Tech News - Game Development

GitHub for Unity is now available in version 1. GitHub for Unity 1.0 is a free and open source Unity editor extension that brings Git into Unity 5.6, 2017.x, and 2018.x. GitHub for Unity was announced as an alpha version in March 2017.  The beta version was released earlier this year. Now the full release GitHub for Unity 1.0 is available just in time for Unite Berlin 2018, scheduled to happen on June 19-21. GitHub for Unity 1.0 allows you to stay in sync with your team as you can now collaborate with other developers, pull down recent changes, and lock files to avoid troublesome merge conflicts. It also introduces two key features for game developers and their teams for managing large assets and critical scene files using Git, with the same ease of managing code files. Updates to Git LFS GitHub for Unity 1.0 has improved Git and Git LFS support for Mac. Git Large File Storage (LFS) replaces large files such as audio samples, videos, datasets, and graphics with text pointers inside Git. Previously, the package included full portable installations of the Git and Git LFS. Now, these are downloaded when needed, reducing the package size to 1.6MB. Critical Git and Git LFS updates and patches are distributed faster and in a more flexible way now. File locking File locking management is now a top-level view within the GitHub window. With this new feature developers can now lock or unlock multiple files. Other features include: Diffing support to visualize changes to files. The diffing program can be customized (set in the “Unity Preferences” area) directly from the “Changes” view in the GitHub window. No hassles of command line, as developers can now view project history, experiment in branches, craft a commit from their changes and push their code to GitHub without leaving Unity. A Git action bar for essential operations. Game developers will now get a notification within Unity whenever a new version is available. They can choose to download or skip the current update. Easy email sign in. Developers can sign in to their GitHub account with their GitHub username or the email address associated with their account. GitHub for Unity 1.0 is available for download at unity.github.com and from the Unity Asset Store. Lead developer at Unity, Andreia Gaita will conduct a GitHub for Unity talk on June 19 at Unite Berlin to explain how to incorporate Git into your game development workflow. Put your game face on! Unity 2018.1 is now available Unity announces a new automotive division and two-day Unity AutoTech Summit AI for Unity game developers: How to emulate real-world senses in your NPC agent behavior

The rise of the video game industry over the last two decades has been staggering, to say the least. Considered to be an area with massive revenue potential, we have seen a revolution in the way games are designed, developed and played across various platforms.Unity, the most popular cross-platform game development platform, is now encouraging even the non-programmers to take up Unity game development by equipping them with state-of-the-art tools for designing interactive games. Unity game development simplified for non-developers These days, there are a lot of non-developers, game designers and even artists who wish to build their own games. Well, they are now in for a treat. Unity have come up with their 2D and 3D Game kits wherein the users develop 2D or 3D gameplays without the need to code. With the help of these game kits, beginners can utilize the elements, tools and systems within the kit to design their gameplay. The Unity 2D game kit currently supports versions Unity 2017.3 and higher, while the 3D game kit is supported by Unity 2018.1 or higher. Visual scripting with Bolt Unity  have also introduced a new visual scripting tool called Bolt, which allows non-programmers to create new gameplays from scratch and design interactive systems in Unity, without having to write a single line of code. With live editing, predictive debugging and a whole host of other features, Bolt ensures you can get started with designing your own game in no time at all. The idea of introducing these game kits and the Bolt scripting engine is to encourage more and more non-programmers to take up game development and let their creative juices flow. It will also serve as a starting point for absolute beginners to start their journey in game development. To know more about how to use these Unity game kits, check out the introduction to game kit by Unity.

After about three long years of development, the most awaited Blender version, Blender 2.80 finally shipped yesterday. This release comes with a redesigned UI interface, workspaces, templates, Eevee real-time renderer, grease pencil, and much more. The user interface is revamped with a focus on usability and accessibility Blender’s user interface is revamped with a better focus on usability and accessibility. It has a fresh look and feel with a dark theme and modern icon set. The icons change color based on the theme you select so that they are readable against bright or dark backgrounds. Users can easily access the most used features via the default shortcut keys or map their own. You will be able to fully use Blender with a one-button trackpad or pen input as it now supports the left mouse button by default for selection. It provides a new right-click context menu for quick access to important commands in the given context. There is also a Quick Favorites popup menu where you can add your favorite commands. Get started with templates and workspaces You can now choose from multiple application templates when starting a new file. These include templates for 3D modeling, shading, animation, rendering, grease pencil based 2D drawing and animation, sculpting, VFX, video editing, and the list goes on. Workspaces give you a screen layout for specific tasks like modeling, sculpting, animating, or editing. Each template that you choose will provide a default set of workspaces that can be customized. You can create new workspaces or copy from the templates as well. Completely rewritten 3D Viewport Blender 2.8’s completely rewritten 3D viewport is optimized for modern graphics and offers several new features. The new Workbench render engine helps you get work done in the viewport for tasks like scene layout, modeling, and sculpting. Viewport overlays allow you to decide which utilities are visible on top of the render. The LookDev new shading mode allows you to test multiple lighting conditions (HDRIs) without affecting the scene settings. The smoke and fire simulations are overhauled to make them look as realistic as possible. Eevee real-time renderer Blender 2.80 has a new physically-based real-time renderer called Eevee. It performs two roles: a renderer for final frames and the engine driving Blender's real-time viewport for creating assets. Among the various features it supports volumetrics, screen-space reflections and refractions, depth of field, camera motion blur, bloom, and much more. You can create Eevee materials using the same shader nodes as Cycles, which makes it easier to render existing scenes. 2D animation with Grease Pencil Grease Pencil enables you to combine 2D and 3D worlds together right in the viewport. With this release, it has now become a “full 2D drawing and animation system.” It comes with a new multi-frame edition mode with which you can change and edit several frames at the same time. It has a build modifier to animate the drawings similar to the Build modifier for 3D objects. There are many other features added to grease pencil. Watch this video to get a glimpse of what you can create with it: https://www.youtube.com/watch?v=JF3KM-Ye5_A Check out for more features in Blender 2.80 on its official website. Blender celebrates its 25th birthday! Following Epic Games, Ubisoft joins Blender Development fund; adopts Blender as its main DCC tool Epic Games grants Blender $1.2 million in cash to improve the quality of their software development projects  

