoT, video cameras, social media, and other sources to guide decisions about city services.The metaverse is positioned as a platform for smarter interactive services based on automatically collected user feedback and environmental data.
  • C. Smart Cities: AI enables automation and intelligence in smart services, including an EEG-based BCI architecture integrating VR and IoT to control home appliances.The architecture records brain signals using visual information captured through a head-mounted display.
  • C. Smart Cities: Building a metaverse ecosystem for administrative services requires authenticated big-data integration alongside rules for usage, ethics, and security.The proposed ecosystem covers environmental, educational, transportation, cultural, and other civil services, which AI can analyze and improve.

D. Gaming

Gaming is a prime metaverse application in which machine learning and deep learning are reshaping game development across console, mobile, and PC platforms. These methods help create realistic worlds, attractive challenges, unique stories, and systems or NPCs that respond dynamically to player actions.

  • D. Gaming: ML and DL are redefining gaming across console, mobile, and PC platforms.Gaming is presented as a prime application for developing the next generation of metaverse experiences.
  • D. Gaming: ML has strongly influenced video-game development over the last decade.Developers and studios increasingly use ML as a toolset for building more realistic worlds, attractive challenges, and unique stories.
  • D. Gaming: ML enables game systems and NPCs to respond dynamically and reasonably to player actions.This adaptive behavior supports more realistic game worlds and interactive experiences.

E. Other Potential Applications · V. METAVERSE PROJECTS

The paper identifies additional metaverse applications in e-commerce, human resources, real estate, and decentralized finance, then surveys projects that apply AI across varied virtual-world services. These projects span decentralized platforms, gaming worlds, integrated virtual ecosystems, user-created communities, and healthcare environments.

  • E. Other Potential Applications: E-commerce brands are integrating digital stores into the metaverse to create seamless shopping experiences across online and offline channels.This development is occurring despite VR devices remaining unpopular among mainstream consumers.
  • E. Other Potential Applications: Metaverse-based human resources initiatives aim to create friendlier, more interactive recruitment and workplace environments.Horizon Workrooms represents users as avatars who can work, collaborate, and communicate with others.
  • E. Other Potential Applications: Virtual real estate enables users to buy, sell, and trade limited-supply plots of land and virtual houses, commonly associated with NFTs.Examples include the Sandbox, Axie Infinity, Decentraland, and Upland.
  • E. Other Potential Applications: Blockchain-based decentralized finance brings cryptocurrency services such as lending, yield farming, and insurance into open financial systems without centralized authorities.DeFi is programmed through smart contracts and differs from finance controlled by a centralized entity or person.
  • V. METAVERSE PROJECTS: The surveyed metaverse projects—Decentraland, Sandbox, Realy, Star Atlas, Bit.Country, and DeHealth—apply AI to deliver diverse virtual-world services and applications.The projects include virtual environments spanning real estate, e-commerce, gaming, social interaction, trading, and healthcare.
  • V. METAVERSE PROJECTS: Decentraland is a decentralized Ethereum-based virtual reality platform where users create, experience, and monetize assets, content, and applications.Its virtual land is a non-fungible, transferable digital asset recorded by an Ethereum smart contract, without control by a centralized organization.
  • V. METAVERSE PROJECTS: The Sandbox is a decentralized, user-generated Ethereum virtual world where users and gamers build, own, and monetize gaming experiences.AI supports virtual-agent intelligence, rendering quality, and the reduction of gaming crashes through ML models, DL models, and AI frameworks.
  • V. METAVERSE PROJECTS: Realy integrates e-commerce, social, gaming, and trading in a super-realistic virtual ecosystem, while Star Atlas combines gaming, immersive 3D visualization, blockchain finance, and AI-powered game engines.Bit.Country provides user-created 3D communities with configurable rules, content-creation services, and a VR-oriented gaming view; DeHealth connects doctors and patients in a 3D healthcare metaverse with AI-bot recommendations and health analytics.

VI. CONCLUSION AND RESEARCH DIRECTIONS

The survey investigates AI’s role in establishing the metaverse and enhancing immersive virtual-world experiences. It identifies research directions involving conversational AI, AI-based hyperreal content generation, and explainable AI.

  • The survey examines AI’s role in the metaverse’s foundation and its potential to enhance immersive experiences in virtual worlds.
  • Research directions: Conversational AI can support purpose-specific virtual customers and employee assistants that provide human-like interactions in the metaverse.These systems combine speech recognition, language processing, advanced dialog management, and machine learning to recognize speech and text, understand intentions, decipher languages, and respond.
  • Research directions: AI could enable users to quickly create and endlessly remix hyperreal objects and content, including faces, bodies, plants, animals, vehicles, and buildings.The proposed direction combines virtual reality with AI-based content generation to support unique experiences and creation.
  • Research directions: Explainable AI can address the opacity of machine-learning-driven decisions in metaverse services and applications.It can increase end-user confidence, model auditability, and operative efficiency while mitigating legal risks and security threats and supporting reliable user experiences.
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