IBM at AAAI 2024

The field of AI-generated content has experienced notable advancements recently, thanks to large language models and diffusion models that are capable of generating text and images. These developments have broadened applications across various domains, including text, image, video, and 3D object generation. Considering the increasing attention garnered by powerful generative models like ChatGPT for text and diffusion models for image synthesis, it is necessary for the AAAI community to fully explore these developments. This tutorial seeks to foster a deeper understanding of the field among conference attendees. Our tutorial will provide a comprehensive overview of AI-generated content, covering its foundations, frontiers, applications, and societal implications. It will cover the basics of large language models and diffusion models, as well as recent research and applications in this area. We will also discuss the societal concerns surrounding AI-generated content, including AI ethics and safety. By the end of the tutorial, attendees will have a better understanding of the current state of the field and the opportunities and challenges it presents. Our tutorial will be useful for researchers and practitioners interested in the application of AI-generated content to various domains. Attendees will gain insights into the latest techniques and tools for generating high-quality content and learn about the potential benefits and risks associated with this technology.

The overarching goal of this tutorial is twofold: The first aim is to conduct a comprehensive assessment of the latest advancements in the gradient-free learning paradigm, also referred to as zeroth-order machine learning (ZO-ML). This involves an exploration of the theoretical and methodological foundations that support ZO-ML. The second goal is to illustrate the effective integration of ZO-ML techniques with emerging ML/AI applications. This step aims to bridge the theoretical and practical aspects of ZO-ML, demonstrating its potential to overcome design limitations in current foundation model (FM)-oriented applications.