Google Genie — a generative AI model that creates fully interactive worlds from images | AI in business #123

Imagine a futuristic scenario in which an advanced artificial intelligence system brings to life any image, photo, or even a handwritten sketch, transforming it into a fully playable, interactive virtual reality. Amazing, right? And yet the technology already exists. It’s called Google Genie, and it’s a breakthrough AI model that could change the face of the gaming industry, AI system training, and even robotics. Want to know the details of this sensational innovation? Read on.

What is Google Genie?

Google Genie (https://sites.google.com/view/genie-2024/) is a foundational world model developed by DeepMind. It is a generative AI model trained on over 30,000 hours of publicly available 2D platformer video game footage. Its key feature is the ability to generate fully interactive, playable environments directly from single images, photos and even hand-drawn sketches.

Source: Genie: Generative Interactive Environments (https://arxiv.org/abs/2402.15391)

How is this possible? Genie uses an unsupervised learning technique in the process of acquiring the ability to precisely control the environment based solely on video footage. No human action tagging is required. Using a special action coding module, it captures subtle changes between successive video frames and maps them to internal representations of motion, such as jumping or turning left. The dynamics model then generates the next frame in the sequence based on the coded actions.

As a result, Genie can create fully controllable, interactive game environments from any visual data. Each player movement generates a new, unique frame in real time, creating a smooth, playable session. This is a really big innovation that allows us to create entire interactive worlds from images or text.

Why is Genie innovative?

The Genie’s innovation lies in combining several key elements in a single model:

• generative video models, such as Phenaki (https://phenaki.video/), TECO (https://wilson1yan.github.io/teco/) or maskvit (https://arxiv.org/abs/2206.11894), which can predict future frames of a sequence based on input frames and text, but do not offer active control capabilities,
• world models that focus on predicting future environmental states based on an agent’s actions, but requiring data provided by humans,
• unsupervised learning, which allows Genie to learn both environmental dynamics and action space from raw video data alone, without human action labels.

Although each of these areas has been explored before, Genie is the first model to combine them to learn controllable environments directly from video footage. This unprecedented approach to teaching models without human supervision is a key innovation of Genie. It opens the door to using the vast amount of video available on the Internet as a training source for AI models, and breaks down the barriers associated with the limited availability of labeled data.

The combination of generative video models, world models and unsupervised learning in a single solution represents a fundamental advance in the development of artificial intelligence. Genie demonstrates that advanced AI systems can learn complex behaviors and environments directly from unstructured data, without manual tagging. This is a key step on the road to achieving true Artificial General Intelligence (AGI).

Source: Google Genie (https://sites.google.com/view/genie-2024/)

Potential applications of Google Genie

Google Genie’s capabilities go far beyond generating video games. This pioneering AI model can find applications in many fields:

• tool for animators – just upload an image, sketch or short text description and Genie will generate a consistent animation,
• unlimited training resource for AI agents – with its ability to generalize to entirely new domains, Genie offers an infinite pool of challenges on which future AI systems can learn. The lack of diverse training environments has so far been one of the key barriers to the development of generic AI agents,
• physical simulations for robotics – research has shown that Genie is able not only to control virtual robots, but also to realize the physical properties of deformable objects. This could have huge implications for the development of robotics and physical simulations,
• applications in the creative industries – Genie can facilitate the creation of interactive art installations, virtual exhibitions or films. Simply upload a sketch and the model will generate a fully controllable 3D world, ready for exploration.

However, the potential challenges and limitations of this technology should not be overlooked. At the current stage of development, Genie works best in narrow domains such as 2D platform games. Scaling up to more complex 3D environments will require additional research and optimization. In addition, there is a risk that this technology could be abused to create harmful or dangerous content. It is therefore critical to develop a robust ethical and legal framework to govern the development and use of such AI models.

Source: Google Genie (https://sites.google.com/view/genie-2024/)

Summary

By enabling the creation of fully interactive environments directly from visual data, without the need to manually tag actions,, Google Genie represents a true breakthrough in generative artificial intelligence. This fundamental world model gives the power to express imagery in the form of playable virtual realities that can be explored and controlled by a human or AI agent.

Genie’s potential is enormous – from tools for game developers, to an unlimited source of training data for AI, to physical simulations for robotics. It’s also an important step on the road to AGI. As models like Genie continue to evolve, the boundary between the real and virtual worlds is becoming more fluid.

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