Generative AI and Games

Intriguing approach .

The Generative AI Revolution in Games

Excerpt from James Gwertzman and Jack Soslow   in a16z.com,  Andreessen Horowitz

AI, machine & deep learning  gaming, social, and new media  Generative AI  machine learning

To understand how radically gaming is about to be transformed by Generative AI, look no further than this recent Twitter post by @emmanuel_2m. In this post he explores using Stable Diffusion + Dreambooth, popular 2D Generative AI models, to generate images of potions for a hypothetical game.

What’s transformative about this work is not just that it saves time and money while also delivering quality – thus smashing the classic “you can only have two of cost, quality, or speed” triangle. Artists are now creating high-quality images in a matter of hours that would otherwise take weeks to generate by hand. What’s truly transformative is that:

This creative power is now available to anyone who can learn a few simple tools.

These tools can create an endless number of variations in a highly iterative way.

Once trained, the process is real-time – results are available near instantaneously.

There hasn’t been a technology this revolutionary for gaming since real-time 3D. Spend any time at all talking to game creators, and the sense of excitement and wonder is palpable. So where is this technology going? And how will it transform gaming? First, though, let’s review what is Generative AI?

Generative AI is a category of machine learning where computers can generate original new content in response to prompts from the user. Today text and images are the most mature applications of this technology, but there is work underway in virtually every creative domain, from animation, to sound effects, to music, to even creating virtual characters with fully fleshed out personalities.

AI is nothing new in games, of course. Even early games, like Atari’s Pong, had computer-controlled opponents to challenge the player. These virtual foes, however, were not running AI as we know it today. They were simply scripted procedures crafted by game designers. They simulated an artificially intelligent opponent, but they couldn’t learn, and they were only as good as the programmers who built them.

What’s different now is the amount of computing power available, thanks to faster microprocessors and the cloud. With this power, it’s possible to build large neural networks that can identify patterns and representations in highly complex domains.

This blog post has two parts:

Part I consists of our observations and predictions for the field of Generative AI for games. ... '