Relevant bibliographies by topics / Intelligence explosion

Contents

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

Academic literature on the topic 'Intelligence explosion'

Author: Grafiati
Published: 20 February 2023

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Journal articles on the topic "Intelligence explosion"

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Jebari, Karim, and Joakim Lundborg. "The intelligence explosion revisited." foresight 21, no. 1 (2019): 167–74. http://dx.doi.org/10.1108/fs-04-2018-0042.

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Purpose The claim that super intelligent machines constitute a major existential risk was recently defended in Nick Bostrom’s book Superintelligence and forms the basis of the sub-discipline AI risk. The purpose of this paper is to critically assess the philosophical assumptions that are of importance to the argument that AI could pose an existential risk and if so, the character of that risk. Design/methodology/approach This paper distinguishes between “intelligence” or the cognitive capacity of an individual and “techne”, a more general ability to solve problems using, for example, technological artifacts. While human intelligence has not changed much over historical time, human techne has improved considerably. Moreover, the fact that human techne has more variance across individuals than human intelligence suggests that if machine techne were to surpass human techne, the transition is likely going to be prolonged rather than explosive. Findings Some constraints for the intelligence explosion scenario are presented that imply that AI could be controlled by human organizations. Originality/value If true, this argument suggests that efforts should focus on devising strategies to control AI rather strategies that assume that such control is impossible.
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Prasad, Mahendra. "Nicolas de Condorcet and the First Intelligence Explosion Hypothesis." AI Magazine 40, no. 1 (2019): 29–33. http://dx.doi.org/10.1609/aimag.v40i1.2855.

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The intelligence explosion hypothesis (for example, a technological singularity) is roughly the hypothesis that accelerating knowledge or technological growth radically changes humanity. While 20th-century figures are commonly credited as the first discoverers of the hypothesis, I assert that Nicolas de Condorcet, the 18th-century mathematician, is the earliest to (1) mathematically model an intelligence explosion, and (2) present an accelerating historical worldview, and (3) make intelligence explosion predictions that were restated centuries later. Condorcet provides insights on how ontology and social choice can help resolve value alignment.
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Khan, Amad Ullah, Saad Salman, Khan Muhammad, and Mudassar Habib. "Modelling Coal Dust Explosibility of Khyber Pakhtunkhwa Coal Using Random Forest Algorithm." Energies 15, no. 9 (2022): 3169. http://dx.doi.org/10.3390/en15093169.

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Coal dust explosion constitutes a significant hazard in underground coal mines, coal power plants and other industries utilising coal as fuel. Knowledge of the explosion mechanism and the factors causing coal explosions is essential to investigate for the identification of the controlling factors for preventing coal dust explosions and improving safety conditions. However, the underlying mechanism involved in coal dust explosions is rarely studied under Artificial Intelligence (AI) based modelling. Coal from three different regions of Khyber Pakhtunkhwa, Pakistan, was tested for explosibility in 1.2 L Hartmann apparatus under various particle sizes and dust concentrations. First, a random forest algorithm was used to model the relationship between inputs (coal dust particle size, coal concentration and gross calorific value (GCV)), outputs (maximum pressure (Pmax) and the deflagration index (Kst)). The model reported an R2 value of 0.75 and 0.89 for Pmax and Kst. To further understand the impact of each feature causing explosibility, the random forest AI model was further analysed for sensitivity analysis by SHAP (Shapley Additive exPlanations). The study revealed that the most critical parameter affecting the explosibility of coal dust were particle size > GCV > concentration for Pmax and GCV > Particle size > Concentration for Kst. Mutual interaction SHAP plots of two variables at a time revealed that with <200 gm/L concentration, −73 µm size and a high GCV coal was the most explosive at a high concentration (>400 gm/L), explosibility is relatively lower irrespective of GCV and particle sizes.
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Khan, Amad Ullah, Saad Salman, Khan Muhammad, and Mudassar Habib. "Modelling Coal Dust Explosibility of Khyber Pakhtunkhwa Coal Using Random Forest Algorithm." Energies 15, no. 9 (2022): 3169. http://dx.doi.org/10.3390/en15093169.

