Scaling Instructable Agents: How Google DeepMind is Creating AI for Virtual Environments

TLDRGoogle DeepMind has developed a generalist AI agent, called SEMA, that can follow natural language instructions to complete tasks in various video game settings. This scalable instructable agent aims to generalize across different domains, paving the way for AI agents that can perform human-like actions in simulated 3D environments. By training on a broad distribution of data from visually complex and semantically rich environments, AI research is making significant progress towards developing embodied AI.

Key insights

🎮Google DeepMind has developed SEMA, a generalist AI agent for 3D virtual environments.

🌍SEMA can follow natural language instructions to complete tasks in a variety of video game settings.

💡The goal is to develop an instructable agent that can accomplish anything a human can do in any simulated 3D environment.

📈Training AI agents on diverse high-quality data from visually complex and semantically rich environments is critical to making progress in general AI.

🤖SEMA's ability to ground language in behavior and execute instructions at scale is a core challenge for developing embodied AI.

Q&A

What is SEMA?

—SEMA is a generalist AI agent developed by Google DeepMind that can follow natural language instructions to complete tasks in different video game settings.

What is the goal of SEMA?

—The goal is to develop an instructable agent that can accomplish anything a human can do in any simulated 3D environment.

Why is training AI agents on diverse data important?

—Training AI agents on diverse high-quality data from visually complex and semantically rich environments is critical to making progress in general AI.

What is the challenge in developing embodied AI?

—The challenge is to bridge the gap between language and behavior, connecting language instructions to specific actions that an AI agent can execute at scale.

What are the key insights in this video?

—The key insights are: 1) Google DeepMind has developed SEMA, a generalist AI agent for 3D virtual environments, 2) SEMA can follow natural language instructions in various video game settings, 3) The goal is to develop an instructable agent that can accomplish human-like actions, 4) Training AI agents on diverse data is essential for progress in general AI, and 5) Bridging the gap between language and behavior is a core challenge in developing embodied AI.

Timestamped Summary

00:00Google DeepMind has developed SEMA, a generalist AI agent for 3D virtual environments.

02:59The goal is to develop an instructable agent that can accomplish anything a human can do in any simulated 3D environment.

06:13Training AI agents on diverse high-quality data from visually complex and semantically rich environments is critical to making progress in general AI.

09:00SEMA's ability to ground language in behavior and execute instructions at scale is a core challenge for developing embodied AI.

Browse more