Relevant bibliographies by topics / Machine theory of collective intelligence

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Machine theory of collective intelligence'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Machine theory of collective intelligence"

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Canonico, Lorenzo Barberis, Christopher Flathmann, and Nathan McNeese. "The Wisdom of the Market: Using Human Factors to Design Prediction Markets for Collective Intelligence." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (November 2019): 1471–75. http://dx.doi.org/10.1177/1071181319631282.

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There is an ever-growing literature on the power of prediction markets to harness “the wisdom of the crowd” from large groups of people. However, traditional prediction markets are not designed in a human-centered way, often restricting their own potential. This creates the opportunity to implement a cognitive science perspective on how to enhance the collective intelligence of the participants. Thus, we propose a new model for prediction markets that integrates human factors, cognitive science, game theory and machine learning to maximize collective intelligence. We do this by first identifying the connections between prediction markets and collective intelligence, to then use human factors techniques to analyze our design, culminating in the practical ways with which our design enables artificial intelligence to complement human intelligence.
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Dzyaloshinsky, I. M. "Artificial Intelligence: A Humanitarian Perspective." Vestnik NSU. Series: History and Philology 21, no. 6 (June 17, 2022): 20–29. http://dx.doi.org/10.25205/1818-7919-2022-21-6-20-29.

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The article is devoted to the study of the features of human intelligence and the intelligence of complex computer systems, usually referred to as artificial intelligence (AI). As a hypothesis, a statement was formulated about a significant difference between human and artificial intelligence. Human intelligence is a product of a multi-thousand-year history of the development and interaction of three interrelated processes: 1) the formation and development of the human personality; 2) the formation of complex network relationships between members of the social community; 3) collective activity as the basis for the existence and development of communities and individuals. AI is a complex of technological solutions that imitate human cognitive processes. Because of this, with all the options for technical development (acceleration of processes for collecting and processing data and finding solutions, using computer vision, speech recognition and synthesis, etc.). AI will always be associated with human activity. In other words, only people (not machines) are the ultimate source and determinant of values on which any artificial intelligence depends. No mind (human or machine) will ever be truly autonomous: everything we do depends on the social context created by other people who determine the meaning of what we want to achieve. This means that people are responsible for everything that AI does.
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Barberis Canonico, Lorenzo, Nathan J. McNeese, and Chris Duncan. "Machine Learning as Grounded Theory: Human-Centered Interfaces for Social Network Research through Artificial Intelligence." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (September 2018): 1252–56. http://dx.doi.org/10.1177/1541931218621287.

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Internet technologies have created unprecedented opportunities for people to come together and through their collective effort generate large amounts of data about human behavior. With the increased popularity of grounded theory, many researchers have sought to use ever-increasingly large datasets to analyze and draw patterns about social dynamics. However, the data is simply too big to enable a single human to derive effective models for many complex social phenomena. Computational methods offer a unique opportunity to analyze a wide spectrum of sociological events by leveraging the power of artificial intelligence. Within the human factors community, machine learning has emerged as the dominant AI-approach to deal with big data. However, along with its many benefits, machine learning has introduced a unique challenge: interpretability. The models of macro-social behavior generated by AI are so complex that rarely can they translated into human understanding. We propose a new method to conduct grounded theory research by leveraging the power of machine learning to analyze complex social phenomena through social network analysis while retaining interpretability as a core feature.
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Orallo, José Hernández. "Heasuring (machine) intelligence universally: An interdisciplinary challenge." Acta Europeana Systemica 4 (July 13, 2020): 37–40. http://dx.doi.org/10.14428/aes.v4i1.57073.

