The development of Digital Twins (DTs) represents a transformative advance for simulating and optimizing complex systems in a controlled digital space. Despite their potential, the challenge of constructing DTs that accurately replicate and predict the dynamics of real-world systems remains substantial. This paper introduces an intelligent framework for the construction and evaluation of DTs, specifically designed to enhance the accuracy and utility of DTs in testing algorithmic performance. We propose a novel construction methodology that integrates deep learning-based policy gradient techniques to dynamically tune the DT parameters, ensuring high fidelity in the digital replication of physical systems. Moreover, the Mean STate Error (MSTE) is proposed as a robust metric for evaluating the performance of algorithms within these digital space. The efficacy of our framework is demonstrated through extensive simulations that show our DT not only accurately mirrors the physical reality but also provides a reliable platform for algorithm evaluation. This work lays a foundation for future research into DT technologies, highlighting pathways for both theoretical enhancements and practical implementations in various industries.

This article presents a digital twin (DT)-enhanced reinforcement learning (RL) framework aimed at optimizing performance and reliability in network resource management, since the traditional RL methods face several unified challenges when applied to physical networks, including limited exploration efficiency, slow convergence, poor long-term performance, and safety concerns during the exploration phase. To deal with the above challenges, a comprehensive DT-based framework is proposed to enhance the convergence speed and performance for unified RL-based resource management. The proposed framework provides safe action exploration, more accurate estimates of long-term returns, faster training convergence, higher convergence performance, and real-time adaptation to varying network conditions. Then, two case studies on ultra-reliable and low-latency communication (URLLC) services and multiple unmanned aerial vehicles (UAV) network are presented, demonstrating improvements of the proposed framework in performance, convergence speed, and training cost reduction both on traditional RL and neural network based Deep RL (DRL). Finally, the article identifies and explores some of the research challenges and open issues in this rapidly evolving field.

Agent-based modeling has been around for decades, and applied widely across the social and natural sciences. The scope of this research method is now poised to grow dramatically as it absorbs the new affordances provided by Large Language Models (LLM)s. Generative Agent-Based Models (GABM) are not just classic Agent-Based Models (ABM)s where the agents talk to one another. Rather, GABMs are constructed using an LLM to apply common sense to situations, act "reasonably", recall common semantic knowledge, produce API calls to control digital technologies like apps, and communicate both within the simulation and to researchers viewing it from the outside. Here we present Concordia, a library to facilitate constructing and working with GABMs. Concordia makes it easy to construct language-mediated simulations of physically- or digitally-grounded environments. Concordia agents produce their behavior using a flexible component system which mediates between two fundamental operations: LLM calls and associative memory retrieval. A special agent called the Game Master (GM), which was inspired by tabletop role-playing games, is responsible for simulating the environment where the agents interact. Agents take actions by describing what they want to do in natural language. The GM then translates their actions into appropriate implementations. In a simulated physical world, the GM checks the physical plausibility of agent actions and describes their effects. In digital environments simulating technologies such as apps and services, the GM may handle API calls to integrate with external tools such as general AI assistants (e.g., Bard, ChatGPT), and digital apps (e.g., Calendar, Email, Search, etc.). Concordia was designed to support a wide array of applications both in scientific research and for evaluating performance of real digital services by simulating users and/or generating synthetic data.

In order to improve the task execution capability of home service robot, and to cope with the problem that purely physical robot platforms cannot sense the environment and make decisions online, a method for building digital twin system for home service robot based on motion simulation is proposed. A reliable mapping of the home service robot and its working environment from physical space to digital space is achieved in three dimensions: geometric, physical and functional. In this system, a digital space-oriented URDF file parser is designed and implemented for the automatic construction of the robot geometric model. Next, the physical model is constructed from the kinematic equations of the robot and an improved particle swarm optimization algorithm is proposed for the inverse kinematic solution. In addition, to adapt to the home environment, functional attributes are used to describe household objects, thus improving the semantic description of the digital space for the real home environment. Finally, through geometric model consistency verification, physical model validity verification and virtual-reality consistency verification, it shows that the digital twin system designed in this paper can construct the robot geometric model accurately and completely, complete the operation of household objects successfully, and the digital twin system is effective and practical.

