IBM at AAAI 2024
- Vancouver, BC, Canada
About
IBM is proud to be taking part in AAAI 2024. We invite all attendees to visit us during the event.
We look forward to meeting you at the event and telling you more about our latest work and career opportunities at IBM Research. Our team will be presenting a series of workshops, papers and demos related to a broad range of AI topics such as foundation models, trustworthy AI, natural language processing and understanding, knowledge and reasoning, AI automation, human-centered AI, and federated learning.
Presentation times of conference workshops, demos, papers, and tutorials can be found at the agenda section at the bottom of this page. Note: All times are displayed in your local time.
Why attend
Join conversations on machine learning best practices, attend education tutorials, and participate in workshops. Meet with IBM recruiting and hiring managers about future job opportunities or 2024 summer internships.
Career opportunities
Visit us at the IBM Booth to meet with IBM researchers and recruiters to speak about future job opportunities or 2024 summer internships.
Featured positions to learn more about at AAAI 2024:
Full Time Positions:
- Technical Product Manager, Incubation (Cambridge)
- Postdoctoral Researcher in Computational Chemistry (Riyadh)
2024 Internships:
- Hybrid Cloud Research Tokyo Student Intern (Japan)
- AD-DTF Research Tokyo Student Intern (Japan）
- AI Research Tokyo Student Intern (Japan)
- AI for Drug Discovery- MSc & PhD Summer internship 2024 (Haifa)
- Math Decision Making- MSc and PHD-Summer internship 2024 (Haifa)
- AI Applied Science- MSc/PhD Summer internship 2024 (Haifa)
- AI NLP Language & Conversation- Student Position (Haifa)
- AI Language Researcher- MSc/PhD Summer internship 2024 (Haifa)
- LLM-NLP Intelligent Automation-MSc&PhD Summer intern 2024 (Haifa)
- AI Intelligent Automation-MSc&PhD Summer internship 2024 (Haifa)
- Cybersecurity - MSc and PhD Summer Internship 2024 (Haifa)
- AI Applied Science- MSc/PhD Student Position (Haifa)
Sign up to be notified of future openings by joining our Talent Network.
Agenda
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.
XGXiaojie GuoThe 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.