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Artificial intelligence (Al) is having a deep impact on the way humans work, communicate and enjoy their leisure time. Al systems have been traditionally devised to solve specific tasks, such as playing chess, diagnosing a disease or driving a car. However, more and more Al systems are now being devised to be generally adaptable, and learn to solve a variety of tasks or to assist humans and organisations in their everyday tasks. As a result, an increasing number of robots, bots, avatars and 'smart' devices are enhancing our capabilities as individuals, collectives and humanity as a whole. What are these systems capable of doing? What is their global intelligence? How to tell whether they are meeting their specifications?Are the organisations including Al systems being less predictable and difficult to govern? The truth is that we lack proper measurement tools to evaluate the cognitive abilities and expected behaviour of this variety of systems. includino hybrid [e.g. machine-enhanced humans] and collectives. Once realised the relevance of Al evaluation and its difficulty, we will survey what has been done in the past twenty years in this area, focussing on approaches based on algorithmic information theory and Kolmogorov complexity, and its relation to other disciplines that are concerned with intelligence evaluation in humans and animals, such as psychometrics and comparative cognition. This will lead us to the notion of universal intelligence test and the new endeavour of universal psychometrics.
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Liang, Thow Yick. "The inherent structure and dynamic of intelligent human organizations." Human Systems Management 21, no. 1 (February 16, 2002): 9–19. http://dx.doi.org/10.3233/hsm-2002-21102.

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As humankind ventures deeper into the intelligence era, a totally re-defined mindset is essential to ensure its continuity. With the emerging new environment, human organizations must behave as intelligent beings, in the same manner as biological entities are competing for survival in an ecological system. They must learn, self-organize, adapt, compete and evolve. Thus, human systems can no longer be like machine. Consequently, the structures and characteristics of the industrial era will have to be dismantled. This shift in paradigm requires all human organizations to re-design their structure and operations around intelligence. Therefore, to strategize for the future, the first initiative human organizations need to adopt is to establish an intelligent structure, and to nurture an orgmind and its collective intelligence. A significant component of the orgmind is an intelligence enhancer comprising three entities, namely, intelligence, knowledge structure and theory. These entities interact continuously among themselves, supported by at least one physical symbol system. Eventually, the accuracy and appropriateness of the language used helps to enhance the engagement of the interacting agents in organization. In this respect, the ability to learn continuously, to adapt quickly, and to evolve effectively, is sustained by the intelligence enhancer.
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Liang, Thow Yick. "The new intelligence leadership strategy for iCAS." Human Systems Management 26, no. 2 (July 13, 2007): 111–22. http://dx.doi.org/10.3233/hsm-2007-26204.

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As humanity becomes more dependent on information and knowledge, the current concepts, theories and practices associated with leadership strategy have to be transformed. Fundamentally, the influence of the knowledge-intensive, fast-changing and more complex environment has initiated a shift in the mindset, strategic thinking, ability and style in the new generation of leaders. In addition, for all categories of human organizations (economics, business, social, education and political) their members are becoming better educated and informed, and consequently they are more sophisticated interacting agents with modified expectations. Leading these new intelligent human organizations is drastically different from leading a traditional setup. Consequently, the introduction of a new leadership strategy is inevitable. Concurrently, in the new context, it is also highly significant to recognize that all human thinking systems and human organizations are indeed complex adaptive systems. In such systems, order and complexity co-exist, and they learn, adapt and evolve with the changing environment, similar to the behavior of any biological species in an ecological system. The complex and nonlinear evolving dynamic is driven by the intrinsic intelligence of the individuals and the collective intelligence of the group. Therefore, focusing and exploiting the bio-logic rather than machine-logic perspective is definitely more appropriate. In this respect, a better comprehension of leadership strategy and organizational dynamics can be acquired by “bisociating” the complexity theory and the concept of organizing around intelligence. The resulting evolutionary model of this analysis is the intelligence leadership strategy.
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Chen, Zhengxin. "Learning about Learners: System Learning in Virtual Learning Environment." International Journal of Computers Communications & Control 3, no. 1 (March 1, 2008): 33. http://dx.doi.org/10.15837/ijccc.2008.1.2372.

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Virtual learning is not just about a set of useful IT tools for learning. From an examination on where virtual learning stands in the overall learning spectrum, we point out the important impact of natural computing on virtual learning. We survey and analyze selected literature on important role of natural computing aspects, such as emergence (using swarm intelligence to achieve collective intelligence) and emotion, to virtual learning. In addition, in order to effectively incorporate these aspects into virtual learning, we propose using infrastructural support for virtual learning through system learning: The virtual learning environment not only provides facilities for learners, but also observes the behavior of learners and takes actions, so that its own performance can be improved (i.e., to better serve the learners). In this sense, system learning is concerned with learning about learners. Consequently, a virtual learning environment is a true human-machine symbiosis, paired by human learning and system learning.
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Moore, Phoebe V., Kendra Briken, and Frank Engster. "Machines and measure." Capital & Class 44, no. 2 (February 6, 2020): 139–44. http://dx.doi.org/10.1177/0309816820902016.

