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GDC 2018: Microsoft Discusses WinML API for Games

Manufacturer: Microsoft

It's all fun and games until something something AI.

Microsoft announced the Windows Machine Learning (WinML) API about two weeks ago, but they did so in a sort-of abstract context. This week, alongside the 2018 Game Developers Conference, they are grounding it in a practical application: video games!

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Specifically, the API provides the mechanisms for game developers to run inference on the target machine. The training data that it runs against would be in the Open Neural Network Exchange (ONNX) format from Microsoft, Facebook, and Amazon. Like the initial announcement suggests, it can be used for any application, not just games, but… you know. If you want to get a technology off the ground, and it requires a high-end GPU, then video game enthusiasts are good lead users. When run in a DirectX application, WinML kernels are queued on the DirectX 12 compute queue.

We’ve discussed the concept before. When you’re rendering a video game, simulating an accurate scenario isn’t your goal – the goal is to look like you are. The direct way of looking like you’re doing something is to do it. The problem is that some effects are too slow (or, sometimes, too complicated) to correctly simulate. In these cases, it might be viable to make a deep-learning AI hallucinate a convincing result, even though no actual simulation took place.

Fluid dynamics, global illumination, and up-scaling are three examples.

Previously mentioned SIGGRAPH demo of fluid simulation without fluid simulation...
... just a trained AI hallucinating a scene based on input parameters.

Another place where AI could be useful is… well… AI. One way of making AI is to give it some set of data from the game environment, often including information that a player in its position would not be able to know, and having it run against a branching logic tree. Deep learning, on the other hand, can train itself on billions of examples of good and bad play, and make results based on input parameters. While the two methods do not sound that different, the difference between logic being designed (vs logic being assembled from an abstract good/bad dataset) someone abstracts the potential for assumptions and programmer error. Of course, it abstracts that potential for error into the training dataset, but that’s a whole other discussion.

The third area that AI could be useful is when you’re creating the game itself.

There’s a lot of grunt and grind work when developing a video game. Licensing prefab solutions (or commissioning someone to do a one-off asset for you) helps ease this burden, but that gets expensive in terms of both time and money. If some of those assets could be created by giving parameters to a deep-learning AI, then those are assets that you would not need to make, allowing you to focus on other assets and how they all fit together.

These are three of the use cases that Microsoft is aiming WinML at.

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Sure, these are smooth curves of large details, but the antialiasing pattern looks almost perfect.

For instance, Microsoft is pointing to an NVIDIA demo where they up-sample a photo of a car, once with bilinear filtering and once with a machine learning algorithm (although not WinML-based). The bilinear algorithm behaves exactly as someone who has used Photoshop would expect. The machine learning algorithm, however, was able to identify the objects that the image intended to represent, and it drew the edges that it thought made sense.

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Like their DirectX Raytracing (DXR) announcement, Microsoft plans to have PIX support WinML “on Day 1”. As for partners? They are currently working with Unity Technologies to provide WinML support in Unity’s ML-Agents plug-in. That’s all the game industry partners they have announced at the moment, though. It’ll be interesting to see who jumps in and who doesn’t over the next couple of years.


March 21, 2018 | 02:53 PM - Posted by Marcelo (not verified)

It seems like IA will do the massive part of all areas, and humans will be focused in more context/comum sense dependent parts.

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