The Emergence of Artificial Intelligence in the Home: Products, Services, and Broader
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What Is a Complex Adaptive System?
PROJECT GUTS What is a Complex Adaptive System? Introduction During the last three decades a leap has been made from the application of computing to help scientists ‘do’ science to the integration of computer science concepts, tools and theorems into the very fabric of science. The modeling of complex adaptive systems (CAS) is an example of such an integration of computer science into the very fabric of science; models of complex systems are used to understand, predict and prevent the most daunting problems we face today; issues such as climate change, loss of biodiversity, energy consumption and virulent disease affect us all. The study of complex adaptive systems, has come to be seen as a scientific frontier, and an increasing ability to interact systematically with highly complex systems that transcend separate disciplines will have a profound affect on future science, engineering and industry as well as in the management of our planet’s resources (Emmott et al., 2006). The name itself, “complex adaptive systems” conjures up images of complicated ideas that might be too difficult for a novice to understand. Instead, the study of CAS does exactly the opposite; it creates a unified method of studying disparate systems that elucidates the processes by which they operate. A complex system is simply a system in which many independent elements or agents interact, leading to emergent outcomes that are often difficult (or impossible) to predict simply by looking at the individual interactions. The “complex” part of CAS refers in fact to the vast interconnectedness of these systems. Using the principles of CAS to study these topics as related disciplines that can be better understood through the application of models, rather than a disparate collection of facts can strengthen learners’ understanding of these topics and prepare them to understand other systems by applying similar methods of analysis (Emmott et al., 2006). -
Organization, Self-Organization, Autonomy and Emergence: Status and Challenges
Organization, Self-Organization, Autonomy and Emergence: Status and Challenges Sven Brueckner 1 Hans Czap 2 1 New Vectors LLC 3520 Green Court, Suite 250, Ann Arbor, MI 48105-1579, USA Email: [email protected] http://www.altarum.net/~sbrueckner 2 University of Trier, FB IV Business Information Systems I D-54286 Trier, Germany Email: [email protected] http://www.wi.uni-trier.de/ Abstract: Development of IT-systems in application react and adapt autonomously to changing requirements. domains is facing an ever-growing complexity resulting from Therefore, approaches that rely on the fundamental principles a continuous increase in dynamics of processes, applications- of self-organization and autonomy are growing in acceptance. and run-time environments and scaling. The impact of this Following such approaches, software system functionality trend is amplified by the lack of central control structures. As is no longer explicitly designed into its component processes a consequence, controlling this complexity and dynamics is but emerges from lower-level interactions that are one of the most challenging requirements of today’s system purposefully unaware of the system-wide behavior. engineers. Furthermore, organization, the meaningful progression of The lack of a central control instance immediately raises local sensing, processing and action, is achieved by the the need for software systems which can react autonomously system components themselves at runtime and in response to to changing environmental requirements and conditions. the current state of the environment and the problem that is to Therefore, a new paradigm is necessary how to build be solved, rather than being enforced from the “outside” software systems changing radically the way one is used to through design or external control. -
1 Emergence of Universal Grammar in Foreign Word Adaptations* Shigeko
1 Emergence of Universal Grammar in foreign word adaptations* Shigeko Shinohara, UPRESA 7018 University of Paris III/CNRS 1. Introduction There has been a renewal of interest in the study of loanword phonology since the recent development of constraint-based theories. Such theories readily express target structures and modifications that foreign inputs are subject to (e.g. Paradis and Lebel 1994, Itô and Mester 1995a,b). Depending on how the foreign sounds are modified, we may be able to make inferences about aspects of the speaker's grammar for which the study of the native vocabulary is either inconclusive or uninformative. At the very least we expect foreign words to be modified in accordance with productive phonological processes and constraints (Silverman 1992, Paradis and Lebel 1994). It therefore comes as some surprise when patterns of systematic modification arise for which the rules and constraints of the native system have nothing to say or even worse contradict. I report a number of such “emergent” patterns that appear in our study of the adaptations of French words by speakers of Japanese (Shinohara 1997a,b, 2000). I claim that they pose a learnability problem. My working hypothesis is that these emergent patterns are reflections of Universal Grammar (UG). This is suggested by the fact that the emergent patterns typically correspond to well-established crosslinguistic markedness preferences that are overtly and robustly attested in the synchronic phonologies of numerous other languages. It is therefore natural to suppose that these emergent patterns follow from the default parameter settings or constraint rankings inherited from the initial stages of language acquisition that remain latent in the mature grammar. -