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This Special Issue, entitled ‘Machines & Measure’, is largely the dissemination from a workshop held at University of Leicester School of Business, organised by editor Phoebe V Moore, for the Conference for Socialist Economists South Group in February 2018, which was hosted by the University of Leicester School of Business, Philosophy and Political Economy Centre. Not all the authors in the Special Issue were speakers at the event, but this collection provides a carefully selected, representative collection of articles and essays which address the questions and disturbances that drove the event’s concept, those being, as articulated in the event description: How are machines being used in contemporary capitalism to perpetuate control and to intensify power relations at work? Theorising how this occurs through discussions about the physical machine, the calculation machine and the social machine, the workshop was designed to re-visit questions about how quantification and measure both human and machinic become entangled in the social and how the incorporation and absorption of workers as appendages within the machine as Marx identified, where artificial intelligence and the platform economy dominate today’s discussions in digitalised work research.Stemming from Marxist critical theory, questions of money, time, space are also revisited in the Special Issues articles, as well as less debated concepts in rhythmanalysis and a revival of historically frequently discussed issues such as activities on the shop floor, where a whole range of semi-automated and fully automated methods to manage work through numeration without, necessarily, remuneration continue. Articles ask the most important questions today and begin to identify possible solutions from a self-consciously Marxist perspective.
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KAMYSHOVA, G. N. "MODELING OF NEURAL PREDICTIVE CONTROL OF IRRIGATION MACHINES." Prirodoobustrojstvo, no. 1 (2021): 14–22. http://dx.doi.org/10.26897/1997-6011-2021-1-14-22.

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The purpose of the study is to develop new scientific approaches to improve the efficiency of irrigation machines. Modern digital technologies allow the collection of data, their analysis and operational management of equipment and technological processes, often in real time. All this allows, on the one hand, applying new approaches to modeling technical systems and processes (the so-called “data-driven models”), on the other hand, it requires the development of fundamentally new models, which will be based on the methods of artificial intelligence (artificial neural networks, fuzzy logic, machine learning algorithms and etc.).The analysis of the tracks and the actual speeds of the irrigation machines in real time showed their significant deviations in the range from the specified speed, which leads to a deterioration in the irrigation parameters. We have developed an irrigation machine’s control model based on predictive control approaches and the theory of artificial neural networks. Application of the model makes it possible to implement control algorithms with predicting the response of the irrigation machine to the control signal. A diagram of an algorithm for constructing predictive control, a structure of a neuroregulator and tools for its synthesis using modern software are proposed. The versatility of the model makes it possible to use it both to improve the efficiency of management of existing irrigation machines and to develop new ones with integrated intelligent control systems.
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Sen, Wang, Zhu Xiaomei, and Deng Lin. "Impact of Job Demands on Employee Learning: The Moderating Role of Human–Machine Cooperation Relationship." Computational Intelligence and Neuroscience 2022 (December 6, 2022): 1–11. http://dx.doi.org/10.1155/2022/7406716.

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New artificial intelligence (AI) technologies are applied to work scenarios, which may change job demands and affect employees’ learning. Based on the resource conservation theory, the impact of job demands on employee learning was evaluated in the context of AI. The study further explores the moderating effect of the human–machine cooperation relationship between them. By collecting 500 valid questionnaires, a hierarchical regression for the test was performed. Results indicate that, in the AI application scenario, a U-shaped relationship exists between job demands and employee learning. Second, the human–machine cooperation relationship moderates the U-shaped curvilinear relationship between job demands and employees’ learning. In this study, AI is introduced into the field of employee psychology and behavior, enriching the research into the relationship between job demands and employee learning.
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Dissertations / Theses on the topic "Machine theory of collective intelligence"

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Ekpe, Bassey. "Theories of collective intelligence and decision-making : towards a viable United Nations intelligence system." Thesis, University of Huddersfield, 2005. http://eprints.hud.ac.uk/id/eprint/7481/.

