Seminars

Feb
21
Fri
Igor Molybog (University of Hawaii at Manoa) – Development and deployment of large-scale AI systems and the tasks they still struggle with @ Hackerman Hall B17
Feb 21 @ 12:00 pm – 1:15 pm

Abstract

In this talk, Igor will share the latest findings from his research group on the development, evaluation, and deployment of large language models (LLMs) and their multi-modal extensions. We will outline the end-to-end pipeline, with a particular focus on the challenges and costs associated with data generation in the later stages of training. Additionally, we will explore strategies for integrating multiple models into inference systems to enhance efficiency. Finally, we will highlight key tasks where even the most advanced multi-modal models face difficulties and discuss approaches that yield significantly better performance.

Bio

Igor is an Assistant Professor at the University of Hawai’i at Manoa. In 2022, I graduated with a Ph.D. in Engineering from UC Berkeley and worked as a research engineer at Google DeepMind and Meta AI. He specializes in large language models (LLM), focusing on the efficient development and robust evaluation of computer systems that automate economically viable yet tedious tasks typically requiring human intervention. His immediate areas of interest include:

  • Expanding AI’s Impact: Identifying novel use cases for automation with LLM and novel tasks for increasing professionals’ productivity. Developing frameworks for robust evaluation of AI agents. Sourcing diverse data to address unique challenges in emerging applications.
  • Multimodal Modeling: Integrating video and other sensory modalities into AI reasoning to advance robotics and other cyber-physical systems applications. Enhancing large language models’ capabilities in computer programming and mathematical problem-solving.
  • Core Machine Learning and Scaling: Addressing efficiency challenges and fundamental obstacles to expand the computational resources available to AI systems. Optimizing the design of modeling experiments and developing predictable training processes.

Center for Language and Speech Processing