Tractable Big Data and Big Models in Machine Learning

在几乎所有的科学和工程领域，我们收集的数据量正以前所未有的速度增长。我们产生的数据大小不再是从兆字节到千兆字节，而是从太字节到拍字节（甚至更大）。机器学习是我们用来理解这些不断增长的数据量（“大数据”）的关键工具之一，现在它被用来解决非常复杂的任务，通过将日益复杂的模型拟合到这些庞大的数据集（“大模型”）。机器学习用于日常技术，如电子邮件垃圾邮件过滤、产品推荐系统、广告排名系统、新的运动传感设备以及语音识别的最新改进。从物理学到生物学，从教育技术到人机交互，机器学习仍然有巨大的潜力来影响应用。

In nearly all fields of science and engineering, the amount of data we collect is growing at unprecedented rates. We no longer produce data sizes from megabytes to gigabytes, but rather from terabytes to petabytes (and beyond). Machine learning is one of the key tools we use to make sense of these ever-growing quantities of data ('big data'), and it is now being used to solve very complicated tasks by fitting increasingly-complicated models to these huge data sets ('big models'). Machine learning is used in everyday technologies like e-mail spam filtering, product recommendation systems, advertisement ranking systems, new motion sensing devices, and recent improvements in speech recognition. There remains a huge potential for machine learning to impact applications ranging from physics to biology and from education technology to human-computer interaction.