一句话：探讨 ML for Systems 是否接近类似 AlexNet 的拐点，并提出领域分类框架与新兴问题。
这篇演讲摘要讨论机器学习在计算机系统领域（ML for Systems）的发展趋势。作者指出，自然语言处理、计算机视觉和软件工程等领域都已经历从 ML 作为诸多方法之一到成为主导方法的拐点，而计算机系统仍是显著例外。报告将分析 ML for Systems 的新兴趋势，比较系统领域与其他领域的差异，并设想系统领域的“AlexNet 时刻”可能呈现何种形态。作者还描述一个对该领域工作进行分类的框架，并讨论新兴研究问题与机会。摘要未给出具体实证结果。
A wide range of research areas - from natural language processing to computer vision and software engineering - have been (or are being) revolutionized by machine learning and artificial intelligence. Each of these areas went through an inflection point where they transitioned from ML as one of many approaches to ML becoming a predominant approach of the field. No example symbolizes this better than the AlexNet paper from 2012, which fundamentally transformed the field of computer vision. Computer systems remain a notable exception. In this talk, I will discuss emerging trends in the ML for Systems domain, how systems differ from these other areas, and what an ''AlexNet Moment'' for systems might look like. Along the way, I will describe a framework for categorizing work in the field and discuss emerging research problems and opportunities.