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Coal dust explosion constitutes a significant hazard in underground coal mines, coal power plants and other industries utilising coal as fuel. Knowledge of the explosion mechanism and the factors causing coal explosions is essential to investigate for the identification of the controlling factors for preventing coal dust explosions and improving safety conditions. However, the underlying mechanism involved in coal dust explosions is rarely studied under Artificial Intelligence (AI) based modelling. Coal from three different regions of Khyber Pakhtunkhwa, Pakistan, was tested for explosibility in 1.2 L Hartmann apparatus under various particle sizes and dust concentrations. First, a random forest algorithm was used to model the relationship between inputs (coal dust particle size, coal concentration and gross calorific value (GCV)), outputs (maximum pressure (Pmax) and the deflagration index (Kst)). The model reported an R2 value of 0.75 and 0.89 for Pmax and Kst. To further understand the impact of each feature causing explosibility, the random forest AI model was further analysed for sensitivity analysis by SHAP (Shapley Additive exPlanations). The study revealed that the most critical parameter affecting the explosibility of coal dust were particle size > GCV > concentration for Pmax and GCV > Particle size > Concentration for Kst. Mutual interaction SHAP plots of two variables at a time revealed that with <200 gm/L concentration, −73 µm size and a high GCV coal was the most explosive at a high concentration (>400 gm/L), explosibility is relatively lower irrespective of GCV and particle sizes.
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Pratt, Gill A. "Is a Cambrian Explosion Coming for Robotics?" Journal of Economic Perspectives 29, no. 3 (2015): 51–60. http://dx.doi.org/10.1257/jep.29.3.51.

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About half a billion years ago, life on earth experienced a short period of very rapid diversification called the “Cambrian Explosion.” Many theories have been proposed for the cause of the Cambrian Explosion, one of the most provocative being the evolution of vision, allowing animals to dramatically increase their ability to hunt and find mates. Today, technological developments on several fronts are fomenting a similar explosion in the diversification and applicability of robotics. Many of the base hardware technologies on which robots depend—particularly computing, data storage, and communications—have been improving at exponential growth rates. Two newly blossoming technologies—“Cloud Robotics” and “Deep Learning”—could leverage these base technologies in a virtuous cycle of explosive growth. I examine some key technologies contributing to the present excitement in the robotics field. As with other technological developments, there has been a significant uptick in concerns about the societal implication of robotics and artificial intelligence. Thus, I offer some thoughts about how robotics may affect the economy and some ways to address potential difficulties.
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Mirenayat, Sayyed Ali, and Elaheh Soofastaei. "Singularity and Intelligence Explosion in William Hertling’s A.I. Apocalypse." International Letters of Social and Humanistic Sciences 50 (March 2015): 82–85. http://dx.doi.org/10.18052/www.scipress.com/ilshs.50.82.

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William Hertling’s A.I. Apocalypse(2012) is a post-apocalyptic novel about vulnerability of modern technological life against superintelligence. The novel shows living in an A.I-controlled society behind a revolution or revolt by self-controlling machines. A personified computer virus infects all the world’s computers. As a result, it leads to disruption in main factors of human life— billions of people are about to die. A novel in which, Hertling warns of dehumanization under the shadow of superintelligence to challenge our minds to ponder about such a future. This study attempts to demonstrate that Hertling criticizes singularity and intelligence explosion in which mankind are entrapped in A.I-controlled society. It also investigates probable bilateral relation between dehumanization and intelligence explosion.
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Ng, Ho Leung. "Recent Biophysical Advances in Drug Discovery." Biophysica 2, no. 2 (2022): 121–22. http://dx.doi.org/10.3390/biophysica2020013.

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Szhydlowski, Alexey V., and Maksim Yu Zadorin. "NATURAL LAW AND ARTIFICIAL INTELLIGENCE: TOWARDS A «DIGITAL CAMBRIAN EXPLOSION»." Bulletin of the Moscow State Regional University (Jurisprudence), no. 3 (2021): 39–53. http://dx.doi.org/10.18384/2310-6794-2021-3-39-53.

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Sterelny, Kim. "Social intelligence, human intelligence and niche construction." Philosophical Transactions of the Royal Society B: Biological Sciences 362, no. 1480 (2007): 719–30. http://dx.doi.org/10.1098/rstb.2006.2006.

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This paper is about the evolution of hominin intelligence. I agree with defenders of the social intelligence hypothesis in thinking that externalist models of hominin intelligence are not plausible: such models cannot explain the unique cognition and cooperation explosion in our lineage, for changes in the external environment (e.g. increasing environmental unpredictability) affect many lineages. Both the social intelligence hypothesis and the social intelligence–ecological complexity hybrid I outline here are niche construction models. Hominin evolution is hominin response to selective environments that earlier hominins have made. In contrast to social intelligence models, I argue that hominins have both created and responded to a unique foraging mode; a mode that is both social in itself and which has further effects on hominin social environments. In contrast to some social intelligence models, on this view, hominin encounters with their ecological environments continue to have profound selective effects. However, though the ecological environment selects, it does not select on its own. Accidents and their consequences, differential success and failure, result from the combination of the ecological environment an agent faces and the social features that enhance some opportunities and suppress others and that exacerbate some dangers and lessen others. Individuals do not face the ecological filters on their environment alone, but with others, and with the technology, information and misinformation that their social world provides.
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Maulana, Yuga, Yuga Maulana, Ganda Marihot Simangunsong, and Tri Karian. "Review Of The Artificial Neural Network Application In Prediciting Blast Vibration." Jurnal GEOSAPTA 7, no. 1 (2021): 47. http://dx.doi.org/10.20527/jg.v7i1.9353.

