Variational Autoencoders for multimodal data hold promise for many tasks in data analysis, such as representation learning, conditional generation, and imputation.Current architectures either share the encoder output, decoder input, or both across modalities to learn a shared representation. Such architectures impose hard constraints on the model. In this work, we show that a better latent representation can be obtained by replacing these hard constraints with a soft constraint. We propose a new mixture-of-experts prior, softly guiding each modality's latent representation towards a shared aggregate posterior.This approach results in a superior latent representation and allows each encoding to preserve information better from its uncompressed original features. In extensive experiments on multiple benchmark datasets and two challenging real-world datasets, we show improved learned latent representations and imputation of missing data modalities compared to existing methods.

Agent-based models (ABMs) and video games, including those taking advantage of virtual reality (VR), have undergone a remarkable parallel evolution, achieving impressive levels of complexity and sophistication. This paper argues that while ABMs prioritize scientific analysis and understanding and VR aims for immersive entertainment, they both simulate artificial worlds and can benefit from closer integration. Coupling both approaches indeed opens interesting possibilities for research and development in various fields, and in particular education, at the heart of the SIMPLE project, an EU-funded project on the development of digital tools for awareness raising on environmental issues. However, existing tools often present limitations, including technical complexity, limited functionalities, and lack of interoperability. To address these challenges, we introduce a novel framework for linking GAMA, a popular ABM platform, with Unity, a widely used game engine. This framework enables seamless data exchange, real-time visualization, and user interaction within VR environments, allowing researchers to leverage the strengths of both ABMs and VR for more impactful and engaging simulations. We demonstrate the capabilities of our framework through two prototypes built to highlight its potential in representing and interacting with complex socio-environmental system models. We conclude by emphasizing the importance of continued collaboration between the ABM and VR communities to develop robust, user-friendly tools, paving the way for a new era of collaborative research and immersive experiences in simulations.

Smart manufacturing systems increasingly rely on adaptive control mechanisms to optimize complex processes. This research presents a novel approach integrating Soft Actor-Critic (SAC) reinforcement learning with digital twin technology to enable real-time process control in robotic additive manufacturing. We demonstrate our methodology using a Viper X300s robot arm, implementing two distinct control scenarios: static target acquisition and dynamic trajectory following. The system architecture combines Unity's simulation environment with ROS2 for seamless digital twin synchronization, while leveraging transfer learning to efficiently adapt trained models across tasks. Our hierarchical reward structure addresses common reinforcement learning challenges including local minima avoidance, convergence acceleration, and training stability. Experimental results show rapid policy convergence and robust task execution in both simulated and physical environments, with performance metrics including cumulative reward, value prediction accuracy, policy loss, and discrete entropy coefficient demonstrating the effectiveness of our approach. This work advances the integration of reinforcement learning with digital twins for industrial robotics applications, providing a framework for enhanced adaptive real-time control for smart additive manufacturing process.

For its millions of fans, the Eurovision Song Contest is a showcase of the best pop tracks to come out of Europe and beyond. But while each act can seem to provide a more outrageous costume, set and musical performance than the last, there are certainly some repeating elements. These include lyrics which centre on love and relationships, as well as a rousing hook designed to be sung along to, both of which are features of the UK's entry this year. After being given the prompt'Write a song that would win the Eurovision Song Contest', OpenAI's bot generated the lyrics to'Song of Unity', as below. To see whether simply amalgamating these common features is actually effective, MailOnline asks AI chatbot, ChatGPT to write a Eurovision winning hit.

Hybrid and multi-cloud environments continued to grow and evolve last year, enabling stunning advances in artificial-intelligence technologies and expanded opportunities for companies to flourish and scale. Simultaneously, US inflation mushroomed to a 40-year peak and rates remain higher than at any time since the early 1980s. More expensive prices across the board have forced tech companies to right-size their spending, an action executives hope ultimately will make services more available to small and midsized businesses. Savvy tech companies will take advantage of the convergence of these trends in 2023. Opportunities exist for budget-conscious leaders in the cloud and AI arenas, cybersecurity enhancement and creativity regarding cloud-resource expenditures.

My name is Jim, and I'll be your instructor. Creating this course has been a dream of mine ever since I made the official tutorials for Bolt on Unity's Learn Site. In collaboration with Holistic3D, I took Penny's quintessential C# tutorial series The Beginner's Guide to Artificial Intelligence and adapted it to *drumroll*... Unity Visual Scripting! I've helped thousands learn visual scripting from the early years to today. Through an open-ended, practice-based approach you will follow along as each step is revealed for you to build two game worlds created with Unity 2021.3.9

Neural radiance field (NeRF), in particular its extension by instant neural graphics primitives, is a novel rendering method for view synthesis that uses real-world images to build photo-realistic immersive virtual scenes. Despite its potential, research on the combination of NeRF and virtual reality (VR) remains sparse. Currently, there is no integration into typical VR systems available, and the performance and suitability of NeRF implementations for VR have not been evaluated, for instance, for different scene complexities or screen resolutions. In this paper, we present and evaluate a NeRF-based framework that is capable of rendering scenes in immersive VR allowing users to freely move their heads to explore complex real-world scenes. We evaluate our framework by benchmarking three different NeRF scenes concerning their rendering performance at different scene complexities and resolutions. Utilizing super-resolution, our approach can yield a frame rate of 30 frames per second with a resolution of 1280x720 pixels per eye. We discuss potential applications of our framework and provide an open source implementation online.

The term "AI" has been used in video games since their inception, but it rarely means true artificial intelligence. Instead, it's a generic term to describe a preprogrammed opponent or character that feigns intelligence but is really just following a narrow set of instructions. This is slowly changing, though -- and the people who build video games are helping out. At GDC today, EA announced that it's been training AI agents in 2016's WWI shooter Battlefield 1.The company says it's the first time this sort of work has been done in a high-budget AAA title (which is disputable), but more importantly, it says the methods it's developing will help improve future games: providing tougher, more realistic enemies for human players and giving developers new ways to debug their software. EA's AI agents -- which, unlike bots, are expected to learn how to play instead of merely following instructions -- are being trained using a combination of two standard methods: imitation learning and reinforcement learning.

OAKLAND, Calif., Oct 13 (Reuters) - Used by two-thirds of the world's 100 biggest banks to aid lending decisions, credit scoring giant Fair Isaac Corp (FICO.N) and its artificial intelligence software can wreak havoc if something goes wrong. That crisis nearly came to pass early in the pandemic. As FICO recounted to Reuters, the Bozeman, Montana company's AI tools for helping banks identify credit and debit card fraud concluded that a surge in online shopping meant fraudsters must have been busier than usual. The AI software told banks to deny millions of legitimate purchases, at a time when consumers had been scrambling for toilet paper and other essentials. But consumers ultimately faced few denials, according to FICO.

Unity has been playing catch-up with its multiplayer capabilities, an area where it historically has been lacking,