报告题目：Retrospection of Practical AI in Product
报告专家：Zhe Wang, Microsoft
报告时间：6月3日周二下午14:30-15:30
报告地点：人工智能研究院412会议室
报告人简介：Zhe Wang received the Ph.D. degree in electrical engineering from Michigan State University, in 2017. He is currently a Software Engineer with Microsoft. His research interests lie in scalable machine learning, data visualization, and computational brain analysis.
报告摘要：The presentation reflects on practical AI implementation in products, emphasizing the balance between needs, resources, and costs. It highlights cases like the Netflix Prize, where the winning model wasn't deployed due to excessive costs, and financial institutions using hybrid models for robustness and interpretability. It also discusses applications of N-gram models in handwriting and speech recognition, and the evolution towards large language models (LLMs). The key takeaway is that while advanced models offer improvements, they must be weighed against deployment costs and practicality. Businesses often favor simpler, cost-effective solutions that meet their needs without excessive resource consumption.
