Evorus: A Crowd-powered Conversational Assistant Built to Automate Itself Over Time

Crowd-powered conversational assistants have been shown to be more robust than automated systems, but do so at the cost of higher response latency and monetary costs. A promising direction is to combine the two approaches for high quality, low latency, and low cost solutions. In this paper, we introduce Evorus, a crowd-powered conversational assistant built to automate itself over time by (i) allowing new chatbots to be easily integrated to automate more scenarios, (ii) reusing prior crowd answers, and (iii) learning to automatically approve response candidates. Our 5-month-long deployment with 80 participants and 281 conversations shows that Evorus can automate itself without compromising conversation quality. Crowd-AI architectures have long been proposed as a way to reduce cost and latency for crowd-powered systems; Evorus demonstrates how automation can be introduced successfully in a deployed system. Its architecture allows future researchers to make further innovation on the underlying automated components in the context of a deployed open domain dialog system.Comment: 10 pages. To appear in the Proceedings of the Conference on Human Factors in Computing Systems 2018 (CHI'18

Crowd-powered systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 217-237).Crowd-powered systems combine computation with human intelligence, drawn from large groups of people connecting and coordinating online. These hybrid systems enable applications and experiences that neither crowds nor computation could support alone. Unfortunately, crowd work is error-prone and slow, making it difficult to incorporate crowds as first-order building blocks in software systems. I introduce computational techniques that decompose complex tasks into simpler, verifiable steps to improve quality, and optimize work to return results in seconds. These techniques develop crowdsourcing as a platform so that it is reliable and responsive enough to be used in interactive systems. This thesis develops these ideas through a series of crowd-powered systems. The first, Soylent, is a word processor that uses paid micro-contributions to aid writing tasks such as text shortening and proofreading. Using Soylent is like having access to an entire editorial staff as you write. The second system, Adrenaline, is a camera that uses crowds to help amateur photographers capture the exact right moment for a photo. It finds the best smile and catches subjects in mid-air jumps, all in realtime. Moving beyond generic knowledge and paid crowds, I introduce techniques to motivate a social network that has specific expertise, and techniques to data mine crowd activity traces in support of a large number of uncommon user goals. These systems point to a future where social and crowd intelligence are central elements of interaction, software, and computation.by Michael Scott Bernstein.Ph.D

Crowd-powered positive psychological interventions

Recent advances in crowdsourcing have led to new forms of assistive technologies, commonly referred to as crowd-powered devices. To best serve the user, these technologies crowdsource human intelligence as needed, when automated methods alone are insufficient. In this paper, we provide an overview of how these systems work and how they can be used to enhance technological interventions for positive psychology. As a specific example, we describe previous work that crowdsources positive reappraisals, providing users timely and personalized suggestions for ways to reconstrue stressful thoughts and situations. We then describe how this approach could be extended for use with other positive psychological interventions. Finally, we outline future directions for crowd-powered positive psychological interventions

Crowds in two seconds: Enabling realtime crowd-powered interfaces

Interactive systems must respond to user input within seconds. Therefore, to create realtime crowd-powered interfaces, we need to dramatically lower crowd latency. In this paper, we introduce the use of synchronous crowds for on-demand, realtime crowdsourcing. With synchronous crowds, systems can dynamically adapt tasks by leveraging the fact that workers are present at the same time. We develop techniques that recruit synchronous crowds in two seconds and use them to execute complex search tasks in ten seconds. The first technique, the retainer model, pays workers a small wage to wait and respond quickly when asked. We offer empirically derived guidelines for a retainer system that is low-cost and produces on-demand crowds in two seconds. Our second technique, rapid refinement, observes early signs of agreement in synchronous crowds and dynamically narrows the search space to focus on promising directions. This approach produces results that, on average, are of more reliable quality and arrive faster than the fastest crowd member working alone. To explore benefits and limitations of these techniques for interaction, we present three applications: Adrenaline, a crowd-powered camera where workers quickly filter a short video down to the best single moment for a photo; and Puppeteer and A|B, which examine creative generation tasks, communication with workers, and low-latency voting

Deadline-aware fair scheduling for multi-tenant crowd-powered systems

Crowdsourcing has become an integral part of many systems and services that deliver high-quality results for complex tasks such as data linkage, schema matching, and content annotation. A standard function of such crowd-powered systems is to publish a batch of tasks on a crowdsourcing platform automatically and to collect the results once the workers complete them. Currently, these systems provide limited guarantees over the execution time, which is problematic for many applications. Timely completion may even be impossible to guarantee due to factors specific to the crowdsourcing platform, such as the availability of workers and concurrent tasks. In our previous work, we presented the architecture of a crowd-powered system that reshapes the interaction mechanism with the crowd. Specifically, we studied a push-crowdsourcing model whereby the workers receive tasks instead of selecting them from a portal. Based on this interaction model, we employed scheduling techniques similar to those found in distributed computing infrastructures to automate the task assignment process. In this work, we first devise a generic scheduling strategy that supports both fairness and deadline-awareness. Second, to complement the proof-of-concept experiments previously performed with the crowd, we present an extensive set of simulations meant to analyze the properties of the proposed scheduling algorithms in an environment with thousands of workers and tasks. Our experimental results show that, by accounting for human factors, micro-task scheduling can achieve fairness for best-effort batches and boosts production batches

Overview of crowd simulation in computer graphics

High-powered technology use computer graphics in education, entertainment, games, simulation, and virtual heritage applications has led it to become an important area of research. In simulation, according to Tecchia et al. (2002), it is important to create an interactive, complex, and realistic virtual world so that the user can have an immersive experience during navigation through the world. As the size and complexity of the environments in the virtual world increased, it becomes more necessary to populate them with peoples, and this is the reason why rendering the crowd in real-time is very crucial. Generally, crowd simulation consists of three important areas. They are realism of behavioral (Thompson and Marchant 1995), high-quality visualization (Dobbyn et al. 2005) and convergence of both areas. Realism of behavioral is mainly used for simple 2D visualizations because most of the attentions are concentrated on simulating the behaviors of the group. High quality visualization is regularly used for movie productions and computer games. It gives intention on producing more convincing visual rather than realism of behaviors. The convergences of both areas are mainly used for application like training systems. In order to make the training system more effective, the element of valid replication of the behaviors and high-quality visualization is added

Predicting the Quality of Short Narratives from Social Media

An important and difficult challenge in building computational models for narratives is the automatic evaluation of narrative quality. Quality evaluation connects narrative understanding and generation as generation systems need to evaluate their own products. To circumvent difficulties in acquiring annotations, we employ upvotes in social media as an approximate measure for story quality. We collected 54,484 answers from a crowd-powered question-and-answer website, Quora, and then used active learning to build a classifier that labeled 28,320 answers as stories. To predict the number of upvotes without the use of social network features, we create neural networks that model textual regions and the interdependence among regions, which serve as strong benchmarks for future research. To our best knowledge, this is the first large-scale study for automatic evaluation of narrative quality.Comment: 7 pages, 2 figures. Accepted at the 2017 IJCAI conferenc