Last week, Google researchers announced the release of  Google Research Football Environment, a reinforcement learning environment where agents can master football. This environment comes with a physics-based 3D football simulation where agents control either one or all football players on their team, they learn how to pass between them, and further manage to overcome their opponent’s defense to score goals. The Football Environment offers a game engine, a set of research problems called Football Benchmarks and Football Academy and much more. The researchers have released a beta version of open-source code on Github to facilitate the research. Let’s have a brief look at each of the elements in the Google Research Football Environment. Football engine: The core of the Football Environment Based on the modified version of Gameplay Football, the Football engine simulates a football match including fouls, goals, corner and penalty kicks, and offsides. The engine is programmed in C++,  which allows it to run with GPU as well as without GPU-based rendering enabled. The engine allows learning from different state representations that contain semantic information such as the player’s locations and learning from raw pixels. The engine can be run in both stochastic mode as well as deterministic mode for investigating the impact of randomness. The engine is also compatible with OpenAI Gym API. Read Also: Create your first OpenAI Gym environment [Tutorial] Football Benchmarks: Learning from the actual field game The researchers propose a set of benchmark problems for RL research based on the Football Engine with the help of Football Benchmarks. These benchmarks highlight the goals such as playing a “standard” game of football against a fixed rule-based opponent. The researchers have provided three versions, the Football Easy Benchmark, the Football Medium Benchmark, and the Football Hard Benchmark, which differ only in the strength of the opponent. They also provide benchmark results for two state-of-the-art reinforcement learning algorithms including DQN and IMPALA that can be run in multiple processes on a single machine or concurrently on many machines. Image Source: Google’s blog post These results indicate that the Football Benchmarks are research problems that vary in difficulties. According to the researchers, the Football Easy Benchmark is suitable for research on single-machine algorithms and Football Hard Benchmark is challenging for massively distributed RL algorithms. Football Academy: Learning from a set of difficult scenarios   Football Academy is a diverse set of scenarios of varying difficulty that allow researchers to look into new research ideas and allow testing of high-level concepts. It also provides a foundation for investigating curriculum learning, research ideas, where agents can learn harder scenarios. The official blog post states, “Examples of the Football Academy scenarios include settings where agents have to learn how to score against the empty goal, where they have to learn how to quickly pass between players, and where they have to learn how to execute a counter-attack. Using a simple API, researchers can further define their own scenarios and train agents to solve them.” Users are giving mixed reaction to this news as some find nothing new in Google Research Football Environment. A user commented on HackerNews, “I guess I don't get it... What does this game have that SC2/Dota doesn't? As far as I can tell, the main goal for reinforcement learning is to make it so that it doesn't take 10k learning sessions to learn what a human can learn in a single session, and to make self-training without guiding scenarios feasible.” Another user commented, “This doesn't seem that impressive: much more complex games run at that frame rate? FIFA games from the 90s don't look much worse and certainly achieved those frame rates on much older hardware.” While a few others think that they can learn a lot from this environment. Another comment reads, “In other words, you can perform different kinds of experiments and learn different things by studying this environment.” Here’s a short YouTube video demonstrating Google Research Football. https://youtu.be/F8DcgFDT9sc To know more about this news, check out Google’s blog post. Google researchers propose building service robots with reinforcement learning to help people with mobility impairment Researchers propose a reinforcement learning method that can hack Google reCAPTCHA v3 Researchers input rabbit-duck illusion to Google Cloud Vision API and conclude it shows orientation-bias  

Unity has announced a new learning platform for professionals and hobbyists to advance their Unity knowledge and skills within their industry. The Unity Learn Premium, builds upon the launch of the free Unity Learn platform. The Unity Learn, platform hosts hundreds of free projects and tutorials, including two new beginner projects. Users can search learning materials by topic, content type, and level of expertise. Tutorials comes with  how-to instructions, video clips, and code snippets, making it easier to switch between Unity Learn and the Unity Editor. The Unity Learn Premium service allows creators to get immediate answers, feedback, and guidance directly from experts with Learn Live, biweekly interactive sessions with Unity-certified instructors. Learners can also track progress on guided learning paths, work through shared challenges with peers, and access an exclusive library of resources updated every month with the latest Unity releases. The premium version will offer live access to Unity experts, and learning content across industries, including architecture, engineering, and construction, automotive, transportation, and manufacturing), media and entertainment, and gaming. The Unity Learn Premium announcement comes on the heels of the launch of the Unity Academic Alliance. With this membership program,  educators and institutions can incorporate Unity into their curriculum. Jessica Lindl, VP and Global Head of Education, Unity Technologies wrote to us in a statement, “Until now, there wasn’t a definitive learning resource for learning intermediate to advanced Unity skills, particularly for professionals in industries beyond gaming. The workplace of today and tomorrow is fast-paced and driven by innovation, meaning workers need to become lifelong learners, using new technologies to upskill and ultimately advance their careers. We hope that Unity Learn Premium will be the perfect tool for professionals to continue on this learning path.” She further wrote to us, "Through our work to enable the success of creators around the world, we discovered there is no definitive source for advancing from beginner to expert across all industries, which is why we're excited to launch the Unity Learn Platform. The workplace of today and tomorrow is fast-paced and driven by innovation, forcing professionals to constantly be reskilling and upskilling in order to succeed. We hope the Unity Learn Platform enables these professionals to excel in their respective industries." Unity Learn Premium will be available at no additional cost for Plus and Pro subscribers and offered as a standalone subscription for $15/month. You can access more information here. Related News Developers can now incorporate Unity features into native iOS and Android apps Unity Editor will now officially support Linux Obstacle Tower Environment 2.0: Unity announces Round 2 of its ‘Obstacle Tower Challenge’ to test AI game players.