Clunio Populations to Different Tidal Conditions
Genetic adaptation in emergence time of Clunio populations to different tidal conditions DIETRICH NEUMANN Zoologisches Institut der Universitiit Wiirzburg, Wiirzburg KURZFASSUNG: Genetische Adaptation der Schliipfzeiten yon Clunio-Populationen an verschiedene Gezeitenbedingungen. Die Schliipfzeiten der in der Gezeitenzone Iebenden Clunio-Arten (Diptera, Chironomidae) sind mit bestimmten Wasserstandsbedingungen syn- chronisiert, und zwar derart, dat~ die nnmittelbar anschlief~ende Fortpflanzung der kurz- lebigen Imagines auf dem trockengefallenen Habitat stattfinden kann. Wenn das Habitat einer Clunio-Art in der mittleren Gezeitenzone liegt und parallel zu dem halbt~igigen Gezeiten- zyklus (T = 12,4 h) zweimal t~iglich auftaucht, dann kann sich eine 12,4stiindige Schliipf- periodik einstellen (Beispiel: Clunio takahashii). Wenn das Habitat in der unteren Gezeiten- zone liegt und nur um die Zeit der Springtiden auftaucht, dann ist eine 15t~igige (semilunare) SchRipfperiodik zu erwarten (Beispiele: Clunio marinus und C. mecliterraneus). Diese 15t~igige Schliipfperiodik ist synchronisiert mit bestimmten Niedrigwasserbedingungen, die an einem Kiistenort alle 15 Tage jewqils um die gleiche Tageszeit auftreten. Sie wird daher dutch zwei Daten eindeutig gekennzeichnet: (1) die lunaren Schliipftage (wenige aufeinanderfolgende Tage um Voll- und Neumond) und (2) die t~igliche Schliipfzeit. Wie experimentelle Untersuchungen iiber die Steuerung der Schliipfperiodik zeigten, kiSnnen die Tiere beide Daten richtig voraus- bestimmen. Die einzelnen Kiistenpopulationen -
Ophthalmology and the Emergence of Artificial Intelligence
Perspectives Ophthalmology and the emergence of artificial intelligence Rapid advances in AI in ophthalmology are a harbinger of things to come for other fields of medicine he autonomous detection and triage of eye for retinopathy using retinal photography. This disease, or even accurate estimations of gender, vast demand for diabetic eye screening services Tage, and blood pressure from a simple retinal has stimulated the development of AI algorithms to photo, may sound like the realms of science fiction, identify sight-threatening disease. Several algorithms but advances in artificial intelligence (AI) have already have achieved performance that meets or exceeds that made this a reality.1 Ophthalmology is at the vanguard of human experts.4,5 Accordingly, in 2018, the United of the development and clinical application of AI. States Food and Drug Administration approved an AI Advances in the field may provide useful insights into system to detect referable diabetic retinopathy from the application of this technology in health care more retinal photographs, the first autonomous diagnostic broadly. system to be approved in any field of medicine.6 Advances in deep learning have extended to other Artificial intelligence imaging modalities that are commonly used in ophthalmology. Ocular coherence tomography is an Once described as the capacity of intelligent machines imaging technology that produces highly detailed, to imitate human intelligence and behaviour, AI now depth-resolved images of the retina. A recent describes many theories and practices used to achieve collaboration between researchers and clinicians computer intelligence (Box 1).2 Machine learning is at Google DeepMind, Moorfields Eye Hospital an application of AI that uses algorithms or statistical and University College London culminated in the models to make decisions or predictions. -
Wearables: Their Time Has Come Norm Rose June 2015
ANALYSIS Wearables: Their Time Has Come Norm Rose June 2015 This article explores the evolution of the wearables segment, with a focus on applications for the travel industry and barriers to adoption. This content is published by Phocuswright Inc., a wholly owned subsidiary of Northstar Travel Media, LLC.The information herein is derived from a variety of sources. While every effort has been made to verify the information, the publisher assumes neither responsibility for inconsistencies or inaccuracies in the data nor liability for any damages of any type arising from errors or omissions. All Phocuswright publications are protected by copyright. It is illegal under U.S. federal law (17USC101 et seq.) to copy, fax or electronically distribute copyrighted material beyond the parameters of the License or outside of your organization without explicit permission. © 2015 Phocuswright, Inc All Rights Reserved. Wearables: Their Time Has Come June 2015 About Phocuswright is the travel industry research authority on how travelers, suppliers and intermediaries connect. Independent, rigorous and unbiased, Phocuswright fosters smart strategic planning, tactical decisionmaking and organizational effectiveness. Phocuswright delivers qualitative and quantitative research on the evolving dynamics that influence travel, tourism and hospitality distribution. Our marketplace intelligence is the industry standard for segmentation, sizing, forecasting, trends, analysis and consumer travel planning behavior. Every day around the world, senior executives,marketers, strategists and research professionals from all segments of the industry value chain use Phocuswright research for competitive advantage. To complement its primary research in North and Latin America, Europe and Asia, Phocuswright produces several highprofile conferences in the United States and Europe, and partners with conferences in China and Singapore. -
Artificial Intelligence: How Does It Work, Why Does It Matter, and What Can We Do About It?