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The idea of a United Nations (UN) intelligence system is widely misunderstood and debates about it seem to be both misplaced and anecdotal. The lack of a consistent theory on intelligence has fostered the widely held view that such a system is not feasible or incompatible with the UN collective security system. This dissertation takes as its central thesis, the question, of whether an intelligence system is both desirable and feasible within the UN structure. In spite of the fact that no known study has so far engaged with the subject matter at the depth presented in this dissertation, the study advances the concept of collective intelligence, and its implications for managing international conflicts. The dissertation examines existing barriers in efforts to interface intelligence system with the UN structure, and proposes that, with suitable refinements, the concept of intelligence need not be incompatible with the UN system. It is also argued that these constraints should not preclude evolutionary changes to include an intelligence system that is compatible with an organisation such as the UN. By developing a concept of collective intelligence, the thesis proposes theoretical frameworks that suggest a potential nature of a viable intelligence capability within the UN. The analysis is developed normatively and conceptually, which lead to a further conclusion that the UN already possesses an intelligence capability which exists in manner that is not recognised. The lack of scholarly efforts to ground such a system on a reasonable framework creates a vacuum in the study of international organisations, and in particular the United Nations system. At a minimum, this dissertation fills this gap.
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Carlucci, Lorenzo. "Some cognitively-motivated learning paradigms in Algorithmic Learning Theory." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file 0.68 Mb., p, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:3220797.

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Gramer, Rachel. "A GENRE OF COLLECTIVE INTELLIGENCE: BLOGS AS INTERTEXTUAL, RECIPROCAL, AND PEDAGOGICAL." Master's thesis, University of Central Florida, 2008. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2341.

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This thesis investigates the rhetorical features of blogs that lend them dialogic strength as an online genre through the lens of Mikhail Bakhtin's theories of speech genres, utterances, and dialogism. As a relatively new online genre, blogs stem from previous genres (in print and online as well as verbal), but their emergence as a popular form of expression in our current culture demands attention to how blogs also offer us different rhetorical opportunities to meet our changing social exigencies as online subjects in the 21st century. This thesis was inspired by questions about how blogs redefine the rhetorical situation to alter our textual roles as readers, writers, and respondents in the new generic circumstances we encounter--and reproduce--online. Applying the framework of Henry Jenkins' Convergence Culture and Pierre Levy's Collective Intelligence, this thesis analyzes how blogs enable us as online subjects to add our utterances to our textual collective intelligence, which benefits from our personal experience and the epistemic conversations of blogs as online texts. In addition, it is also an inquiry into how the rhetorical circumstances of blogs as textual sites of collective intelligence can create a reciprocal learning environment in the writing classroom. I ultimately examine blogs through the lenses of alternative pedagogy--informed by David Wallace and Helen Rothschild Ewald's Mutuality in the Rhetoric and Composition Classroom and Xin Liu Gale's Teachers, Discourses, and Authority in the Postmodern Composition Classroom--to suggest the potential consequences of a writing education that includes how we are currently writing--and being written by--our culture's online generic practice of blogs.
M.A.
Department of English
Arts and Humanities
English MA
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Lu, Yibiao. "Statistical methods with application to machine learning and artificial intelligence." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44730.

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This thesis consists of four chapters. Chapter 1 focuses on theoretical results on high-order laplacian-based regularization in function estimation. We studied the iterated laplacian regularization in the context of supervised learning in order to achieve both nice theoretical properties (like thin-plate splines) and good performance over complex region (like soap film smoother). In Chapter 2, we propose an innovative static path-planning algorithm called m-A* within an environment full of obstacles. Theoretically we show that m-A* reduces the number of vertex. In the simulation study, our approach outperforms A* armed with standard L1 heuristic and stronger ones such as True-Distance heuristics (TDH), yielding faster query time, adequate usage of memory and reasonable preprocessing time. Chapter 3 proposes m-LPA* algorithm which extends the m-A* algorithm in the context of dynamic path-planning and achieves better performance compared to the benchmark: lifelong planning A* (LPA*) in terms of robustness and worst-case computational complexity. Employing the same beamlet graphical structure as m-A*, m-LPA* encodes the information of the environment in a hierarchical, multiscale fashion, and therefore it produces a more robust dynamic path-planning algorithm. Chapter 4 focuses on an approach for the prediction of spot electricity spikes via a combination of boosting and wavelet analysis. Extensive numerical experiments show that our approach improved the prediction accuracy compared to those results of support vector machine, thanks to the fact that the gradient boosting trees method inherits the good properties of decision trees such as robustness to the irrelevant covariates, fast computational capability and good interpretation.
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Riedel, Marion, and Tino Schwarze. "Machine Translation (MT) - History, Theory, Problems and Usage." Universitätsbibliothek Chemnitz, 2001. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-200100437.