AMD has made V-EZ, the Vulkan wrapper library open source. The V-EZ wrapper is C based lightweight layer around Vulkan which reduces the complexity of using the Vulkan API. It abstracts away the lower level complexities of the Vulkan API. It also reduces differences between traditional graphics APIs and Vulkan by providing similar semantics to Vulkan. V-EZ is basically designed to increase the adoption of Vulkan in the gaming industry. It provides game developers with all the modern graphics API features without all of the low-level responsibilities. The low-level Vulkan API features abstracted in V-EZ include: Memory management Swapchain management Render Passes Pipeline permutations, layouts, and barriers Descriptor pools, sets, and set layouts Image layouts GLSL compilation Vulkan API objects and their interactions V-EZ has only a slight performance overhead as compared to native Vulkan APIs and offers most Vulkan API features including Batching queue submissions Multi-threaded command buffer recording Reusing command buffers Minimizing pipeline bindings Minimizing resource bindings Batching draw calls As mentioned on their Github repo, V-EZ is not hardware vendor specific and should work on non-AMD hardware as well. For building V-EZ you can follow these instructions: Run cmake to generate Visual Studio solution files or Linux make files. No specific settings need to be set. Pull down submodules git submodule init git submodule update Build V-EZ project. Reddit is abuzz with discussion on whether Vulkan is right to be advertised as a general replacement to OpenGL. Some said that Vulkan is a viable replacement to OpenGL but only at a lower level. A lot of the logic that openGL drivers take care of internally are exposed in the Vulkan API to allow for more optimization and performance focused coding. It's a lower level replacement. Most of of the code deals with stuff like GPU memory allocation, command buffering, synchronisation, and other such low-level concerns that, AFAIK, OpenGL doesn't let you touch. Some said Vulkan only stands out when you build games. I see indie game developers who are writing their own games without an existing engine would benefit greatly from higher abstractions of Vulkan, like this V-EZ project. They will get most of the performance improvements of Vulkan without a lot of the complexity. And in some cases the Vulkan abstraction is easier to understand and reason about than the OpenGL equivalent. Most people shouldn't use Vulkan directly. They should use a graphics library that would deal with the low level stuff. Only people making game engines and graphics libraries have to use low level Vulkan API and for those purposes Vulkan is superior. You can follow the entire Reddit thread for other comments. Also, see the Github repo for more details on V-EZ open sourcing. Think Silicon open sources GLOVE: An OpenGL ES over Vulkan middleware. Debugging in Vulkan.

The Blender team released beta version 2.8 of its Blender, a free and open-source 3D creation software, earlier this week. Blender 2.8 beta comes with new features and updates such as EEVEE, a high-end Viewport, Collections, Cycles, and 2D animation among others. Blender is a 3D creation suite that offers the entirety of the 3D pipeline including modeling, rigging, animation, simulation, rendering, compositing, and motion tracking. It allows video editing as well as game creation. What’s new in Blender 2.8 Beta? EEVEE Blender 2.8 beta comes with EEVEE, a new physically based real-time renderer. EEVEE works as a renderer for final frames, and also as the engine driving Blender’s real-time viewport. It consists of advanced features like volumetrics, screen-space reflections and refractions, subsurface scattering, soft and contact shadows, depth of field, camera motion blur and bloom. A new 3D Viewport There's a new and modern 3D viewport that was completely rewritten. It can help optimize the modern graphics cards as well as add powerful new features. It consists of a workbench engine that helps visualize your scene in flexible ways. EEVEE also helps power the viewport to enable interactive modeling and painting with PBR materials. 2D Animation There are a new and improved 2D drawing capabilities, which include a new Grease Pencil. Grease Pencil is a powerful and new 2D animation system that was added, with a native 2D grease pencil object type, modifier, and shader effects. In a nutshell, it helps to create a user-friendly interface for the 2D artist. Collections Blender 2.8 beta introduces ‘collections’, a new concept that lets you organize your scene with the help of Collections and View Layers. Cycles Blender 2.8 beta comes with a new feature called Cycles that includes new principled volume and hair shaders, bevel and ambient occlusion shaders, along with many other improvements and optimizations. Other features Dependency Graph: In blender 2.8 beta, the core object evaluation and computation system have been rewritten. Blender offers better performance for modern many-core CPUs as well as for new features in the future releases. Multi-object editing: Blender 2.8 beta comes with multiple-object editing that allows you to enter edit modes for multiple objects together. For more information, check out the official Blender 2.8 beta release notes. Mozilla partners with Khronos Group to bring glTF format to Blender Building VR objects in React V2 2.0: Getting started with polygons in Blender Blender 2.5: Detailed Render of the Earth from Space

