In the last years machine learning (ML) has moved from a academic endeavor to a pervasive technology adopted in almost every aspect of computing. ML-powered products are now embedded in our digital lives: from recommendations of what to watch, to divining our search intent, to powering virtual assistants in consumer and enterprise settings. Recent successes in applying ML in natural sciences revealed that ML can be used to tackle some of the hardest real-world problems humanity faces today. For these reasons ML has become central in the strategy of tech companies and has gathered even more attention from academia than ever before. Despite these successes, what we have witnessed so far is just the beginning. Right now the people training and using ML models are expert developers working within large organizations, but we believe the next wave of ML systems will allow a larger amount of people, potentially without coding skills, to perform the same tasks. These new ML systems will not require users to fully understand all the details of how models are trained and utilized for obtaining predictions. Declarative interfaces are well suited for this goal, by hiding complexity and favouring separation of interests, and can lead to increased productivity. We worked on such abstract interfaces by developing two declarative ML systems, Overton and Ludwig, that require users to declare only their data schema (names and types of inputs) and tasks rather then writing low level ML code. In this article we will describe how ML systems are currently structured, highlight important factors for their success and adoption, what are the issues current ML systems are facing and how the systems we developed addressed them. Finally we will talk about learnings from the development of ML systems throughout the years and how we believe the next generation of ML systems will look like.
A New Perspective on Machine Learning: How to do Perfect Supervised Learning
iFLYTEK Laboratory for Neural Computing and Machine Learning (iNCML)（科大讯飞神经计算与机器学习实验室（iNCML））；Department of Electrical Engineering and Computer Science York University（约克大学电气工程与计算机科学系）
Uncertainty Quantification for Data-Driven Machine Learning Models in Nuclear Engineering Applications: Where We Are and What Do We Need?
North Carolina State University (NCSU)（北卡罗来纳州立大学）；The South African Nuclear Energy Corporation SOC Ltd (Necsa)（南非核能源公司）；U.S. Nuclear Regulatory Commission (NRC)（美国核管理委员会）
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
Fraunhofer Heinrich Hertz Institute（弗劳恩霍夫研究所海因里希赫兹研究所）；Technische Universität Berlin（柏林工业大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Korea University（高丽大学）；Max Planck Institut für Informatik（马克斯·普朗克计算机科学研究所）
Actionable Interpretation of Machine Learning Models for Sequential Data: Dementia-related Agitation Use Case
University of Virginia（弗吉尼亚大学）；North Carolina A&T State University（北卡罗来纳农工州立大学）；Virginia Tech Carilion School of Medicine（弗吉尼亚理工卡里利安医学院）；The George Washington University（乔治·华盛顿大学）
University College London（伦敦大学学院）；University of Cambridge（剑桥大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Istituto Italiano di Tecnologia（意大利理工学院）；University of Oxford（牛津大学）；Shanghai Jiao Tong University（上海交通大学）；ETH Zürich（苏黎世联邦理工学院）
