133,670 research outputs found
Curriculum Guidelines for Undergraduate Programs in Data Science
The Park City Math Institute (PCMI) 2016 Summer Undergraduate Faculty Program
met for the purpose of composing guidelines for undergraduate programs in Data
Science. The group consisted of 25 undergraduate faculty from a variety of
institutions in the U.S., primarily from the disciplines of mathematics,
statistics and computer science. These guidelines are meant to provide some
structure for institutions planning for or revising a major in Data Science
Uncertainty in climate science and climate policy
This essay, written by a statistician and a climate scientist, describes our
view of the gap that exists between current practice in mainstream climate
science, and the practical needs of policymakers charged with exploring
possible interventions in the context of climate change. By `mainstream' we
mean the type of climate science that dominates in universities and research
centres, which we will term `academic' climate science, in contrast to `policy'
climate science; aspects of this distinction will become clearer in what
follows.
In a nutshell, we do not think that academic climate science equips climate
scientists to be as helpful as they might be, when involved in climate policy
assessment. Partly, we attribute this to an over-investment in high resolution
climate simulators, and partly to a culture that is uncomfortable with the
inherently subjective nature of climate uncertainty.Comment: submitted as contribution to Conceptual Foundations of
ClimateModeling, Winsberg, E. and Lloyd, E., eds., The University of Chicago
Pres
NP-complete Problems and Physical Reality
Can NP-complete problems be solved efficiently in the physical universe? I
survey proposals including soap bubbles, protein folding, quantum computing,
quantum advice, quantum adiabatic algorithms, quantum-mechanical
nonlinearities, hidden variables, relativistic time dilation, analog computing,
Malament-Hogarth spacetimes, quantum gravity, closed timelike curves, and
"anthropic computing." The section on soap bubbles even includes some
"experimental" results. While I do not believe that any of the proposals will
let us solve NP-complete problems efficiently, I argue that by studying them,
we can learn something not only about computation but also about physics.Comment: 23 pages, minor correction
Challenges in Complex Systems Science
FuturICT foundations are social science, complex systems science, and ICT.
The main concerns and challenges in the science of complex systems in the
context of FuturICT are laid out in this paper with special emphasis on the
Complex Systems route to Social Sciences. This include complex systems having:
many heterogeneous interacting parts; multiple scales; complicated transition
laws; unexpected or unpredicted emergence; sensitive dependence on initial
conditions; path-dependent dynamics; networked hierarchical connectivities;
interaction of autonomous agents; self-organisation; non-equilibrium dynamics;
combinatorial explosion; adaptivity to changing environments; co-evolving
subsystems; ill-defined boundaries; and multilevel dynamics. In this context,
science is seen as the process of abstracting the dynamics of systems from
data. This presents many challenges including: data gathering by large-scale
experiment, participatory sensing and social computation, managing huge
distributed dynamic and heterogeneous databases; moving from data to dynamical
models, going beyond correlations to cause-effect relationships, understanding
the relationship between simple and comprehensive models with appropriate
choices of variables, ensemble modeling and data assimilation, modeling systems
of systems of systems with many levels between micro and macro; and formulating
new approaches to prediction, forecasting, and risk, especially in systems that
can reflect on and change their behaviour in response to predictions, and
systems whose apparently predictable behaviour is disrupted by apparently
unpredictable rare or extreme events. These challenges are part of the FuturICT
agenda
Information Processing, Computation and Cognition
Computation and information processing are among the most fundamental notions in cognitive science. They are also among the most imprecisely discussed. Many cognitive scientists take it for granted that cognition involves computation, information processing, or both â although others disagree vehemently. Yet different cognitive scientists use âcomputationâ and âinformation processingâ to mean different things, sometimes without realizing that they do. In addition, computation and information processing are surrounded by several myths; first and foremost, that they are the same thing. In this paper, we address this unsatisfactory state of affairs by presenting a general and theory-neutral account of computation and information processing. We also apply our framework by analyzing the relations between computation and information processing on one hand and classicism and connectionism/computational neuroscience on the other. We defend the relevance to cognitive science of both computation, at least in a generic sense, and information processing, in three important senses of the term. Our account advances several foundational debates in cognitive science by untangling some of their conceptual knots in a theory-neutral way. By leveling the playing field, we pave the way for the future resolution of the debatesâ empirical aspects
Computation vs. Information Processing: Why Their Difference Matters to Cognitive Science
Since the cognitive revolution, itâs become commonplace that cognition involves both computation and information processing. Is this one claim or two? Is computation the same as information processing? The two terms are often used interchangeably, but this usage masks important differences. In this paper, we distinguish information processing from computation and examine some of their mutual relations, shedding light on the role each can play in a theory of cognition. We recommend that theorists of cognition be explicit and careful in choosing\ud
notions of computation and information and connecting them together. Much confusion can be avoided by doing so
Research Priorities for Robust and Beneficial Artificial Intelligence
Success in the quest for artificial intelligence has the potential to bring
unprecedented benefits to humanity, and it is therefore worthwhile to
investigate how to maximize these benefits while avoiding potential pitfalls.
This article gives numerous examples (which should by no means be construed as
an exhaustive list) of such worthwhile research aimed at ensuring that AI
remains robust and beneficial.Comment: This article gives examples of the type of research advocated by the
open letter for robust & beneficial AI at
http://futureoflife.org/ai-open-lette
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