Earlier this year in January, Google’s DeepMind AI AlphaStar had defeated two professional players, TLO and MaNa, at StarCraft II, a real-time strategy game. Two days ago, DeepMind announced that AlphaStar has now achieved the highest possible online competitive ranking, called Grandmaster level, in StarCraft II. This makes AlphaStar the first AI to reach the top league of a widely popular game without any restrictions. AplhaStar used the multi-agent reinforcement learning technique and rated above 99.8% of officially ranked human players. It was able to achieve the Grandmaster level for all the three StarCraft II races - Protoss, Terran, and Zerg. The DeepMind researchers have published the details of AlphaStar in the paper titled, ‘Grandmaster level in StarCraft II using multi-agent reinforcement learning’. https://twitter.com/DeepMindAI/status/1189617587916689408 How did AlphaStar achieve the Grandmaster level in StarCraft II? The DeepMind researchers were able to develop a robust and flexible agent by understanding the potential and limitations of open-ended learning. This helped the researchers to make AlphaStar cope with complex real-world domains. “Games like StarCraft are an excellent training ground to advance these approaches, as players must use limited information to make dynamic and difficult decisions that have ramifications on multiple levels and timescales,” states the blog post. The StarCraft II video game requires players to balance high-level economic decisions with individual control of hundreds of units. When playing this game, humans are under physical constraints which limits their reaction time and their rate of actions. Accordingly, AphaStar was also imposed with these constraints, thus making it suffer from delays due to network latency and computation time. In order to limit its actions per minute (APM), AphaStar’s peak statistics were kept substantially lower than those of humans. To align with the standard human movement, it also had a limited viewing of the portion of the map, AlphaStar could register only a limited number of mouse clicks and had only 22 non-duplicated actions to play every five seconds. AlphaStar uses a combination of general-purpose techniques like neural network architectures, imitation learning, reinforcement learning, and multi-agent learning. The games were sampled from a publicly available dataset of anonymized human replays, which were later trained to predict the action of every player. These predictions were then used to procure a diverse set of strategies to reflect the different modes of human play. Read More: DeepMind’s Alphastar AI agent will soon anonymously play with European StarCraft II players Dario “TLO” WÜNSCH, a professional starcraft II player says, “I’ve found AlphaStar’s gameplay incredibly impressive – the system is very skilled at assessing its strategic position, and knows exactly when to engage or disengage with its opponent. And while AlphaStar has excellent and precise control, it doesn’t feel superhuman – certainly not on a level that a human couldn’t theoretically achieve. Overall, it feels very fair – like it is playing a ‘real’ game of StarCraft.” According to the paper, AlphaStar had the 1026 possible actions available at each time step, thus it had to make thousands of actions before learning if it has won or lost the game. One of the key strategies behind AlphaStar’s performance was learning human strategies. This was necessary to ensure that the agents keep exploring those strategies throughout self-play. The researchers say, “To do this, we used imitation learning – combined with advanced neural network architectures and techniques used for language modeling – to create an initial policy which played the game better than 84% of active players.” AlphaStar also uses a latent variable to encode the distribution of opening moves from human games. This helped AlphaStar to preserve the high-level strategies and enabled it to represent many strategies within a single neural network. By training the advances in imitation learning, reinforcement learning, and the League, the researchers were able to train AlphaStar Final, the agent that reached the Grandmaster level at the full game of StarCraft II without any modifications. AlphaStar used a camera interface, which helped it get the exact information that a human player would receive. All the interface and restrictions faced by AlphaStar were approved by a professional player. Finally, the results indicated that general-purpose learning techniques can be used to scale AI systems to work in complex and dynamic environments involving multiple actors. AlphaStar’s great feat has got many people excited about the future of AI. https://twitter.com/mickdooit/status/1189604170489315334 https://twitter.com/KaiLashArul/status/1190236180501139461 https://twitter.com/JoshuaSpanier/status/1190265236571459584 Interested readers can read the research paper to check AlphaStar’s performance. Head over to DeepMind’s blog for more details. Google AI introduces Snap, a microkernel approach to ‘Host Networking’ Are we entering the quantum computing era? Google’s Sycamore achieves ‘quantum supremacy’ while IBM refutes the claim Smart Spies attack: Alexa and Google Assistant can eavesdrop or vish (voice phish) unsuspecting users, disclose researchers from SRLabs

Yesterday, Valve Corporation, the popular American video game developer, announced the Half-Life: Alyx, the first new game in the popular Half-Life series in over a decade. The company tweeted that it will unveil the first look on Thursday, 21st November 2019, at 10 am Pacific Time. https://twitter.com/valvesoftware/status/1196566870360387584 Half-Life: Alyx, a brand-new game in the Half-Life universe, is designed exclusively for PC virtual reality systems (Valve Index, Oculus Rift, HTC Vive, Windows Mixed Reality). Talking about Valve’s history in PC games, it has created some of the most influential and critically games ever made. However, “Valve has famously never finished either of its Half-Life supposed trilogies of games. After Half-Life and Half-Life 2, the company created Half-Life: Episode 1 and Half-Life: Episode 2, but no third game in the series,” the Verge reports. Ars Technica reveals, “The game's name confirms what has been loudly rumored for months: that you will play this game from the perspective of Alyx Vance, a character introduced in 2004's Half-Life 2. Instead of stepping forward in time, HLA will rewind to the period between the first two mainline Half-Life games.” “A data leak from Valve's Source 2 game engine, as uncovered in September by Valve News Network, pointed to a new control system labeled as the "Grabbity Gloves" in its codebase. Multiple sources have confirmed that this is indeed a major control system in HLA,” Ars Technica claims. These Grabbity gloves can also be described as ‘Magnet gloves’, which allow pointing out and attracting distant objects to your hands. Valve has already announced plans to support all major VR PC systems for its next VR game, and these new gloves seem like the right system to scale to whatever controllers that would come to VR. Many gamers are excited to check out this Half-life version and are also looking forward to whether the company really stands up to what it says. A user on Hacker News commented, “Wonder what Valve is doubling down with this title? It seems like the previous games were all ground-breaking narratives, but with most of the storytellers having left in the last few years, I'd be curious to see what makes this different than your standard VR games.” Another user on Hacker News commented, “From the tech side it was the heavy, and smart, use of scripting that made HL1 stand out. With HL2 it was the added physics engine trough the change to Source, back then that used to be a big deal and whole gameplay mechanics revolve around that (gravity gun). In that context, I do not really consider it that surprising for the next HL project to focus on VR because even early demos of that combination looked already very promising 5 years ago” We will update this space after the Half-Life: Alyx is unveiled on Thursday. To know more about the announcement in detail, read Ars Technica’s complete coverage. Valve reveals new Index VR Kit with detail specs and costs upto $999 Why does Oculus CTO John Carmack prefer 2D VR interfaces over 3D Virtual Reality interfaces? Oculus Rift S: A new VR with inside-out tracking, improved resolution and more!

