With the massive proliferation of data-driven algorithms, such as deep learning-based approaches, the availability of high-quality data is of great interest. Volumetric data is very important in medicine, as it ranges from disease diagnoses to therapy monitoring. When the dataset is sufficient, models can be trained to help doctors with these tasks. Unfortunately, there are scenarios where large amounts of data is unavailable. For example, rare diseases and privacy issues can lead to restricted data availability. In non-medical fields, the high cost of obtaining enough high-quality data can also be a concern. A solution to these problems can be the generation of realistic synthetic data using Generative Adversarial Networks (GANs). The existence of these mechanisms is a good asset, especially in healthcare, as the data must be of good quality, realistic, and without privacy issues. Therefore, most of the publications on volumetric GANs are within the medical domain. In this review, we provide a summary of works that generate realistic volumetric synthetic data using GANs. We therefore outline GAN-based methods in these areas with common architectures, loss functions and evaluation metrics, including their advantages and disadvantages. We present a novel taxonomy, evaluations, challenges, and research opportunities to provide a holistic overview of the current state of volumetric GANs.

KUWAIT: The Kuwait Foundation for the Advancement of Sciences (KFAS) is eager to restore scientific culture to reach a wide sector of audience, said KFAS' deputy director general for support programs and functions Amani Al-Baddah. Speaking on the sidelines of a lecture on the'Space Month' held at the KFAS' Scientific Center, she said this event is part of a series, called KFAS Links, in various scientific fields. David Hansen, the creator of inventor of Sophia the Robot, was selected to speak in the lecture, she noted. She pointed out that there is a wide sector of young people and school students interested in artificial intelligence in particular, and science and technology in general. She indicated that the lecture was a chance to educate youngsters on the relationship between arts and technology.

Unsupervised Learning: Foundations of Neural Computation is a collection of 21 papers published in the journal Neural Computation in the 10-year period since its founding in 1989 by Terrence Sejnowski. Neural Computation has become the leading journal of its kind. The editors of the book are Geoffrey Hinton and Terrence Sejnowski, two pioneers in neural networks. The selected papers include some of the most influential titles of late, for example, "What Is the Goal of Sensory Coding" by David Field and "An Information-Maximization Approach to Blind Separation and Blind Deconvolution" by Anthony Bell and Terrence Sejnowski. The edited volume provides a sample of important works on unsupervised learning, which cut across the fields of