Another month, another revolutionary generative AI development that will apparently fundamentally alter how an entire industry operates. This time tech giant Microsoft has created a "gameplay ideation" tool, Muse, which it calls the world's first Wham, or World and Human Action Model. Microsoft claims that Muse will speed up the lengthy and expensive process of game development by allowing designers to play around with AI-generated gameplay videos to see what works. Muse is trained on gameplay data from UK studio Ninja Theory's game Bleeding Edge. It has absorbed tens of thousands of hours of people's real gameplay, both footage and controller inputs.

An artificial intelligence model from Microsoft can recreate realistic video game footage that the company says could help designers make games, but experts are unconvinced that the tool will be useful for most game developers. Neural networks that can produce coherent and accurate footage from video games are not new. A recent Google-created AI generated a fully playable version of the classic computer game Doom without access to the underlying game engine. The original Doom, however, was released in 1993; more modern games are far more complex, with sophisticated physics and computationally intensive graphics, which have proved trickier for AIs to faithfully recreate. Google creates self-replicating life from digital'primordial soup' Now, Katja Hofmann at Microsoft Research and her colleagues have developed an AI model called Muse, which can recreate full sequences of the multiplayer online battle game Bleeding Edge. These sequences appear to obey the game's underlying physics and keep players and in-game objects consistent over time, which implies that the model has grasped a deep understanding of the game, says Hofmann.

AI algorithms capable of generating simulated environments -- represent one forefront of machine learning. Today, Microsoft published new research in the journal Nature detailing Muse, a model capable of generating game visuals and controller inputs. Unexpectedly, it was born out of a training set Microsoft built from Bleeding Edge. If, like me, you had completely erased that game from your memory (or never knew it existed in the first place), Bleeding Edge is a 4 vs. 4 brawler developed by Ninja Theory, the studio better known for its work on the Hellblade series. Ninja Theory stopped updating Bleeding Edge less than a year after release, but Microsoft included a clause in the game's EULA that gave it permission to record games people played online.

First up, I want to remind everyone – deep learning has really only been around as an applied method since 2012. So we haven't even had five years to use this stuff in medicine, and us medical folks typically lag behind a bit. With that perspective some of these results are even more incredible, but we should acknowledge that this is just the beginning. I'm going to review each paper I think is evidence of breakthrough medical automation, or that adds something useful to the conversation. I'll describe the research, but spend time discussing a few key elements: The task – is it a clinical task?

Now it's time for us to see how deep learning really works! In case you missed the previous part and is now wondering how deep learning has anything to do with you, go check it out! In this part, we'll show you all the basic concepts you need to get started with deep learning. Machine learning problems are typically where you want a computer to answer some questions without being explicitly programmed. For example, the question can be something like "What's the price of my 1800 sqft apartment in Seattle?", or "Is this news article telling the truth?"

Deep learning is a branch of machine learning that has shown incredible results on very difficult tasks like recognizing objects from an image, understanding speech and languages, and of course, playing board games. A bunch of smartest people have been working on it for decades, and it's absolutely state-of-the-art. Since you've clicked into a dummy's guide, chances are that you are a curious dummy. So first of all, let me answer a few questions I know a curious dummy might ask. It's software programs trying to mimic the human brain. The way it forms, the way it learns and the way it responds.

As I'm typing this sentence, a beautiful program is running on my mac in the background. My model is learning to do something we never imagined a computer could do. It's not doing well enough yet, but it's getting better every day. Welcome to the 3rd part of this article! In case you haven't seen part I or part II yet, In this final piece, I'm going to walk you through a real example.