Epic games released a new version of it’s flagship game engine, Unreal Engine 4.22. This release comes with a total of 174 improvements, focused on “pushing the boundaries of photorealism in real-time environments”. It also comes with improved build times, up to 3x faster, and new features such as real-time ray tracing. Unreal Engine 4.22 also adds support for Microsoft HoloLens remote streaming and Visual Studio 2019. What’s new in Unreal Engine 4.22? Real-Time Ray Tracing and Path Tracing (Early Access): The Ray Tracing features, first introduced in a preview in mid-february,  are composed of a series of ray tracing shaders and ray tracing effects. They help in achieving natural realistic looking lighting effects in real-time. The Path Tracer includes a full global illumination path for indirect lighting that creates ground truth reference renders right inside of the engine. This improves workflow content in a scene without needing to export to a third-party offline path tracer for comparison. New Mesh drawing pipeline: The new pipeline for Mesh drawing results in faster caching of information for static scene elements. Automatic instancing merges draw calls where possible, resulting in four to six time fewer lines of code. This change is a big one so backwards compatibility for Drawing Policies is not possible. Any Custom Drawing Policies will need to be rewritten as FMeshPassProcessors in the new architecture. Multi-user editing (Early Access): Simultaneous multi user editing allows multiple level designers and artists to connect multiple instances of Unreal Editor together to work collaboratively in a shared editing session. Faster C++ iterations: Unreal has licensed Molecular Matters' Live++ for all developers to use on their Unreal Engine projects, and integrated it as the new Live Coding feature. Developers can now make C++ code changes in their development environment and compile and patch it into a running editor or standalone game in a few seconds. UE 4.22 also optimizes UnrealBuildTool and UnrealHeaderTool, reducing build times and resulting in up to 3x faster iterations when making C++ code changes. Improved audio with TimeSynth (Early access): TimeSynth is a new audio component with features like sample accurate starting, stopping, and concatenation of audio clips. Also includes precise and synchronous audio event queuing. Enhanced Animation: Unreal Engine 4.22 comes with a new Animation Plugin which is based upon the Master-Pose Component system and adds blending and additive Animation States. It reduces the overall amount of animation work required for a crowd of actors. This release also features an Anim Budgeter tool to help developers set a fixed budget per platform (ms of work to perform on the gamethread). Improvements in the Virtual Production Pipeline: New Composure UI: Unreal’s built-in compositing tool Composure has an updated UI to achieve real time compositing capabilities to build images, video feeds, and CG elements directly within the Unreal Engine. OpenColorIO (OCIO) color profiles: Unreal Engine now supports the Open Color IO framework for transforming the color space of any Texture or Composure Element directly within the Unreal Engine. Hardware-accelerated video decoding (Experimental): On Windows platforms, UE 4.22 can use the GPU to speed up the processing of H.264 video streams to reduce the strain on the CPU when playing back video streams. New Media I/O Formats: UE 4.22 ships with new features for professional video I/O input formats and devices, including 4K UHD inputs for both AJA and Blackmagic and AJA Kona 5 devices. nDisplay improvements (Experimental): Several new features make the nDisplay multi-display rendering system more flexible, handling new kinds of hardware configurations and inputs. These were just a select few updates. To learn more about Unreal Engine 4.22 head on over to the Unreal Engine blog. Unreal Engine 4.22 update: support added for Microsoft’s DirectX Raytracing (DXR) Unreal Engine 4.20 released with focus on mobile and immersive (AR/VR/MR) devices Implementing an AI in Unreal Engine 4 with AI Perception components [Tutorial]

