Where Are My Intelligent Assistant's Mistakes? A Systematic Testing Approach

Intelligent assistants are handling increasingly critical tasks, but until now, end users have had no way to systematically assess where their assistants make mistakes. For some intelligent assistants, this is a serious problem: if the assistant is doing work that is important, such as assisting with qualitative research or monitoring an elderly parent’s safety, the user may pay a high cost for unnoticed mistakes. This paper addresses the problem with WYSIWYT/ML (What You See Is What You Test for Machine Learning), a human/computer partnership that enables end users to systematically test intelligent assistants. Our empirical evaluation shows that WYSIWYT/ML helped end users find assistants’ mistakes significantly more effectively than ad hoc testing. Not only did it allow users to assess an assistant’s work on an average of 117 predictions in only 10 minutes, it also scaled to a much larger data set, assessing an assistant’s work on 623 out of 1,448 predictions using only the users’ original 10 minutes’ testing effort

Integrating rich user feedback into intelligent user interfaces

The potential for machine learning systems to improve via a mutually beneficial exchange of information with users has yet to be explored in much detail. Previously, we found that users were willing to provide a generous amount of rich feedback to machine learning systems, and that the types of some of this rich feedback seem promising for assimilation by machine learning algorithms. Following up on those findings, we ran an experiment to assess the viability of incorporating real-time keyword-based feedback in initial training phases when data is limited. We found that rich feedback improved accuracy but an initial unstable period often caused large fluctuations in classifier behavior. Participants were able to give feedback by relying heavily on system communication in order to respond to changes. The results show that in order to benefit from the user’s knowledge, machine learning systems must be able to absorb keyword-based rich feedback in a graceful manner and provide clear explanations of their predictions

Gender and Interest Targeting for Sponsored Post Advertising at Tumblr

As one of the leading platforms for creative content, Tumblr offers advertisers a unique way of creating brand identity. Advertisers can tell their story through images, animation, text, music, video, and more, and promote that content by sponsoring it to appear as an advertisement in the streams of Tumblr users. In this paper we present a framework that enabled one of the key targeted advertising components for Tumblr, specifically gender and interest targeting. We describe the main challenges involved in development of the framework, which include creating the ground truth for training gender prediction models, as well as mapping Tumblr content to an interest taxonomy. For purposes of inferring user interests we propose a novel semi-supervised neural language model for categorization of Tumblr content (i.e., post tags and post keywords). The model was trained on a large-scale data set consisting of 6.8 billion user posts, with very limited amount of categorized keywords, and was shown to have superior performance over the bag-of-words model. We successfully deployed gender and interest targeting capability in Yahoo production systems, delivering inference for users that cover more than 90% of daily activities at Tumblr. Online performance results indicate advantages of the proposed approach, where we observed 20% lift in user engagement with sponsored posts as compared to untargeted campaigns.Comment: 10 pages, 9 figures, Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2015), Sydney, Australi