This is the first part of a series of articles dedicated to extending Unity from 3D to 4D. In this instalment, we will explore the fourth dimension, from its representations in movies and video games, to its more mathematical and geometrical interpretations.
At the end of the series, you will learn how to create and manipulate 4D objects inside a modern game engine like Unity or Unreal.
You can find all the articles in this series here:
Over the past ten years, Artificial Intelligence (AI) and Machine Learning (ML) have steadily crept into the Art Industry. From Deepfakes to DALL·E, the impact of these new technologies can be longer be ignored, and many communities are now on the edge of a reckoning. On one side, the potential for modern AIs to generate and edit both images and videos is opening new job opportunities for millions; but on the other is also threatening a sudden and disruptive change across many industries.
The purpose of this long article is to serve as an introduction to the complex topic of AI Art: from the technologies that are powering this revolution, to the ethical and legal issues they have unleashed. While this is still an ongoing conversation, I hope it will serve as a primer for anyone interested in better understanding these phenomena—especially journalists who are keen to learn more about the benefits, changes and challenges that that AI will inevitably bring into our own lives. And since the potential of these technologies—and the best way to use them—are still being explored, there will likely be more questions and tentative suggestions, rather than definite answers.
In this article I will try to keep a positive outlook, as I feel is important to show and inspire people on how to better harness this technology, rather than just demonising it. And while predicting the future is beyond the scope of this article, there will be plenty of examples of how new art practices and technologies have impacted art communities in the past.
This is a companion article to the documentary about the world generation of Minecraft, which you can see below. This is a chance to expand on the content, including more information and resources that was not possible to include in the original 45 minutes of the video.
Have you ever wondered how many grains of sand are on this planet? Well …a rough estimate is… over 7 quintillion! That’s a 7 followed by 18 zeros. And yet, that’s not even half the number of the unique words in Minecraft. So how does Minecraft—and other games like it—build such complex, beautifully crafted yet fully procedural worlds? This article will explore how the game generates its worlds: from its tallest mountain, to its deepest cave. Welcome to the World Generation of Minecraft.
Every serious game developer knows that world building is an integral part of the process that creates a truly immersive experience. There are a variety of techniques that can be used to achieve this: from presenting the backstory of your player with a wall of text, to clever level design tricks known as environmental storytelling. The latter is often preferred. Unravelling the lore of your world from a few hints scattered across the levels is, de-facto, a game within the game. And while most players might just ignore them, others could find great pleasure in resolving this meta-puzzle.
Games like Dark Souls are notorious for their rich—and somewhat obscure—lore, which can be pieced together through the strong environmental storytelling and the various hints hidden in the item descriptions. Other games go even deeper than that, and create entire new languages for their fictional civilisations.
This is not something so uncommon, and many other media before games have long relied on fictional languages to create a much deeper sense of immersion. The entire world of The Lord of the Rings was built around a series of languages that J. R. R. Tolkien himself created before writing the books.
🇷🇺 A Russian version of this article is available here.
This series of articles will offer an overview and a practical tutorial on Minecraft Modding through the creation of data packs and resource packs. If you are interested in extending the game, this is the article for you!
If you are working in the field of Computer Science, chances are you might have encountered quite a lot of technical terms and foreign names, such as Dijkstra and Nyquist. And chances are that you have learnt a good part of them solely from books. And there is nothing more embarrassing than being in an interview and mispronouncing some key term in your field of expertise! Learning the correct pronunciation is also an act of respect towards the many men and women which dedication has become the foundation of our daily work.
This page is a collection of some of the most used—and tricky to pronounce—terms and names from Computer Science, with a focus on Game Development and Computer Graphics. For each term, you can find the “most correct” pronunciation using the International Phonetic Alphabet. For many others, you will also find the respective phonetic respelling used by Wikipedia.
Before you keep reading, there are a few points to keep in mind. Many of the names in this list are in foreign languages, and they cannot be pronounced “the correct way” in English. They have, however, an Anglicised version that makes use of the closest sounds found in the English language. Fourier, for instance, is pronounced [fuʁje] in French, but is often approximated in English as /ˈfʊrieɪ,/ (FOOR-ee-ey). Yet, another commonly accepted variations is /ˈfʊriər/ (FOOR-ee-er). Many names and technical terms also variations between British English (🇬🇧) and American English (🇺🇸); effort was made to include both variants.
If you are interested to learn the pronunciation of technical terms, Computational Graphics Pronunciation Guide is another good resource. I hope you will find this collection useful, and feel free to get in touch to suggest a change or a new term to add.
This is the complementary article to the short documentary about Conway’s Game of Life. Join me, as we celebrate the 50th anniversary of its original publication in the October 1970 issues of Scientific American.
This online course introduces the topic of modelling and simulating epidemics. If you are interested in understanding how Mathematicians, Programmers and Data Scientists are studying and fighting the spread of diseases, this series of posts is what you are looking for.
This online course is inspired by the recent COVID-19 pandemic. Now more than ever we need skilled and passionate people to focus on the complex subject of Epidemiology. I hope these articles will help some of you to get started.
All the revenue made from this article through Patreon will be donated to the National Emergencies Trust (NET) to help those most affected by the recent coronavirus outbreak. If you have recently become a patron for this reason, get in touch and I will add your contribution.