AI is the workhorse of modern data analytics and omnipresent across many sectors. Large Language Models and multi-modal foundation models are today capable of generating code, charts, visualizations, etc. How will these massive developments of AI in data analytics shape future data visualizations and visual analytics workflows? What is the potential of AI to reshape methodology and design of future visual analytics applications? What will be our role as visualization researchers in the future? What are opportunities, open challenges and threats in the context of an increasingly powerful AI? This Visualization Viewpoint discusses these questions in the special context of biomedical data analytics as an example of a domain in which critical decisions are taken based on complex and sensitive data, with high requirements on transparency, efficiency, and reliability. We map recent trends and developments in AI on the elements of interactive visualization and visual analytics workflows and highlight the potential of AI to transform biomedical visualization as a research field. Given that agency and responsibility have to remain with human experts, we argue that it is helpful to keep the focus on human-centered workflows, and to use visual analytics as a tool for integrating ``AI-in-the-loop''. This is in contrast to the more traditional term ``human-in-the-loop'', which focuses on incorporating human expertise into AI-based systems.

The field of visual computing is rapidly advancing due to the emergence of generative artificial intelligence (AI), which unlocks unprecedented capabilities for the generation, editing, and reconstruction of images, videos, and 3D scenes. In these domains, diffusion models are the generative AI architecture of choice. Within the last year alone, the literature on diffusion-based tools and applications has seen exponential growth and relevant papers are published across the computer graphics, computer vision, and AI communities with new works appearing daily on arXiv. This rapid growth of the field makes it difficult to keep up with all recent developments. The goal of this state-of-the-art report (STAR) is to introduce the basic mathematical concepts of diffusion models, implementation details and design choices of the popular Stable Diffusion model, as well as overview important aspects of these generative AI tools, including personalization, conditioning, inversion, among others. Moreover, we give a comprehensive overview of the rapidly growing literature on diffusion-based generation and editing, categorized by the type of generated medium, including 2D images, videos, 3D objects, locomotion, and 4D scenes. Finally, we discuss available datasets, metrics, open challenges, and social implications. This STAR provides an intuitive starting point to explore this exciting topic for researchers, artists, and practitioners alike.

For decades, the main theme of computer graphics and the emphasis of associated research has been synthesizing realistic visuals. To make it easier to generate realistic visuals, the lately popular neural rendering approach blends conventional graphics with machine learning technologies.

Making Visual Data Consumable Global data has grown from 4.4 zettabytes to 44 zettabytes between 2013 and 2020, and by 2025, IDC predicts there will be 163 zettabytes of data. Visual data has added much of this exponential increase in data volume. During the big data era, intelligent video analytics finally has achieved true intelligence in the form of artificial intelligence on the edge. Since the 1990's, engineers, CEOs, corporate and educational entities created and consumed data, more data and better data. The key to better consumption of big data was considered to be tied to better organized data.

Practice may or may not make perfect, but technology might be able to substitute for talent. That is, if Zepp has anything to do with it. The digital sports training device maker has introduced a new tool it calls Visual Computing, which promises to help users "take advantage of artificial intelligence technology" in order to better analyze a golf or baseball swing, or a basketball shot. Because why hire a coach when you can just pull out your smartphone? The technology depends on your smart device's camera to record a swing or a shot, and once this data is recorded, you can highlight various aspects of your technique.

Hassan Ugail is a Professor of Visual Computing and the Director of the Centre for Visual Computing at University of Bradford, UK. He works in the broad area of computer graphics, machine learning and artificial intelligence. In particular, he has developed novel computer based methods for reading and analysing the human face using artificial intelligence and machine learning techniques.