On Tuesday, the University of Cambridge published a research performed on  thousands of online game players. The study shows how an online game can work like a “vaccine'' and increase skepticism towards fake news. This was done by giving people a weak dose of the methods behind disinformation campaigns. Last year in February, University of Cambridge researchers helped in launching the browser game Bad News. In this game, you take on the role of fake news-monger. Drop all pretense of ethics and choose a path that builds your persona as an unscrupulous media magnate. But while playing the game you have to keep an eye on your ‘followers’ and ‘credibility’ meters. The task is to get as many followers as you can while slowly building up fake credibility as a news site. And you lose if you tell obvious lies or disappoint your supporters! Jon Roozenbeek, study co-author from Cambridge University, and Dr Sander van der Linden, Director of the Cambridge Social Decision-Making Lab worked with Dutch media collective DROG and design agency Gusmanson to develop Bad News. DROG develops programs and courses and also conducts research aimed at recognizing disinformation online. The game is primarily available in English, and many other languages like Czech, Dutch, German, Greek, Esperanto, Polish, Romanian, Serbian, Slovenian and Swedish. They have also developed a special Junior version for children in the age group between 8 - 11. Jon Roozenbee, said: “We are shifting the target from ideas to tactics. By doing this, we are hoping to create what you might call a general ‘vaccine’ against fake news, rather than trying to counter each specific conspiracy or falsehood.” Hu further added, “We want to develop a simple and engaging way to establish media literacy at a relatively early age, then look at how long the effects last”. The study says that the game increased psychological resistance to fake news After the game was available to play, thousands of people spent fifteen minutes completing it, and many allowed the data to be used for the research. According to a study of 15000 participants, this game has shown to increase “psychological resistance” to fake news. Players stoke anger and fear by manipulating news and social media within the simulation: they deployed twitter bots, photo-shopped evidence, and incited conspiracy theories to attract followers. All of this was done while maintaining a “credibility score” for persuasiveness. “Research suggests that fake news spreads faster and deeper than the truth, so combating disinformation after-the-fact can be like fighting a losing battle,” said Dr Sander van der Linden. “We wanted to see if we could preemptively debunk, or ‘pre-bunk’, fake news by exposing people to a weak dose of the methods used to create and spread disinformation, so they have a better understanding of how they might be deceived. “This is a version of what psychologists call ‘inoculation theory’, with our game working like a psychological vaccination.” The study was performed by asking players to rate the reliability of content before and after gameplay To gauge the effects of the game, players were asked to rate the reliability of a series of different headlines and tweets before and after gameplay. They were randomly allocated a mixture of real and fake news. There were six “badges” to earn in the game, each reflecting a common strategy used by creators of fake news: impersonation; conspiracy; polarisation; discrediting sources; trolling; emotionally provocative content. There were in-game questions too that measured the effects of Bad News deployed for four of its featured fake news badges. As a result for the disinformation tactic of “impersonation”, which involves mimicking of trusted personalities on social media, the game reduced perceived reliability of the fake headlines and tweets by 24% from pre to post gameplay. Further it reduced perceived reliability of deliberately polarising headlines by about 10%, and “discrediting sources” that is attacking a legitimate source with accusations of bias – by 19%. For “conspiracy”, the spreading of false narratives blaming secretive groups for world events, perceived reliability was reduced by 20%. The researchers also found that those who registered as most susceptible to fake news headlines in the beginning benefited most from the “inoculation”. “We find that just fifteen minutes of gameplay has a moderate effect, but a practically meaningful one when scaled across thousands of people worldwide, if we think in terms of building societal resistance to fake news,” said van der Linden. The sample for the study was skewed towards younger male The sample was self-selecting those who came across the game online and opted to play, and as such was skewed toward younger, male, liberal, and more educated demographics. Hence, the first set of results from Bad News has its limitations, say researchers. However, the study found the game to be almost equally effective across age, education, gender, and political persuasion. But researchers did not mention if they plan to do a follow up study keeping in mind the limitations of this research. “Our platform offers early evidence of a way to start building blanket protection against deception, by training people to be more attuned to the techniques that underpin most fake news,” added Roozenbeek. Community discussion revolve around various fake news reporting techniques This news has attracted much attention on Hacker News, and users have commented about various news reporting techniques that journalists use to promote different stories. One of the user comments reads, “The "best" fake news these days is the stuff that doesn't register even to people are read-in on the usual anti-patterns. Subtle framing, selective quotation, anonymous sources, "repeat the lie" techniques, and so on, are the ones that I see happening today that are hard to immunize yourself from. Ironically, the people who fall for these are more likely to self-identify as being aware and clued in on how to avoid fake news.” Another users says, “Second best. The best is selective reporting. Even if every story is reported 100% accurately and objectively, by choosing which stories are promoted, and which buried, you can set any agenda you want.” One of them also commented that the discussion diluted the term Fake news in influences and propaganda, it reads, “This discussion is falling into a trap where "Fake News" is diluted to synonym for all influencing news and propaganda. Fake News is propaganda that consists of deliberate disinformation or hoaxes. Nothing mentioned here falls into a category of Fake News. Fake News creates cognitive dissonance and distrust. More subtler methods work differently. But mainstream media also does Fake News" arguments are whataboutism.” To this another user responds, “I've upvoted you because you make a good point, but I disagree. IMO, Fake News, in your restrictive definition, is to modern propaganda what Bootstrap is to modern frontend dev. It's an easy shortcut, widely known, and even talented operators are going to use it because it's the easiest way to control a (domestic or foreign) population. But resources are there, funding is there, to build much more subtle/complex systems if needed. Cut away Bootstrap, and you don't particularly dent the startup ecosystem. Cut away fake news, and you don't particularly dent the ability of troll farms to get work done. We're in a new era, fake news or not.” Game rivals, Microsoft and Sony, form a surprising cloud gaming and AI partnership DeepMind’s AI uses reinforcement learning to defeat humans in multiplayer games Introducing Minecraft Earth, Minecraft’s AR-based game for Android and iOS users  

