Abstract: 

As the digital frontier expands, the inclusion of Generative and Autonomous AI technologies is becoming an integral part of the next evolutionary step in games, simulations, and the emergent Metaverse. In this in-depth, hands-off tutorial, we delve into the transformative potential of these cutting-edge AI paradigms, focusing on their capacity to create virtual landscapes that are not just visually stunning but also highly interactive, intelligent, and perpetually evolving. Participants will be led through a balanced curriculum that covers both the theoretical foundations and the practical techniques, right from core algorithms to real-world implementation. Alongside this, we will also explore the ethical dimensions of deploying these technologies, discussing questions of data privacy, user agency, and ethical design. Specifically designed for game developers, simulation engineers, and anyone excited about the possibilities of the Metaverse, this session aims to offer a comprehensive toolkit. With a blend of lectures, live coding, and interactive discussions, attendees will leave with a robust understanding and actionable insights on how to harness the capabilities of Generative and Autonomous AI effectively in their future projects. This tutorial is more than just an educational session; it's a launchpad for the next wave of digital innovation in interactive virtual environments.

Session Outline;

Module 1: Introduction to Generative and Autonomous AI
- Learning Objectives: To understand the types and applications of Generative and Autonomous AI in virtual environments.
- Tools: PowerPoint slides, whiteboard
- Datasets: N/A
- Project/Exercise: Interactive discussion to define Generative and Autonomous AI.

Module 2: 3D Asset Generation with Neural Radiance Fields (NeRFs) and Diffusion Models
- Learning Objectives: To understand the principles and methods behind generating 3D assets with text-to-image diffusion models and NeRFs.
- Tools: Jupyter Notebooks, TensorFlow or PyTorch
- Datasets: Pre-captured 2D images for training 3D asset models
- Project/Exercise: Hands-on coding to generate 3D assets like furniture, characters, or artifacts using NeRFs.

Module 3: Autonomous Agents as NPCs in Games
- Learning Objectives: To learn how to implement intelligent, autonomous agents as Non-Playable Characters (NPCs) in game environments.
- Tools: Unity or Unreal Engine, AI frameworks like OpenAI Gym
- Datasets: Pre-designed game levels and AI behavior scripts
- Project/Exercise: Develop a game scenario featuring autonomous NPCs, demonstrating pathfinding, decision-making, and interaction with the player.

Module 4: Ethics and Future Outlook
- Learning Objectives: To understand the ethical landscape and anticipate the future applications of Generative and Autonomous AI and incorporating Responsible AI principles into design and usage of AI.
- Tools: PowerPoint slides, open discussion
- Datasets: N/A
- Project/Exercise: Group debate on ethical considerations and future potential.

- Gain a foundational understanding of Generative and Autonomous AI.
- Learn the technical aspects required for implementation.
- Acquire hands-on experience through project work.
- Understand the ethical implications and future prospects of using AI in virtual worlds.

Background Knowledge:

- Basic understanding of AI and machine learning concepts.
- Some coding experience, preferably in Python or C++.
- Familiarity with game development or simulation engineering is advantageous but not a strict requirement.

Bio: 

Matt White is a distinguished expert in artificial intelligence and business, Renowned for successfully deploying large-scale AI platforms across the telecom, gaming, media, and entertainment industries. With over two decades of experience, Matt has consistently demonstrated his ability to stay ahead of the curve in technological innovation, spearheading advancements in AI applications in diverse domains.