Samsung SDS, Paper Published for 3 Consecutive Years at Global AI Conference ‘NeurIPS’
[Asia Economy Reporter Yuri Choi] Samsung SDS announced on the 27th that it has had combinatorial optimization papers published for three consecutive years at the world-renowned artificial intelligence (AI) conference, the Conference on Neural Information Processing Systems (NeurIPS).
NeurIPS is the largest and most prestigious conference in the fields of AI and machine learning worldwide. The acceptance rate for papers in this field is only about 25%, reflecting the rigorous standards and the high technical impact of the research outcomes produced.
The paper published this time is a study on "Combinatorial Optimization Using Reinforcement Learning." Samsung SDS proposed an optimization method that utilizes reinforcement learning technology, where AI autonomously finds the optimal solution, to quickly and efficiently solve various problems that may arise in industrial sites such as cloud computing (GPU) resource allocation and process optimization in manufacturing and logistics.
Samsung SDS applied this technology to participate in the "NeurIPS Machine Learning Competition for Route Optimization." As a result, it ranked third among 54 teams from global companies and universities. It also received the Jury's Special Award, given to the team that solved problems most innovatively and effectively using AI technology.
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Including the paper accepted at NeurIPS this year, Samsung SDS published a total of 11 papers at major global conferences. A representative example is a paper on optimizing language models using meta-learning, published at the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), a leading global natural language processing conference. Meta-learning is an AI system that learns the learning process itself. Kwon Young-jun, Head of Samsung SDS Research Institute (Vice President), said, "We will continue to actively invest in AI research and development to advance technology and expand its application in industrial sites."
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