Last week, a team within Area 120, Google’s workshop for experimental products, introduced an experimental prototype of Game Builder. It is a “game building sandbox” that enables you to build and play 3D games in just a few minutes. It is currently in early access and is available on Steam. https://twitter.com/artofsully/status/1139230946492682240 Here’s how Game Builder makes “building a game feel like playing a game”: Source: Google Following are some of the features that Game Builder comes with: Everything is multiplayer Game Builder’s always-on multiplayer feature allows multiple users to build and play games simultaneously. Your friends can also play the game while you are working on it. Thousands of 3D models from Google Poly You can find thousands of free 3D models (such as rocket ship, synthesizer, ice cream cone) to use in your games from Google Poly. You can also “remix” most of the models using Tilt Brush and Google Blocks application integration to make it fit for your game. Once you find the right 3D model, you can easily and instantly use it in your game. No code, no compilation required This platform is designed for all skill levels, from enabling players to build their first game to providing game developers a faster way to realize their game ideas. Game Builder’s card-based visual programming allows you to bring your game to life with bare minimum knowledge of programming. You just need to drag and drop cards to answer questions like  “How do I move?.” You can also create your own cards with Game Builder’s extensive JavaScript API. It allows you to script almost everything in the game. As the code is live, you just need to save the changes and you are ready to play the game without any compilation. Apart from these features, you can also create levels with terrain blocks, edit the physics of objects, create lighting and particle effects, and more. Once the game is ready you can share your creations on Steam Workshop. Many people are commending this easy way of game building, but also think that this is nothing new. We have seen such platforms in the past, for instance, GameMaker by YoYo Games. “I just had a play with it. It seems very well thought out. It has a very nice tutorial that introduces all the basic concepts. I am looking forward to trying out the multiplayer aspect, as that seems to be the most compelling thing about it,”  a Hacker News user commented. You can read Google’s official announcement for more details. Google Research Football Environment: A Reinforcement Learning environment for AI agents to master football Google Walkout organizer, Claire Stapleton resigns after facing retaliation from management Ian Lance Taylor, Golang team member, adds another perspective to Go being Google’s language

Last week, the team at Minecraft introduced a new AR-based game called ‘Minecraft Earth’, which is free for Android and iOS users. The most striking feature about Minecraft Earth is that it builds on the real world with augmented reality, I am sure it will remind you of the game Pokémon Go. https://twitter.com/minecraftearth/status/1129372933565108224 Minecraft has around 91 million active players, and now Microsoft is looking forward to taking the Pokémon Go concept on the next level by letting Minecraft players create and share whatever they’ve made in the game with friends in the real world. Users can now build something in Minecraft on their phones and then drop it into their local park for all their friends to see it together at the same location. This game aims to transform single user AR gaming to multi-user gaming while letting users access the virtual world that’s shared by everyone. Read Also: Facebook launched new multiplayer AR games in Messenger Minecraft Earth will be available in beta on iOS and Android, this summer. This game brings modes like creative that has unlimited blocks and items; or survival where you lose all your items when you die. Torfi Olafsson, game director of Minecraft Earth, explains, “This is an adaptation, this is not a direct translation of Minecraft. While it’s an adaptation, it’s built on the existing Bedrock engine so it will be very familiar to existing Minecraft players. If you like building Redstone machines, or you’re used to how the water flows, or how sand falls down, it all works. Olafsson further added, “All of the mobs of animals and creatures in Minecraft are available, too, including a new pig that really loves mud. We have tried to stay very true to the kind of core design pillars of Minecraft, and we’ve worked with the design team in Stockholm to make sure that the spirit of the game is carried through.” Players have to venture out into the real world to collect things just like how it works in Pokemon Go! Minecraft Earth has something similar to pokéstops called “tapables”, which are randomly placed in the world around the player. They are designed to give players rewards that allow them to build things, and players need to collect as many of these as possible in order to get resources and items to build vast structures in the building mode. The maps in this game are based on OpenStreetMap that has allowed Microsoft to place Minecraft adventures into the world. On the Minecraft Earth map, these adventures spawn dynamically and are also designed for multiple people to get involved in. Players can play together while sitting side by side to experience similar adventures at the exact same time and spot. They can also fight monsters, break down structures for resources together, and even stand in front of a friend to block them from physically killing a virtual sheep. Players can even see the tools that fellow players have in their hands on your phone’s screen, alongside their username. Microsoft is also using its Azure Spatial Anchors technology in Minecraft Earth which uses machine vision algorithms so that real-world objects can be used as anchors for digital content. Niantic, a Pokémon Go developer had to recently settle a lawsuit with angry homeowners who had pokéstops placed near their houses. With what happened with Pokemon Go in the past could be a threat for games like Minecraft Earth too. As there are many challenges in bringing augmented reality within private spaces. Saxs Persson, creative director of Minecraft said, “There are lots of very real challenges around user-generated content. It’s a complicated problem at the scale we’re talking about, but that doesn’t mean we shouldn’t tackle it.” https://twitter.com/Toadsanime/status/1129374278384795649 https://twitter.com/ExpnandBanana/status/1129419087216562177 https://twitter.com/flamnhotsadness/status/1129429075490160642 https://twitter.com/pixiebIush/status/1129455271833550848 To know more about Minecraft Earth, check out Minecraft’s page. Game rivals, Microsoft and Sony, form a surprising cloud gaming and AI partnership Obstacle Tower Environment 2.0: Unity announces Round 2 of its ‘Obstacle Tower Challenge’ to test AI game players OpenAI Five beats pro Dota 2 players; wins 2-1 against the gamers