Software bots operating in multiple virtual digital platforms must understand the platforms' affordances and behave like human users. Platform affordances or features differ from one application platform to another or through a life cycle, requiring such bots to be adaptable. Moreover, bots in such platforms could cooperate with humans or other software agents for work or to learn specific behavior patterns. However, present-day bots, particularly chatbots, other than language processing and prediction, are far from reaching a human user's behavior level within complex business information systems. They lack the cognitive capabilities to sense and act in such virtual environments, rendering their development a challenge to artificial general intelligence research. In this study, we problematize and investigate assumptions in conceptualizing software bot architecture by directing attention to significant architectural research challenges in developing cognitive bots endowed with complex behavior for operation on information systems. As an outlook, we propose alternate architectural assumptions to consider in future bot design and bot development frameworks.

In this paper, to deal with the heterogeneity in federated learning (FL) systems, a knowledge distillation (KD) driven training framework for FL is proposed, where each user can select its neural network model on demand and distill knowledge from a big teacher model using its own private dataset. To overcome the challenge of train the big teacher model in resource limited user devices, the digital twin (DT) is exploit in the way that the teacher model can be trained at DT located in the server with enough computing resources. Then, during model distillation, each user can update the parameters of its model at either the physical entity or the digital agent. The joint problem of model selection and training offloading and resource allocation for users is formulated as a mixed integer programming (MIP) problem. To solve the problem, Q-learning and optimization are jointly used, where Q-learning selects models for users and determines whether to train locally or on the server, and optimization is used to allocate resources for users based on the output of Q-learning. Simulation results show the proposed DT-assisted KD framework and joint optimization method can significantly improve the average accuracy of users while reducing the total delay.

In December 2015, the revered French fashion house Louis Vuitton made a surprise announcement about the advertising campaign for its forthcoming spring-summer collection. The new range of clothes and accessories would be modelled on screen and in the pages of glossy magazines not by a famous actor or popstar but by a video game character: the pink-haired warrior Lightning from Final Fantasy XIII. Nicolas Ghesquière, the brand's creative director told the press he considered Lightning to be the "perfect avatar for a global heroic woman". The fictional character even carried out interviews to promote the partnership. It was not the first time a fashion brand had collaborated with a major video game. Previously, H&M, Moschino and Diesel had made digital clothes for The Sims.

Racial stereotypes were upended during a recent study that involved artificial intelligence. New research from the University of Georgia found that Black bots were considered more competent and more human than white or Asian bots used in the same study. This contrasts with past research on human-to-human interactions. "We found that in the digital space, because Black AI is so unusual, stereotypes amplified in the opposite direction," said researcher Nicole Davis, a third-year doctoral student in UGA's Terry College of Business. "The Black bots were not just seen as competent, but really competent – more competent than the white or Asian bots."

The Metaverse is being touted as the next iteration of the internet, supporting ongoing online 3D virtual environments where virtual experiences, real-time 3D content and other related media are connected and accessible through VR/AR, as well as through classic devices such as PC or mobile. It's essentially an immersive Web3 internet – Web3 being the idea of a new kind of internet services that is built using decentralised blockchain. With this new technology, users can meet in virtual spaces, represent themselves as avatars and share virtual objects.. Gartner predicts that by 2026 a quarter of people will spend at least an hour a day in the metaverse for work, shopping, education, social or entertainment. And 30% of organisations will have products and services delivered via the metaverse. However, despite the predictions, a recent YouGov survey revealed that just 37% of UK adults claim to be confident about describing the metaverse to others.

Ten years ago, if you bandied around the word Artificial Intelligence in a Kenyan newsroom, you would be branded an illusionist, today it is the buzzword. AI, machine learning and data processing are fast becoming essential in any newsroom that hopes to survive the digital revolution avalanche. In fact, job titles such as data scientist, data analyst, content strategist, content engineer and impact editors are fast gaining currency in media houses deemed digital journalism early adopters. Today, it is almost impossible to distinguish between an article authored by a machine and that written by a journalist, AI has enabled editors to figure out their audiences needs to granular levels and offer them articles that "speak to their souls". If the trend of automation of journalism catches on in the continent fast enough, in a few years it would be difficult to tell if the news anchor calling you "mpendwa msikilizaji" is a robot or human.