Artificial intelligence: How does it work, why does it matter, and what can we do about it? STUDY Panel for the Future of Science and Technology EPRS | European Parliamentary Research Service Author: Philip Boucher Scientific Foresight Unit (STOA) PE 641.547 – June 2020 EN Artificial intelligence: How does it work, why does it matter, and what can we do about it? Artificial intelligence (AI) is probably the defining technology of the last decade, and perhaps also the next. The aim of this study is to support meaningful reflection and productive debate about AI by providing accessible information about the full range of current and speculative techniques and their associated impacts, and setting out a wide range of regulatory, technological and societal measures that could be mobilised in response. AUTHOR Philip Boucher, Scientific Foresight Unit (STOA), This study has been drawn up by the Scientific Foresight Unit (STOA), within the Directorate-General for Parliamentary Research Services (EPRS) of the Secretariat of the European Parliament. To contact the publisher, please e-mail [email protected] LINGUISTIC VERSION Original: EN Manuscript completed in June 2020. DISCLAIMER AND COPYRIGHT This document is prepared for, and addressed to, the Members and staff of the European Parliament as background material to assist them in their parliamentary work. The content of the document is the sole responsibility of its author(s) and any opinions expressed herein should not be taken to represent an official position of the Parliament. Reproduction and translation for non-commercial purposes are authorised, provided the source is acknowledged and the European Parliament is given prior notice and sent a copy. -
Now Hear This Workshop Full Transcript
April 18, 2017 Workshop Transcript Now Hear This: Competition, Innovation, and Consumer Protection Issues in Hearing Health Care Hosted by the Federal Trade Commission April 18, 2017 FTC Conference Center 400 Seventh Street, SW Washington, DC 20024 Introductory Remarks Tara Isa Koslov (FTC) ............................................................................................................. 1 Opening Remarks Acting Chairman Maureen K. Ohlhausen (FTC) .................................................................... 3 Adult Hearing Loss: Recent Data from the CDC Presentation: John Eichwald (CDC) ....................................................................................... 8 Panel 1: Innovations in Hearing Technology Panel Discussion ................................................................................................................... 16 Panelists: Stavros Basseas (Sound World Solutions), Brent Edwards (Earlens), Jani A. Johnson (University of Memphis), Jan Metzdorff (Sonova Wholesale), Dianne J. Van Tasell (Bose) Moderators: Karen A. Goldman (FTC), Daniel H. Wood (FTC) Panel 2: Innovations in Hearing Health Delivery Panel Discussion ................................................................................................................... 56 Panelists: Rupa Balachandran (University of the Pacific), Lucille Beck (Department of Veterans Affairs), Kim Cavitt (Audiology Resources), Scott Davis (Sivantos), Gary Swearingen (Costco Wholesale) Moderators: Daniel J. Gilman (FTC), David Schmidt (FTC) FDA -
Emergent Complex Systems
Futures 1994 26(6) 568-582 EMERGENT COMPLEX SYSTEMS Silvio Funtowicz and Jerome R. Ravetz Complex systems are becoming the focus of important innovative research and application in many areas, reflecting the progressive displacement of classical physics and the emergence of a new and creative role for mathematics. This article makes a distinction between ordinary and emergent complexity and argues that a full analysis requires dialectical thinking. In so doing the authors aim to provide a philosophical foundation for post-normal science. The exploratory analysis developed here is complementary to those conducted with a more formal, mathematical approach, and begins to articulate what lies on the other side of that somewhat indistinct divide, the conceptual space called emergent complexity. In response to the new leading problems for science, in which the traditional reductionist approach is patently inadequate, complex systems are becoming the focus of important innovative research and application in many areas.’ This development reflects the progressive displacement of classical physics as the exemplar science of our time, and the emergence of a new and creative role for mathematics. Now, formalisms and computations are no longer taken to represent the core of immutable truth and certainty in a world of flux; but they are used with respect for the variability and uncertainty of the world of experience. The distinction has already been made between simple and complex systems;’ we find it useful to further refine ‘complexity’ into ordinary and emergent. These types are characterized by two different patterns of structure and relationships. In ordinary complexity, the most common pattern is a complementarity of competition and cooperation, with a diversity of elements and subsystems. -
Selforganization, Emergence, and Constraint in Complex Natural
