2014/09/16 by Patrice Y. Simard, Patrice Simard, David Chickering +20 · 15 citations
Computer Science · Decision Sciences · Engineering · #Advanced Bandit Algorithms Research #Architecture #Artificial Intelligence (cs.AI) #Artificial intelligence #Computer science #Construct (python library) #Data Stream Mining Techniques #Engineering #FOS: Computer and information sciences #Human–computer interaction #Information Retrieval (cs.IR) #Interface (matter) #Machine Learning and Algorithms #Machine learning #Programming language #Scale (ratio) #Task (project management) #cs.AI #cs.IR
paper · pdf · doi:10.48550/arxiv.1409.4814
published in arXiv (Cornell University) (Cornell University)
arxiv created 2014/09/16 · openalex publication_date 2014/09/16 · arxiv updated 2014/09/18 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
Quick interaction between a human teacher and a learning machine presents numerous benefits and challenges when working with web-scale data. The human teacher guides the machine towards accomplishing the task of interest. The learning machine leverages big data to find examples that maximize the training value of its interaction with the teacher. When the teacher is restricted to labeling examples selected by the machine, this problem is an instance of active learning. When the teacher can provide additional information to the machine (e.g., suggestions on what examples or predictive features should be used) as the learning task progresses, then the problem becomes one of interactive learning. To accommodate the two-way communication channel needed for efficient interactive learning, the teacher and the machine need an environment that supports an interaction language. The machine can access, process, and summarize more examples than the teacher can see in a lifetime. Based on the machine's output, the teacher can revise the definition of the task or make it more precise. Both the teacher and the machine continuously learn and benefit from the interaction. We have built a platform to (1) produce valuable and deployable models and (2) support research on both the machine learning and user interface challenges of the interactive learning problem. The platform relies on a dedicated, low-latency, distributed, in-memory architecture that allows us to construct web-scale learning machines with quick interaction speed. The purpose of this paper is to describe this architecture and demonstrate how it supports our research efforts. Preliminary results are presented as illustrations of the architecture but are not the primary focus of the paper.