Data scientist & software engineer | numerical modeling, machine learning, communication, Python, C++
🔍 Just attended an enlightening panel at Nvidia's GTC24, featuring insights from Kaggle Grandmasters and NVIDIA's leading data scientists, including David Austin, Jiwei Liu, Ph.D., Kazuki Onodera, @Chris Deotte, and Laura Leal-Taixé. The panelists discussed the evolving landscapes of challenges involving AI and the frontiers of Large Language Models (LLMs), providing various perspectives on how these technologies are shaping the future of data science and machine learning. A notable point for me was the increasing importance of synthetic data generation in Kaggle competitions. Moreover, the panelists also pointed out the growing usefulness of Retrieval-Augmented Generation (RAG) technology, which enhances LLMs by integrating external knowledge sources. The discussion also painted Kaggle competitions as not only a platform for improving machine learning skills but also as a significant career stepping stone, offering exposure to real-world problems and the latest technologies, such as llama 2 and Mixtral (open-source LLMs). The emphasis on RAG and LLMs highlights their potential in addressing complex problems. This session has inspired me to explore these technologies further and consider participating in Kaggle competitions 🚀 #NVIDIA #GTC24 #AI #MachineLearning #DataScience #Kaggle #LLM #RAGTechnology #DataScience