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Gulcehre, Caglar. "Two Approaches For Collective Learning With Language Games." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613109/index.pdf.

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Recent studies in cognitive science indicate that language has an important social function. The structure and knowledge of language emerges from the processes of human communication together with the domain-general cognitive processes. Each individual of a community interacts socially with a limited number of peers. Nevertheless societies are characterized by their stunning global regularities. By dealing with the language as a complex adaptive system, we are able to analyze how languages change and evolve over time. Multi-agent computational simulations assist scientists from different disciplines to build several language emergence scenarios. In this thesis several simulations are implemented and tested in order to categorize examples in a test data set efficiently and accurately by using a population of agents interacting by playing categorization games inspired by L. Steels'
s naming game. The emergence of categories throughout interactions between a population of agents in the categorization games are analyzed. The test results of categorization games as a model combination algorithm with various machine learning algorithms on different data sets have shown that categorization games can have a comparable performance with fast convergence.
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Shi, Bin. "A Mathematical Framework on Machine Learning: Theory and Application." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3876.

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The dissertation addresses the research topics of machine learning outlined below. We developed the theory about traditional first-order algorithms from convex opti- mization and provide new insights in nonconvex objective functions from machine learning. Based on the theory analysis, we designed and developed new algorithms to overcome the difficulty of nonconvex objective and to accelerate the speed to obtain the desired result. In this thesis, we answer the two questions: (1) How to design a step size for gradient descent with random initialization? (2) Can we accelerate the current convex optimization algorithms and improve them into nonconvex objective? For application, we apply the optimization algorithms in sparse subspace clustering. A new algorithm, CoCoSSC, is proposed to improve the current sample complexity under the condition of the existence of noise and missing entries. Gradient-based optimization methods have been increasingly modeled and inter- preted by ordinary differential equations (ODEs). Existing ODEs in the literature are, however, inadequate to distinguish between two fundamentally different meth- ods, Nesterov’s acceleration gradient method for strongly convex functions (NAG-SC) and Polyak’s heavy-ball method. In this paper, we derive high-resolution ODEs as more accurate surrogates for the two methods in addition to Nesterov’s acceleration gradient method for general convex functions (NAG-C), respectively. These novel ODEs can be integrated into a general framework that allows for a fine-grained anal- ysis of the discrete optimization algorithms through translating properties of the amenable ODEs into those of their discrete counterparts. As a first application of this framework, we identify the effect of a term referred to as gradient correction in NAG-SC but not in the heavy-ball method, shedding deep insight into why the for- mer achieves acceleration while the latter does not. Moreover, in this high-resolution ODE framework, NAG-C is shown to boost the squared gradient norm minimization at the inverse cubic rate, which is the sharpest known rate concerning NAG-C itself. Finally, by modifying the high-resolution ODE of NAG-C, we obtain a family of new optimization methods that are shown to maintain the accelerated convergence rates as NAG-C for minimizing convex functions.
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Georgescu, Mihai [Verfasser]. "When in doubt ask the crowd : leveraging collective intelligence for improving event detection and machine learning / Mihai Georgescu." Hannover : Technische Informationsbibliothek und Universitätsbibliothek Hannover (TIB), 2015. http://d-nb.info/107359663X/34.

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Ahlberg, Helgee Ernst. "Improving drug discovery decision making using machine learning and graph theory in QSAR modeling." Göteborg : Dept. of Chemistry, University of Gothenburg, 2010. http://gupea.ub.gu.se/dspace/handle/2077/21838.

