12,790 research outputs found
A U.S. Research Roadmap for Human Computation
The Web has made it possible to harness human cognition en masse to achieve
new capabilities. Some of these successes are well known; for example Wikipedia
has become the go-to place for basic information on all things; Duolingo
engages millions of people in real-life translation of text, while
simultaneously teaching them to speak foreign languages; and fold.it has
enabled public-driven scientific discoveries by recasting complex biomedical
challenges into popular online puzzle games. These and other early successes
hint at the tremendous potential for future crowd-powered capabilities for the
benefit of health, education, science, and society. In the process, a new field
called Human Computation has emerged to better understand, replicate, and
improve upon these successes through scientific research. Human Computation
refers to the science that underlies online crowd-powered systems and was the
topic of a recent visioning activity in which a representative cross-section of
researchers, industry practitioners, visionaries, funding agency
representatives, and policy makers came together to understand what makes
crowd-powered systems successful. Teams of experts considered past, present,
and future human computation systems to explore which kinds of crowd-powered
systems have the greatest potential for societal impact and which kinds of
research will best enable the efficient development of new crowd-powered
systems to achieve this impact. This report summarize the products and findings
of those activities as well as the unconventional process and activities
employed by the workshop, which were informed by human computation research.Comment: 32 pages, 25 figures, Workshop report from the CRA-sponsored Human
Computation Roadmap Summit: P. Michelucci, L. Shanley, J. Dickinson, and H.
Hirsh, A U.S. Research Roadmap for Human Computation, Computing Community
Consortium Technical Report, 201
Collective Creativity: Where we are and where we might go
Creativity is individual, and it is social. The social aspects of creativity
have become of increasing interest as systems have emerged that mobilize large
numbers of people to engage in creative tasks. We examine research related to
collective intelligence and differentiate work on collective creativity from
other collective activities by analyzing systems with respect to the tasks that
are performed and the outputs that result. Three types of systems are
discussed: games, contests and networks. We conclude by suggesting how systems
that generate collective creativity can be improved and how new systems might
be constructed.Comment: Presented at Collective Intelligence conference, 2012
(arXiv:1204.2991
Beyond AMT: An Analysis of Crowd Work Platforms
While Amazon's Mechanical Turk (AMT) helped launch the paid crowd work
industry eight years ago, many new vendors now offer a range of alternative
models. Despite this, little crowd work research has explored other platforms.
Such near-exclusive focus risks letting AMT's particular vagaries and
limitations overly shape our understanding of crowd work and the research
questions and directions being pursued. To address this, we present a
cross-platform content analysis of seven crowd work platforms. We begin by
reviewing how AMT assumptions and limitations have influenced prior research.
Next, we formulate key criteria for characterizing and differentiating crowd
work platforms. Our analysis of platforms contrasts them with AMT, informing
both methodology of use and directions for future research. Our cross-platform
analysis represents the only such study by researchers for researchers,
intended to further enrich the diversity of research on crowd work and
accelerate progress
Collaborative Interactive Learning -- A clarification of terms and a differentiation from other research fields
The field of collaborative interactive learning (CIL) aims at developing and
investigating the technological foundations for a new generation of smart
systems that support humans in their everyday life. While the concept of CIL
has already been carved out in detail (including the fields of dedicated CIL
and opportunistic CIL) and many research objectives have been stated, there is
still the need to clarify some terms such as information, knowledge, and
experience in the context of CIL and to differentiate CIL from recent and
ongoing research in related fields such as active learning, collaborative
learning, and others. Both aspects are addressed in this paper
Crowdsourcing for Bioinformatics
Motivation: Bioinformatics is faced with a variety of problems that require
human involvement. Tasks like genome annotation, image analysis, knowledge-base
construction and protein structure determination all benefit from human input.
In some cases people are needed in vast quantities while in others we need just
a few with very rare abilities. Crowdsourcing encompasses an emerging
collection of approaches for harnessing such distributed human intelligence.
Recently, the bioinformatics community has begun to apply crowdsourcing in a
variety of contexts, yet few resources are available that describe how these
human-powered systems work and how to use them effectively in scientific
domains. Results: Here, we provide a framework for understanding and applying
several different types of crowdsourcing. The framework considers two broad
classes: systems for solving large-volume 'microtasks' and systems for solving
high-difficulty 'megatasks'. Within these classes, we discuss system types
including: volunteer labor, games with a purpose, microtask markets and open
innovation contests. We illustrate each system type with successful examples in
bioinformatics and conclude with a guide for matching problems to crowdsourcing
solutions.Comment: Revie
Efficient Crowd Exploration of Large Networks: The Case of Causal Attribution
Accurately and efficiently crowdsourcing complex, open-ended tasks can be
difficult, as crowd participants tend to favor short, repetitive "microtasks".
