Collection of the most popular short stories written by Australian science fiction authors. The stories, chosen by computer on the basis of reader polls, share themes including birth, rebirth and transmutation. Story authors include George Turner, Damien Broderick, Rosaleen Love, Terry Dowling, Greg Egan, Jack Wodhams, Stephen Dedman, Leanne Frahm, David Lake and Dirk Strasser. The editor is author of 'Hot Lead, Cold Sweat'.

This book offers students a task-based introduction to Computer-Mediated Communication and the impact of the internet on social interaction. Divided into four parts which require students to learn, (theory), critique, (current issues), explore, (methods), and reflect, (practice), the book aims to: Provide a foundation to the social and communicative nature of information and communication technologies Enable students to engage with the key theoretical issues associated with CMC Equip students with the necessary research and technical skills as a stimulus to independent enquiry. In spite of the rapidly increasing interest in Internet Studies and CMC and the introduction of many university courses in the area, no specialised, introductory textbook exists. This coursebook responds to the need for such a text. Aimed primarily at communication students, this book would also be useful as a sourcebook for students of media, sociology, psychology and English Language Studies. Companion website resources can be found at http://crispinthurlow.net/cmc/

Deep neural networks (DNNs) with their dense and complex algorithms provide real possibilities for Artificial General Intelligence (AGI). Meta-learning with DNNs brings AGI much closer: artificial agents solving intelligent tasks that human beings can achieve, even transcending what they can achieve. Meta-Learning: Theory, Algorithms and Applications shows how meta-learning in combination with DNNs advances towards AGI. Meta-Learning: Theory, Algorithms and Applications explains the fundamentals of meta-learning by providing answers to these questions: What is meta-learning?; why do we need meta-learning?; how are self-improved meta-learning mechanisms heading for AGI ?; how can we use meta-learning in our approach to specific scenarios? The book presents the background of seven mainstream paradigms: meta-learning, few-shot learning, deep learning, transfer learning, machine learning, probabilistic modeling, and Bayesian inference. It then explains important state-of-the-art mechanisms and their variants for meta-learning, including memory-augmented neural networks, meta-networks, convolutional Siamese neural networks, matching networks, prototypical networks, relation networks, LSTM meta-learning, model-agnostic meta-learning, and the Reptile algorithm. The book takes a deep dive into nearly 200 state-of-the-art meta-learning algorithms from top tier conferences (e.g. NeurIPS, ICML, CVPR, ACL, ICLR, KDD). It systematically investigates 39 categories of tasks from 11 real-world application fields: Computer Vision, Natural Language Processing, Meta-Reinforcement Learning, Healthcare, Finance and Economy, Construction Materials, Graphic Neural Networks, Program Synthesis, Smart City, Recommended Systems, and Climate Science. Each application field concludes by looking at future trends or by giving a summary of available resources. Meta-Learning: Theory, Algorithms and Applications is a great resource to understand the principles of meta-learning and to learn state-of-the-art meta-learning algorithms, giving the student, researcher and industry professional the ability to apply meta-learning for various novel applications. A comprehensive overview of state-of-the-art meta-learning techniques and methods associated with deep neural networks together with a broad range of application areas Coverage of nearly 200 state-of-the-art meta-learning algorithms, which are promoted by premier global AI conferences and journals, and 300 to 450 pieces of key research Systematic and detailed exploration of the most crucial state-of-the-art meta-learning algorithm mechanisms: model-based, metric-based, and optimization-based Provides solutions to the limitations of using deep learning and/or machine learning methods, particularly with small sample sizes and unlabeled data Gives an understanding of how meta-learning acts as a stepping stone to Artificial General Intelligence in 39 categories of tasks from 11 real-world application fields

Everyone dreams of reliving oneʼs life, starting over, but when Gwen Song awoke in her fifteen-year-old body, she found herself in a world of urban sorcery. Though her troubled child- hood remained, the familiar society she had once lived in had all but disappeared, replaced by a parallel reality.Back in her teenage body, she must navigate her new world, once again forging friendships, filial bonds, and gain mastery over the skills necessary to survive in a 21st century Earth ruled by Mages and Magic, where humanity lives in enclaves shielded from Magical Beasts, Demi-humans, and otherworldly beings in a meta-world made unreal by a sorcerous apartheid.

For the second time in human history, we are on the verge of broad new breakthroughs in health, productivity, and personal freedom. And many-to-many networks are the reason. In business, government, and war, information is no longer the privilege of a powerful few. Now everyone knows what anyone knows, and we are applying that diversity of experience and perspective to expand the frontiers of our lives. We are starting to think together. The age of hierarchical organizations has ended. Social media networks, online forums, and guerilla broadcasting are connecting us in communities with fewer bureaucratic layers. In swarms, walkouts, strikes, and insurrections, people are sharing experience directly in real time, marching together into the public square, and demanding a greater voice in the new democracy. Networks Rising is the colorful story of unsung technology wizards waving us on, of philosophers struggling to free us from the dictates of church and state, and of sociologists, futurists, and even science fiction writers offering dozens of new schemes for living in a more connected world.

Deep reinforcement learning has attracted considerable attention recently. Impressive results have been achieved in such diverse fields as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to understand problems that were previously considered to be very difficult. In the game of Go, the program AlphaGo has even learned to outmatch three of the world’s leading players.Deep reinforcement learning takes its inspiration from the fields of biology and psychology. Biology has inspired the creation of artificial neural networks and deep learning, while psychology studies how animals and humans learn, and how subjects’ desired behavior can be reinforced with positive and negative stimuli. When we see how reinforcement learning teaches a simulated robot to walk, we are reminded of how children learn, through playful exploration. Techniques that are inspired by biology and psychology work amazingly well in computers: animal behavior and the structure of the brain as new blueprints for science and engineering. In fact, computers truly seem to possess aspects of human behavior; as such, this field goes to the heart of the dream of artificial intelligence. These research advances have not gone unnoticed by educators. Many universities have begun offering courses on the subject of deep reinforcement learning. The aim of this book is to provide an overview of the field, at the proper level of detail for a graduate course in artificial intelligence. It covers the complete field, from the basic algorithms of Deep Q-learning, to advanced topics such as multi-agent reinforcement learning and meta learning.

Delves into the psychological aspects of the virtual world to understand why humans often behave differently in cyberspace.