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Lucking, Walter. "The application of time encoded signals to automated machine condition classification using neural networks." Thesis, University of Hull, 1997. http://hydra.hull.ac.uk/resources/hull:3766.

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This thesis considers the classification of physical states in a simplified gearbox using acoustical data and simple time domain signal shape characterisation techniques allied to a basic feedforward multi-layer perceptron neural network. A novel extension to the signal coding scheme (TES), involving the application of energy based shape descriptors, was developed. This sought specifically to improve the techniques suitability to the identification of mechanical states and was evaluated against the more traditional minima based TES descriptors. The application of learning based identification techniques offers potential advantages over more traditional programmed techniques both in terms of greater noise immunity and in the reduced requirement for highly skilled operators. The practical advantages accrued by using these networks are studied together with some of the problems associated in their use within safety critical monitoring systems.Practical trials were used as a means of developing the TES conversion mechanism and were used to evaluate the requirements of the neural networks being used to classify the data. These assessed the effects upon performance of the acquisition and digital signal processing phases as well as the subsequent training requirements of networks used for accurate condition classification. Both random data selection and more operator intensive performance based selection processes were evaluated for training. Some rudimentary studies were performed on the internal architectural configuration of the neural networks in order to quantify its influence on the classification process, specifically its effect upon fault resolution enhancement.The techniques have proved to be successful in separating several unique physical states without the necessity for complex state definitions to be identified in advance. Both the computational demands and the practical constraints arising from the use of these techniques fall within the bounds of a realisable system.
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Books on the topic "Machine theory of collective intelligence"

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Wieringa, Roel. Machine intelligence and explanation. Delft: Eburon, 1987.

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Bajpai, Manish Kumar, Koushlendra Kumar Singh, and George Giakos, eds. Machine Vision and Augmented Intelligence—Theory and Applications. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5078-9.

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Hillis, W. Daniel. The connection machine. Cambridge: M.I.T. Press, 1989.

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The connection machine. Cambridge, Mass: MIT Press, 1985.

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Kahraman, Cengiz, and Gülgün Kayakutlu, eds. Energy Management—Collective and Computational Intelligence with Theory and Applications. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75690-5.

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The emergence of artificial cognition: An introduction to collective learning. Singapore: World Scientific, 1993.

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Najim, K. Learning automata: Theory and applications. Oxford, OX, U.K: Pergamon, 1994.

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Okun, Oleg. Ensembles in Machine Learning Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.

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AGI Conference (1st 2008 University of Memphis). Artificial general intelligence, 2008: Proceedings of the First AGI Conference. Amsterdam: IOS Press, 2008.

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Nadia, Nedjah, and Macedo Mourelle Luiza de, eds. Evolvable machines: Theory & practice. Berlin: Springer, 2005.

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Book chapters on the topic "Machine theory of collective intelligence"

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Perennou, Loïc, and Raja Chiky. "Applying Supervised Machine Learning to Predict Virtual Machine Runtime for a Non-hyperscale Cloud Provider." In Computational Collective Intelligence, 676–87. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28374-2_58.

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Psathas, Anastasios Panagiotis, Antonios Papaleonidas, and Lazaros Iliadis. "Machine Learning Modeling of Human Activity Using PPG Signals." In Computational Collective Intelligence, 543–57. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63007-2_42.

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Rahman, Shadikur, Syeda Sumbul Hossain, Saiful Islam, Mazharul Islam Chowdhury, Fatama Binta Rafiq, and Khalid Been Md Badruzzaman. "Context-Based News Headlines Analysis Using Machine Learning Approach." In Computational Collective Intelligence, 167–78. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28374-2_15.

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Cupek, Rafal, Łukasz Gólczyński, and Adam Ziebinski. "An OPC UA Machine Learning Server for Automated Guided Vehicle." In Computational Collective Intelligence, 218–28. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28374-2_19.

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Petrynski, Kacper, Robert Szost, Iwona Pozniak-Koszalka, Leszek Koszalka, and Andrzej Kasprzak. "Single Machine Weighted Tardiness Problem: An Algorithm and Experimentation System." In Computational Collective Intelligence, 36–44. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98446-9_4.