1 SelfOrganization, Emergence, and Constraint in Complex Natural Systems Abstract: Contemporary complexity theory has been instrumental in providing novel rigorous definitions for some classic philosophical concepts, including emergence. In an attempt to provide an account of emergence that is consistent with complexity and dynamical systems theory, several authors have turned to the notion of constraints on state transitions. Drawing on complexity theory directly, this paper builds on those accounts, further developing the constraintbased interpretation of emergence and arguing that such accounts recover many of the features of more traditional accounts. We show that the constraintbased account of emergence also leads naturally into a meaningful definition of selforganization, another concept that has received increasing attention recently. Along the way, we distinguish between order and organization, two concepts which are frequently conflated. Finally, we consider possibilities for future research in the philosophy of complex systems, as well as applications of the distinctions made in this paper. Keywords: Complexity Emergence Selforganization Spontaneous order Dynamical systems Corresponding Author: Jonathan Lawhead, PhD University of Southern California Philosophy & Earth Sciences 3651 Trousdale Parkway Zumberge Hall of Science, 223D Los Angeles, CA 900890740 [email protected] 775.287.8005 2 0. Introduction There’s a growing body of multidisciplinary research exploring complexity theory and related ideas. This field has not yet really settled yet, and so there’s plenty of terminological confusion out there. Different people use the same terms to mean different things (witness the constellation of definitions of ‘complexity’ itself). A good understanding of how central concepts in complexity theory fit together will help in applying those concepts to realworld social and scientific problems. -
Complex Adaptive Systems
Evidence scan: Complex adaptive systems August 2010 Identify Innovate Demonstrate Encourage Contents Key messages 3 1. Scope 4 2. Concepts 6 3. Sectors outside of healthcare 10 4. Healthcare 13 5. Practical examples 18 6. Usefulness and lessons learnt 24 References 28 Health Foundation evidence scans provide information to help those involved in improving the quality of healthcare understand what research is available on particular topics. Evidence scans provide a rapid collation of empirical research about a topic relevant to the Health Foundation's work. Although all of the evidence is sourced and compiled systematically, they are not systematic reviews. They do not seek to summarise theoretical literature or to explore in any depth the concepts covered by the scan or those arising from it. This evidence scan was prepared by The Evidence Centre on behalf of the Health Foundation. © 2010 The Health Foundation Previously published as Research scan: Complex adaptive systems Key messages Complex adaptive systems thinking is an approach that challenges simple cause and effect assumptions, and instead sees healthcare and other systems as a dynamic process. One where the interactions and relationships of different components simultaneously affect and are shaped by the system. This research scan collates more than 100 articles The scan suggests that a complex adaptive systems about complex adaptive systems thinking in approach has something to offer when thinking healthcare and other sectors. The purpose is to about leadership and organisational development provide a synopsis of evidence to help inform in healthcare, not least of which because it may discussions and to help identify if there is need for challenge taken for granted assumptions and further research or development in this area. -
Natural User Interfaces and Accessibility
Chapter 1 Natural User Interfaces and Accessibility he widespread adoption of mobile computing is a computers using actions related to everyday behavior, good thing for librarians who care about access such as touch, gestures, speech, and conversation.1 T for all. That’s because mobile devices make use The goal is to make it easy for humans to understand of natural user interfaces, and those interfaces are and use computers without having to learn compli- making computing easier for people of all ages and cated or abstract ways of doing things. Designers of abilities, as you’ll see in this report. these interfaces aim to create experiences that feel This trend, combined with the move toward multi- just as natural to a novice as to an expert user—and device ecosystems and the emphasis on students as for expert users it can feel like an extension of their creators with mobile apps, means that mobile learning body. is headed in a direction that is empowering for learn- ers of all abilities. There are other trends in mobile learning, but this Putting the Human before report focuses on these three: the Computer: The Move from GUI to NUI • natural user interfaces and accessibility • multi-device ecosystems NUIs are a new branch in the evolution of human- • content creation with mobile devices computer interaction, after GUIs (graphic user inter- faces). GUIs were designed to make computing easier That’s because there are synergies between these to learn than command-line interfaces, where you had trends that offer opportunities for those who care to remember specific commands and type them.