Speaker

Hyunwoo J Kim

Title

Efficient Deep 지니 카지노 Understanding Towards AGI

Abstract

지니 카지노 has become one of the most popular modalities that modern individuals consume and produce. However, developing AI systems that deeply understand 지니 카지노s is still a challenging goal due to the difficulty of annotations, the sheer volume of data, and the substantial computational burden required for training and inference of 지니 카지노 models. To address these problems, I introduce new strategies for pre-training and fine-tuning 지니 카지노 foundation models, including parameter-efficient fine-tuning (PEFT). Additionally, to deploy 지니 카지노 models to users, I present training-free cost-efficient inference techniques for 지니 카지노 transformers. To demonstrate the generalizability of 지니 카지노 foundation models, I highlight our recent work in '지니 카지노 Question Answering' which implicitly requires tackling various subtasks and achieving a deeper understanding of 지니 카지노s. Lastly, I discuss how 지니 카지노 QA and Multimodal QA systems can serve as stepping stones towards artificial general intelligence, and outline future research directions.

Bio

Hyunwoo J. Kim is an associate professor in the School of Computing (SoC) at Korea Advanced Institute of Science & Technology (KAIST). He is also affiliated faculty in the Kim Jaechul Graduate School of AI at KAIST. Prior to the position, he led his lab at Korea University (Mar. 2019 ~ Jan. 2025). Earlier in his career, he worked at Amazon Lab126 in Sunnyvale California. In 2017, he earned the Ph.D. in the Department of Computer Sciences at the University of Wisconsin-Madison (Ph.D. minor: Statistics) under the supervision of Dr. Vikas Singh. In 2013, he completed his internship in the Machine Learning Analytics Team at Amazon in Seattle, Washington.

Language

English