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Tukeyev, Ualsher, Dina Amirova, Aidana Karibayeva, Aida Sundetova, and Balzhan Abduali. "Combined Technology of Lexical Selection in Rule-Based Machine Translation." In Computational Collective Intelligence, 491–500. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67077-5_47.

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Wolpert, David. "Theory of Collective Intelligence." In Collectives and the Design of Complex Systems, 43–106. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/978-1-4419-8909-3_2.

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Mussiraliyeva, Shynar, Milana Bolatbek, Batyrkhan Omarov, and Kalamkas Bagitova. "Detection of Extremist Ideation on Social Media Using Machine Learning Techniques." In Computational Collective Intelligence, 743–52. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63007-2_58.

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Mansurova, Madina, Vladimir Barakhnin, Yerzhan Khibatkhanuly, and Ilya Pastushkov. "Named Entity Extraction from Semi-structured Data Using Machine Learning Algorithms." In Computational Collective Intelligence, 58–69. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28374-2_6.

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Rana, Md Shohel, Sheikh Shah Mohammad Motiur Rahman, and Andrew H. Sung. "Evaluation of Tree Based Machine Learning Classifiers for Android Malware Detection." In Computational Collective Intelligence, 377–85. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98446-9_35.

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Conference papers on the topic "Machine theory of collective intelligence"

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Dickey, Rachel. "Robots, Cyborgs, and Architecture." In 105th ACSA Annual Meeting Paper Proceedings. ACSA Press, 2017. http://dx.doi.org/10.35483/acsa.am.105.77.

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This paper seeks to examine the robot cyborg paradigm in relation to architecture and artificial intelligence.It asks, what knowledge might arise from the cross disciplinary study of the historical narrative of the robot and cyborg? Referencing the birth of the robot and cyborg and exploring their significance from past to present, this paper strives to point out how these figures could help us question the status quo or reveal something to us about the world. Through the suggestion of a collective non-human form of intelligence in architecture we can ask, what might the machine have to offer that we haven’t considered or weren’t even capable of considering? How might machines actively collaborate in the design process?How might our relationship with technology enhance our creative capacities? The response to these questions begins with a comparative investigation of approaches to architecture and AI.
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Sarabandi, Kamal. "Collective intelligence (challenger)." In 2012 IEEE Technology Time Machine (TTM). IEEE, 2012. http://dx.doi.org/10.1109/ttm.2012.6509051.

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Richmond, Deborah. "Empowered Mobility: Supply Chain Thinking for Youth in Foster Care." In 2016 ACSA International Conference. ACSA Press, 2016. http://dx.doi.org/10.35483/acsa.intl.2016.29.

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The application of global container logistics to high mobility children, such as those in foster care, asks designers to consider an empathic, human-centered approach to an institutionalized system of involuntary mobility, which can result in as many as 3-4 family “placements” per year for some children. In spite of grim statistics for youth in foster care related to graduation, teen pregnancy, drug use and imprisonment, these children develop many positive resiliencies around adaptability, emotional intelligence, empathy and efficiency. Working with a non-profit serving youth in foster care in Watts, Los Angeles, called Peace4Kids, whose motto is “community as family,” the concept of a “mobile village” was born. Following their lead, paired with a deep understanding of consumer culture’s collective intelligence around moving goods through cities, an innovative strategy was used to create a literal delivery platform for educational programming, in partnership with other non-profits, around food equity, social justice and eventually other vocational skills such as apparel arts, machine arts, fine arts and early education.
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Muller-Steinhagen, Hans. "Collective intelligence (panel chair)." In 2012 IEEE Technology Time Machine (TTM). IEEE, 2012. http://dx.doi.org/10.1109/ttm.2012.6509047.

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Huang, Liwei, Haisu Zhang, Guisheng Chen, Yuchao Liu, and Deyi Li. "From turing machine intelligence to collective intelligence." In 2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems (CCIS). IEEE, 2012. http://dx.doi.org/10.1109/ccis.2012.6664568.

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Bundzel, Marek. "Towards collective intelligence." In 2018 IEEE 16th World Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE, 2018. http://dx.doi.org/10.1109/sami.2018.8324847.

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Ojanpera, Tero. "The power of collective intelligence." In 2011 IEEE Technology Time Machine (TTM). IEEE, 2011. http://dx.doi.org/10.1109/ttm.2011.6005170.