Earlier this year, DeepMind’s AI Alphastar defeated two professional players at StarCraft II, a real-time strategy video game. Now, European Starcraft II players will get a chance to face off experimental versions of AlphaStar, as part of ongoing research into AI. https://twitter.com/MaxBakerTV/status/1149067938131054593 AlphaStar learns by imitating the basic micro and macro-strategies used by players on the StarCraft ladder. A neural network was trained initially using supervised learning from anonymised human games released by Blizzard. Once the agents get trained from human game replays, they’re then trained against other competitors in the “AlphaStar league”. This is where a multi-agent reinforcement learning process starts. New competitors are added to the league (branched from existing competitors). Each of these agents then learns from games against other competitors. This ensures that each competitor performs well against the strongest strategies, and does not forget how to defeat earlier ones. Anyone who wants to participate in this experiment will have to opt into the chance to play against the StarCraft II program. There will be an option provided in the in-game pop-up window. Users can alter their opt-in selection at any time. To ensure anonymity, all games will be blind test matches. European players that opt-in won't know if they've been matched up against AlphaStar. This will help ensure that all games are played under the same conditions, as players may tend to react differently when they know they’re against an AI. A win or a loss against AlphaStar will affect a player’s MMR (Matchmaking Rating) like any other game played on the ladder. "DeepMind is currently interested in assessing AlphaStar’s performance in matches where players use their usual mix of strategies," Blizzard said in its blog post. "Having AlphaStar play anonymously helps ensure that it is a controlled test, so that the experimental versions of the agent experience gameplay as close to a normal 1v1 ladder match as possible. It also helps ensure all games are played under the same conditions from match to match." Some people have appreciated the anonymous testing feature. A Hacker News user commented, “Of course the anonymous nature of the testing is interesting as well. Big contrast to OpenAI's public play test. I guess it will prevent people from learning to exploit the bot's weaknesses, as they won't know they are playing a bot at all. I hope they eventually do a public test without the anonymity so we can see how its strategies hold up under focused attack.” Others find it interesting to see what happens if players know they are playing against AlphaStar. https://twitter.com/hardmaru/status/1149104231967842304   AlphaStar will play in Starcraft’s three in-universe races (Terran, Zerg, or Protoss). Pairings on the ladder will be decided according to normal matchmaking rules, which depend on how many players are online while AlphaStar is playing. It will not be learning from the games it plays on the ladder, having been trained from human replays and self-play. The Alphastar will also use a camera interface and more restricted APMs. Per the blog post, “AlphaStar has built-in restrictions, which cap its effective actions per minute and per second. These caps, including the agents’ peak APM, are more restrictive than DeepMind’s demonstration matches back in January, and have been applied in consultation with pro players.” https://twitter.com/Eric_Wallace_/status/1148999440121749504 https://twitter.com/Liquid_MaNa/status/1148992401157054464   DeepMind will be benchmarking the performance of a number of experimental versions of AlphaStar to enable DeepMind to gather a broad set of results during the testing period. DeepMind will use a player’s replays and the game data (skill level, MMR, the map played, race played, time/date played, and game duration) to assess and describe the performance of the AlphaStar system. However, Deepmind will remove identifying details from the replays including usernames, user IDs and chat histories. Other identifying details will be removed to the extent that they can be without compromising the research DeepMind is pursuing. For now, AlphaStar agents will play only in Europe. The research results will be released in a peer-reviewed scientific paper along with replays of AlphaStar’s matches. Google DeepMind’s AI AlphaStar beats StarCraft II pros TLO and MaNa; wins 10-1 against the gamers Deepmind’s AlphaZero shows unprecedented growth in AI, masters 3 different games Deepmind’s AlphaFold is successful in predicting the 3D structure of a protein making major inroads for AI use in healthcare

Minecraft Server Java Edition has added a new (spigot) plugin which changes climate mechanics in the game. This plugin adds the concept of greenhouse gases (CO2) in the game world's atmosphere. According to a recent report, only 45 percent of Americans think that global warming will pose a serious threat in their lifetime, and just 43 percent say they worry about climate change. These figures are alarming because serious damages due to Global Warming are imminent. As such, games and other forms of entertainment services are a good approach to change these ideologies and make people aware of how serious the threat of Global warming is. Minecraft’s approach could not only spread awareness but also has the potential to develop personal accountability and healthy personal habits. What does the Minecraft plugin do? The Furnaces within the game emit CO2 when players smelt items. Every furnace burn causes a Contribution to emissions with an associated numerical value. The trees are designed to instantly absorb CO2 when they grow from a sapling. Every tree growth causes a Reduction from emissions with an associated numerical value. As CO2 levels rise, the global temperature of the game environment will also rise because of the Greenhouse Effect. The global temperature is a function of the net global carbon score. As the global temperature rises, the frequency and severity of negative climate damages increases. Players need to design a default model that doesn't quickly destroy worlds. Players are best off when they cooperate and agree to reduce their emissions. What are its features? Scoreboard and Economy Integration Carbon Scorecard, where each player can see their latest carbon footprint trends via command line. Custom Models, with configurable thresholds, probabilities, and distributions. Load data on startup, queue DB changes to be done asynchronously and at intervals, and empty queue on shutdown. How was the response? The new Minecraft plugin received mixed reviews. Some considered it a great idea for teaching in schools. “Global warming is such an abstract problem and if you can tie it to individual's behaviors inside a (small) simulated world, it can be a very powerful teaching tool.” Others were not as happy. People feel that Minecraft lacks the basic principle of conservation of matter and energy, which is where you start with ecology. As a hacker news user pointed out, “I wish there was a game which would get the physical foundations right so that the ecology could be put on as a topping. What I imagine is something like a Civilization, where each map cell would be like 1 km2 and you could define what industries would be in that cell (perhaps even design the content of each cell). Each cell would contain a little piece of civilization and/or nature. These cells would then exchange different materials with each other, according to conservation laws.” While there will always be room for improvement, we think Minecraft is setting the tone for what could become a movement within the gaming community to bring critical abstract ideas to players in a non-threatening and thought-provoking way. The gaming industry has always lead technological innovations that then further cascade to other industries. We are excited to see this new real-world dimension becoming a focus area for Minecraft. You can read more about the Minecraft Plugin on its Github repo. Building a portable Minecraft server for LAN parties in the park Minecraft: The Programmer’s Sandbox Minecraft Modding Experiences and Starter Advice