48,631 research outputs found
The importance of the observer in science
The concept of {\em complexity} (as a quantity) has been plagued by numerous
contradictory and confusing definitions. By explicitly recognising a role for
the observer of a system, an observer that attaches meaning to data about the
system, these contradictions can be resolved, and the numerous complexity
measures that have been proposed can be seen as cases where different observers
are relevant, and/or being proxy measures that loosely scale with complexity,
but are easy to compute from the available data. Much of the epistemic
confusion in the subject can be squarely placed at science's tradition of
removing the observer from the description in order to guarantee {\em
objectivity}. Explicitly acknowledging the role of the observer helps untangle
other confused subject areas. {\em Emergence} is a topic about which much ink
has been spilt, but it can be understand easily as an irreducibility between
description space and meaning space. Quantum Mechanics can also be understood
as a theory of observation. The success in explaining quantum mechanics, leads
one to conjecture that all of physics may be reducible to properties of the
observer. And indeed, what are the necessary (as opposed to contingent)
properties of an observer? This requires a full theory of consciousness, from
which we are a long way from obtaining. However where progress does appear to
have been made, e.g. Daniel Dennett's {\em Consciousness Explained}, a
recurring theme of self-observation is a crucial ingredient.Comment: In Proceedings The Two Cultures: Reconsidering the division between
the Sciences and Humanities, UNSW, July 200
Normalized Information Distance
The normalized information distance is a universal distance measure for
objects of all kinds. It is based on Kolmogorov complexity and thus
uncomputable, but there are ways to utilize it. First, compression algorithms
can be used to approximate the Kolmogorov complexity if the objects have a
string representation. Second, for names and abstract concepts, page count
statistics from the World Wide Web can be used. These practical realizations of
the normalized information distance can then be applied to machine learning
tasks, expecially clustering, to perform feature-free and parameter-free data
mining. This chapter discusses the theoretical foundations of the normalized
information distance and both practical realizations. It presents numerous
examples of successful real-world applications based on these distance
measures, ranging from bioinformatics to music clustering to machine
translation.Comment: 33 pages, 12 figures, pdf, in: Normalized information distance, in:
Information Theory and Statistical Learning, Eds. M. Dehmer, F.
Emmert-Streib, Springer-Verlag, New-York, To appea
Program Synthesis using Natural Language
Interacting with computers is a ubiquitous activity for millions of people.
Repetitive or specialized tasks often require creation of small, often one-off,
programs. End-users struggle with learning and using the myriad of
domain-specific languages (DSLs) to effectively accomplish these tasks.
We present a general framework for constructing program synthesizers that
take natural language (NL) inputs and produce expressions in a target DSL. The
framework takes as input a DSL definition and training data consisting of
NL/DSL pairs. From these it constructs a synthesizer by learning optimal
weights and classifiers (using NLP features) that rank the outputs of a
keyword-programming based translation. We applied our framework to three
domains: repetitive text editing, an intelligent tutoring system, and flight
information queries. On 1200+ English descriptions, the respective synthesizers
rank the desired program as the top-1 and top-3 for 80% and 90% descriptions
respectively
Beyond opening up the black box: Investigating the role of algorithmic systems in Wikipedian organizational culture
Scholars and practitioners across domains are increasingly concerned with
algorithmic transparency and opacity, interrogating the values and assumptions
embedded in automated, black-boxed systems, particularly in user-generated
content platforms. I report from an ethnography of infrastructure in Wikipedia
to discuss an often understudied aspect of this topic: the local, contextual,
learned expertise involved in participating in a highly automated
social-technical environment. Today, the organizational culture of Wikipedia is
deeply intertwined with various data-driven algorithmic systems, which
Wikipedians rely on to help manage and govern the "anyone can edit"
encyclopedia at a massive scale. These bots, scripts, tools, plugins, and
dashboards make Wikipedia more efficient for those who know how to work with
them, but like all organizational culture, newcomers must learn them if they
want to fully participate. I illustrate how cultural and organizational
expertise is enacted around algorithmic agents by discussing two
autoethnographic vignettes, which relate my personal experience as a veteran in
Wikipedia. I present thick descriptions of how governance and gatekeeping
practices are articulated through and in alignment with these automated
infrastructures. Over the past 15 years, Wikipedian veterans and administrators
have made specific decisions to support administrative and editorial workflows
with automation in particular ways and not others. I use these cases of
Wikipedia's bot-supported bureaucracy to discuss several issues in the fields
of critical algorithms studies, critical data studies, and fairness,
accountability, and transparency in machine learning -- most principally
arguing that scholarship and practice must go beyond trying to "open up the
black box" of such systems and also examine sociocultural processes like
newcomer socialization.Comment: 14 pages, typo fixed in v
Graduate Catalog, 1985-1986
https://scholar.valpo.edu/gradcatalogs/1013/thumbnail.jp
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