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Mittrick, Mark R., John Richardson, Mark Dennison, Theron Trout, Eric Heilman, and Timothy Hanratty. "Investigating immersive collective intelligence." In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, edited by Tien Pham. SPIE, 2019. http://dx.doi.org/10.1117/12.2519364.

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van der Gaag, Bert-Jan, Maurice Kardas, Joël van HERWAARDEN, Kees Van Ijselmuijden, and Liesbeth Tromp. "Structural bridge design for additive manufacturing." In IABSE Congress, New York, New York 2019: The Evolving Metropolis. Zurich, Switzerland: International Association for Bridge and Structural Engineering (IABSE), 2019. http://dx.doi.org/10.2749/newyork.2019.0195.

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<p>Companies in the architecture, engineering and construction (AEC) industry are constantly innovating on digital engineering. Digital engineering is the collective name for engineering activities that comprise automation, generative design, parametric design, automated manufacturing and artificial intelligence through machine learning. This research investigates the possibilities of generative design and additive manufacturing as a method to automate the design, engineering and construction process for bridges. Generative design will become an important aspect of future structural engineering of bridges. Automated optimization routines will find ideal structures for a specific case. Generative design finds these solutions within the possibilities and limitations of existing production techniques. With the introduction of additive manufacturing technology, a large range of possibilities become available to the engineer, resulting in innovative structural concepts for bridges. Untill recently, generative design and automated manufacturing have mostly been used in machine factory industry for small components. This paper elaborates on the challenges that come with automated design and manufacturing of bridges. The generative design and engineering challenges are about today’s available software and how they deal with bridge design. The manufacturing challenges are about material properties suitable for additive manufacturing and the possibilities and restrictions of large 3D printers.</p>
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Lu, Xiaoguang. "Knowledge Management Best Practices and Application in Field Development, IORs and Life-Cycle Reservoir Management." In SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/205723-ms.

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Abstract This paper presents a unique E&P knowledge management system which has been widely accepted and applied by upstream petroleum industry. This knowledge management system started in mid-1990s and consists of standard static and dynamic knowledge base, comprehensive evaluation reports, and fit-for-purpose analytics tools applicable to the entire E&P lifecycle. Emphasis is placed on illustrating the breadth and depth of the E&P knowledge and advanced analytics in terms of their capturing and applications in field development and production. This knowledge base consists of &gt;1600 reservoirs from around the world, each containing ~400 reservoir-level static parameters and a set of dynamic performance data. The static parameter covers reservoir characteristics, fluid properties, original in-place volume, EUR, recovery factor, production-related data (such as well spacing, well pattern, well EUR et al.), reservoir management practices, and key IORs/EORs and their incremental recovery. The knowledge extraction process involves collecting, reviewing, and synthesizing geologic, reservoir engineering and production data on a representative sample of global reservoirs. The reliable, coherent, high-quality knowledge base provides a foundation for the development of primary recovery index using supervised machine learning. Insights and intelligence derived from this knowledge base are critical to decision-making for both initial or early field development and production stages. The development application includes, but not limited to: (1) quantifying in-place volume, EUR, and recovery factor; (2) characterizing possible production performance and uncertainties and obtaining a conceptual production performance curve; (3) validating development plan options; and (4) benchmarking reservoir simulation results. The production application includes: (1) benchmarking production performance; (2) identifying upside potential and improved oil recovery opportunities; (3) finding best practices and lessons learned in reservoir management and secondary recovery practice; and (4) screening EOR methods, calibrating potential incremental recovery and characterizing EOR process performance. Lack of knowledge standardization and absence of coherence of data from various data sources are the main challenges facing industry's data-driven application. The knowledge management system presented in this study provides the most reliable knowledge base, advanced analytics tools, and practical application workflow to help the upstream industry become more efficient in applying collective human intelligence.
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Reports on the topic "Machine theory of collective intelligence"

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Engel, David, and Thomas Malone. Measuring Collective Intelligence in Human-Machine Systems. Fort Belvoir, VA: Defense Technical Information Center, December 2013. http://dx.doi.org/10.21236/ada602979.

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