The "metaverse" will be built based on realistic virtual persons, and this foundation will facilitate distant presence, cooperation, education, and entertainment. To make this possible, new 3D virtual human creation tools must be developed that are easy to use and can be easily animated. Traditionally, this has required a lot of time and money spent by the AI artist. Because of this, these methods are not scalable. Allowing people to build their own avatars from one or more photographs is a more realistic solution.

In this paper, we design, analyze the convergence properties and address the implementation aspects of AFAFed. This is a novel Asynchronous Fair Adaptive Federated learning framework for stream-oriented IoT application environments, which are featured by time-varying operating conditions, heterogeneous resource-limited devices (i.e., coworkers), non-i.i.d. local training data and unreliable communication links. The key new of AFAFed is the synergic co-design of: (i) two sets of adaptively tuned tolerance thresholds and fairness coefficients at the coworkers and central server, respectively; and, (ii) a distributed adaptive mechanism, which allows each coworker to adaptively tune own communication rate. The convergence properties of AFAFed under (possibly) non-convex loss functions is guaranteed by a set of new analytical bounds, which formally unveil the impact on the resulting AFAFed convergence rate of a number of Federated Learning (FL) parameters, like, first and second moments of the per-coworker number of consecutive model updates, data skewness, communication packet-loss probability, and maximum/minimum values of the (adaptively tuned) mixing coefficient used for model aggregation.

Artificial Intelligence (AI) is a fast-growing and evolving field, and data scientists with AI skills are in high demand. The field requires broad training involving principles of computer science, cognitive psychology, and engineering. If you want to grow your data scientist career and capitalize on the demand for the role, you might consider getting a graduate degree in AI. U.S. News & World Report ranks the best AI graduate programs at computer science schools based on surveys sent to academic officials in fall 2021 and early 2022. Here are the top 10 programs that made the list as having the best AI graduate programs in the US.

Krafton said it will leverage hyperrealism character production technology to create digital avatars of humans and also tap into artificial intelligence (AI), text-to-speech, speech-to-text, and voice-to-face to improve their communication skills. The virtual humans will also exhibit motion-captured vivid movements, pupil movements, a wide range of facial expressions, and hairs on the skin. Last month, at a town hall, Krafton CEO CH Kim had said the company will actively leverage new technologies to offer unique experiences to gamers and creators. "We are geared up for realising an interactive virtual world (Metaverse) in stages and will continue to introduce more advanced versions of virtual humans and content based on the belief in the infinite scalability of such technologies," Shin Seok-jin, creative director at Krafton said in a statement. Hyperrealism is an old art concept that has inspired artists to create sculptures and paintings that give the illusion of being real. In the present day, it has struck a chord with game developers and animators who now have access to the tools to make digital characters look like real people.

It is the nature of our cognitive systems that we alternate between heuristics and deliberative reasoning. Heuristics are reasoning'shortcuts' based on patterns that help speed up decision making in familiar circumstances. Deliberation takes more attention and energy, but it can go beyond immediately available information and enables complex computations, comparisons, planning, and choice. This'dual mind' theory--as brought to popular attention in books by Kahneman,3 Rugg,7 and Evans1--explains why the heuristics associated with evolution for survival in a dangerous hunter-gatherer world are also responsible for causing systematic biases in our judgments. Says Kahneman: "Jumping to conclusions is efficient if the conclusions are likely to be correct and the costs of an occasional mistake acceptable. Jumping to conclusions is risky when the situation is unfamiliar, the stakes are high and there is no time to collect more information."

This paper stresses the importance of biases in the field of artificial intelligence (AI) in two regards. First, in order to foster efficient algorithmic decision-making in complex, unstable, and uncertain real-world environments, we argue for the structurewise implementation of human cognitive biases in learning algorithms. Secondly, we argue that in order to achieve ethical machine behavior, filter mechanisms have to be applied for selecting biased training stimuli that represent social or behavioral traits that are ethically desirable. We use insights from cognitive science as well as ethics and apply them to the AI field, combining theoretical considerations with seven case studies depicting tangible bias implementation scenarios. Ultimately, this paper is the first tentative step to explicitly pursue the idea of a re-evaluation of the ethical significance of machine biases, as well as putting the idea forth to implement cognitive biases into machines.

We believe that a Standard Model of the Mind should take into account continuous state representations, continuous timing, continuous actions, continuous learning, and parallel control loops. For each of these, we describe initial models that we have made exploring these directions. While we have demonstrated that it is possible to construct high-level cognitive models with these features (which are uncommon in most cognitive modeling approaches), there are many theoretical challenges still to be faced to allow these features to interact in useful ways and to characterize what may be gained by including these features.

Because navigation produces readily observable actions, it provides an important window into how perception and reasoning support intelligent behavior.

Interactive Narrative often involves dialogue with virtual dramatic characters. In this paper we compare two kinds of models of character style: one based on models derived from the Big Five theory personality, and the other derived from a corpus-based method applied to characters and films from the IMSDb archive. We apply these models to character utterances for a pilot narrative-based outdoor augmented reality game called Murder in the Arboretum. We use an objective quantitative metric to estimate the quality of a character model, with the aim of predicting model quality without perceptual experiments. We show that corpus-based character models derived from individual characters are often more detailed and specific than personality based models, but that there is a strong correlation between personality judgments of original character dialogue and personality judgments of utterances generated for Murder in the Arboretum that use the derived character models.

This chapter focuses on the evolution of Human-Centered Design (HCD) in aerospace systems over the last forty years. Human Factors and Ergonomics first shifted from the study of physical and medical issues to cognitive issues circa the 1980s. The advent of computers brought with it the development of human-computer interaction (HCI), which then expanded into the field of digital interaction design and User Experience (UX). We ended up with the concept of interactive cockpits, not because pilots interacted with mechanical things, but because they interacted using pointing devices on computer displays. Since the early 2000s, complexity and organizational issues gained prominence to the point that complex systems design and management found itself center stage, with the spotlight on the role of the human element and organizational setups. Today, Human Systems Integration (HSI) is no longer only a single-agent problem, but a multi-agent research field. Systems are systems of systems, considered as representations of people and machines. They are made of statically and dynamically articulated structures and functions. When they are at work, they are living organisms that generate emerging functions and structures that need to be considered in evolution (i.e., in their constant redesign). This chapter will more specifically, focus on human factors such as human-centered systemic representations, life critical systems, organizational issues, complexity management, modeling and simulation, flexibility, tangibility and autonomy. The discussion will be based on several examples in civil aviation and air combat, as well as aerospace.