TY - GEN
T1 - Generative AI Day
AU - Tang, Jie
AU - Dong, Yuxiao
AU - Vazirgiannis, Michalis
N1 - Publisher Copyright:
© 2024 Copyright held by the owner/author(s).
PY - 2024/8/24
Y1 - 2024/8/24
N2 - The Generative AI (AIGC) Day at KDD'24 is a dedicated full-day event for generative AI at KDD. This is an opportunity to bring together researchers, practitioners, and startups to share the insights about the cutting-edge advancements and to discuss the potential societal impacts of LLMs and AIGC. It is exciting that this year, we have invited speakers from both industry (e.g., Amazon, Zhipu AI) and academia (e.g., USC, UCLA). The topics cover various perspectives of generative AI including foundation models, streaming LLMs, LLM training and inference. As demonstrated, data plays a crucial role in developing cutting-edge generative AI models. For example, the Gemini Team has found that "data quality is an important factor for highly-performing models...''. To date, there is still significant room to define design principles and develop methods for improved data collection, selection, and synthetic data generation for the pre-training and alignment of language, vision, and multi-modal models. Therefore, the Day will invite the speakers and KDD audience to discuss the challenges and opportunities for data mining researchers in the era of generative AI.
AB - The Generative AI (AIGC) Day at KDD'24 is a dedicated full-day event for generative AI at KDD. This is an opportunity to bring together researchers, practitioners, and startups to share the insights about the cutting-edge advancements and to discuss the potential societal impacts of LLMs and AIGC. It is exciting that this year, we have invited speakers from both industry (e.g., Amazon, Zhipu AI) and academia (e.g., USC, UCLA). The topics cover various perspectives of generative AI including foundation models, streaming LLMs, LLM training and inference. As demonstrated, data plays a crucial role in developing cutting-edge generative AI models. For example, the Gemini Team has found that "data quality is an important factor for highly-performing models...''. To date, there is still significant room to define design principles and develop methods for improved data collection, selection, and synthetic data generation for the pre-training and alignment of language, vision, and multi-modal models. Therefore, the Day will invite the speakers and KDD audience to discuss the challenges and opportunities for data mining researchers in the era of generative AI.
KW - chatglm
KW - foundation model
KW - generative ai
KW - large language model
U2 - 10.1145/3637528.3673872
DO - 10.1145/3637528.3673872
M3 - Conference contribution
AN - SCOPUS:85203714375
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 6699
EP - 6700
BT - KDD 2024 - Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
T2 - 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2024
Y2 - 25 August 2024 through 29 August 2024
ER -