We study the crowdsourcing of large networks where the crowd provides the
network topology via microtasks. Crowds can explore many types of social and
information networks, but we focus on the network of causal attributions, an
important network that signifies cause-and-effect relationships. We conduct
experiments on Amazon Mechanical Turk (AMT) testing how workers propose and
validate individual causal relationships and introduce a method for independent
crowd workers to explore large networks. The core of the method, Iterative
Pathway Refinement, is a theoretically-principled mechanism for efficient
exploration via microtasks. We evaluate the method using synthetic networks and
apply it on AMT to extract a large-scale causal attribution network, then
investigate the structure of this network as well as the activity patterns and
efficiency of the workers who constructed this network. Worker interactions
reveal important characteristics of causal perception and the network data they
generate can improve our understanding of causality and causal inference.Comment: 25 pages, 14 figures, in CSCW'1
A Survey on Data Collection for Machine Learning: a Big Data -- AI Integration Perspective
Data collection is a major bottleneck in machine learning and an active
research topic in multiple communities. There are largely two reasons data
collection has recently become a critical issue. First, as machine learning is
becoming more widely-used, we are seeing new applications that do not
necessarily have enough labeled data. Second, unlike traditional machine
learning, deep learning techniques automatically generate features, which saves
feature engineering costs, but in return may require larger amounts of labeled
data. Interestingly, recent research in data collection comes not only from the
machine learning, natural language, and computer vision communities, but also
from the data management community due to the importance of handling large
amounts of data. In this survey, we perform a comprehensive study of data
collection from a data management point of view. Data collection largely
consists of data acquisition, data labeling, and improvement of existing data
or models. We provide a research landscape of these operations, provide
guidelines on which technique to use when, and identify interesting research
challenges. The integration of machine learning and data management for data
collection is part of a larger trend of Big data and Artificial Intelligence
(AI) integration and opens many opportunities for new research.Comment: 20 page
Toward a System Building Agenda for Data Integration
In this paper we argue that the data management community should devote far
more effort to building data integration (DI) systems, in order to truly
advance the field. Toward this goal, we make three contributions. First, we
draw on our recent industrial experience to discuss the limitations of current
DI systems. Second, we propose an agenda to build a new kind of DI systems to
address these limitations. These systems guide users through the DI workflow,
step by step. They provide tools to address the "pain points" of the steps, and
tools are built on top of the Python data science and Big Data ecosystem
(PyData). We discuss how to foster an ecosystem of such tools within PyData,
then use it to build DI systems for collaborative/cloud/crowd/lay user
settings. Finally, we discuss ongoing work at Wisconsin, which suggests that
these DI systems are highly promising and building them raises many interesting
research challenges
TurKPF: TurKontrol as a Particle Filter
TurKontrol, and algorithm presented in (Dai et al. 2010), uses a POMDP to
model and control an iterative workflow for crowdsourced work. Here, TurKontrol
is re-implemented as "TurKPF," which uses a Particle Filter to reduce
computation time & memory usage. Most importantly, in our experimental
environment with default parameter settings, the action is chosen nearly
instantaneously. Through a series of experiments we see that TurKPF and
TurKontrol perform similarly.Comment: 8 pages, 6 figures, formula appendi
Atelier: Repurposing Expert Crowdsourcing Tasks as Micro-internships
Expert crowdsourcing marketplaces have untapped potential to empower workers'
career and skill development. Currently, many workers cannot afford to invest
the time and sacrifice the earnings required to learn a new skill, and a lack
of experience makes it difficult to get job offers even if they do. In this
paper, we seek to lower the threshold to skill development by repurposing
existing tasks on the marketplace as mentored, paid, real-world work
experiences, which we refer to as micro-internships. We instantiate this idea
in Atelier, a micro-internship platform that connects crowd interns with crowd
mentors. Atelier guides mentor-intern pairs to break down expert crowdsourcing
tasks into milestones, review intermediate output, and problem-solve together.
We conducted a field experiment comparing Atelier's mentorship model to a
non-mentored alternative on a real-world programming crowdsourcing task,
finding that Atelier helped interns maintain forward progress and absorb best
practices.Comment: CHI 201
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