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The blasting method is one of the best hard rock excavation methods in mining activities. This method has negative impacts, one of which is the vibrations generated by the residual energy of the explosion. This impact will affect the environment around the blasting area, both slope stability, tunnels, infrastructure, and human settlements if it is close to the blasting site. Therefore, it needs initial planning and prediction to anticipate the blasting vibration that occurs. In general, the blast vibration can be predicted using the scale distance method which uses two parameters, namely the maximum amount of explosive material per time delay and the distance of measurement from the location of the explosion. This method has been widely researched to produce several empirical equations from each explosion location studied. However, as technology develops, several studies have tried to use artificial intelligence technology, one of which is the artificial neural network algorithm as a new approach for predicting detonation vibrations. In this method, the development of the parameters used in predicting the weighting of the most influential parameters from the formation of detonation vibrations can be carried out. This study will review several studies related to the use of artificial neural networks in predicting blasting vibrations in the studies that have been carried out and also compare with prediction methods using several empirical equations.
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Dissertations / Theses on the topic "Intelligence explosion"

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Sternhell, Robert. "Can we Build a Superintelligence Without Being Superintelligent?" Thesis, Department of Philosophy, 2017. http://hdl.handle.net/2123/18836.

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If we create an entity of greater intelligence to us, a superintelligence, it has the possibility to explode in intelligence, creating more and more intelligent entities. If the intelligence explosion argument holds, then the most important step to developing a powerful superintelligence is the development of the first superintelligence. This paper presents objections to the possibility of humans developing this first superintelligence. I argue that this is because we lack required knowledge about them, due to our epistemic position of not being superintelligent. I engage primarily with arguments from David Chalmers and Nick Bostrom about what superintelligences are and the nature of the intelligence explosion. I add my own detail to these areas and explore how to increase intelligence. I argue that my objections stem from flawed expectations of superintelligence such that we ought to change them. I then present my own alternative expectations for superintelligence.
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Yellanki, Sampath Kumar. "Kidney Compatibility Score Generation for a Donor - Recipient pair using Fuzzy Logic." University of Toledo / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1345153510.

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Sousselier, Thomas. "Conception et validation d'un algorithme de mise en formation d'essaim de micro-robots sous-marins auto-organisés : application à l'exploration de zone en guerre des mines." Lorient, 2013. https://hal.archives-ouvertes.fr/tel-01096431.

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La convergence des besoins en mission de guerre des mines et des avancées scientifiques dans le domaine de la robotique nous ont amenés à explorer une méthode innovante pour résoudre ce problème. Contrairement aux solutions de l'état de l'art, nous proposons de prendre en compte l'ensemble des contraintes opérationnelles de la mission et d'utiliser de nombreux UUV. Dans nos travaux, nous proposons une solution originale : le comportement d'essaim. Le comportement réactif des robots permet de faire émerger un comportement de groupe, effectuant la mission. Nous justifions l'approche en essaim de micro-robots par une étude technico-opérationnelle. Nous nous appuyons sur une méthode partant du général vers le particulier pour concevoir notre algorithme. Cet algorithme est capable d'aligner un essaim et de le faire progresser le long d'une trajectoire sans aucune communication. Nos résultats montrent que notre solution est robuste. L'essaim parvient à reconstituer la formation en cas de perte. La présence d'obstacles ne dégrade pas l'efficacité opérationnelle de l'essaim. L'application des méthodes en essaim à un domaine très contraint reste très peu explorée. Nous estimons que nos travaux montrent leurs capacités sur des missions complexes<br>Convergence of mine warfare needs and scientific advances in the field of robotics lead us to explore an innovative method to solve this issue. In our work, we propose a solution never applied to this specific area: the swarm behavior. The reactive behavior of robots leads to an emergent behavior, performing the mission. Our technical and operational study allows us to justify the micro-robots swarm approach. Before designing our algorithm by a method starting from the general to the particular, we specify the operational need. Our algorithm drives the swarm to form a line and to advance along a path without use any communication. We analyze the results of our algorithm to draw various conclusions. We demonstrate that our solution is robust. The swarm manages to reconstruct the formation in case of loss. The presence of obstacles does not decrease the operational efficiency of the swarm. Application of swarm methodologies to a very constrained environment remains largely unexplored. We believe that our research shows their capacity for complex missions
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Alliod, Charlotte. "Conception et modélisation de nouvelles molécules hautement énergétiques en fonction des contraintes réglementaires et environnementales." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSE1035.

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Depuis deux décennies, la recherche militaire se focalise sur l'amélioration des critères de performances des explosifs, tout en prenant en compte leurs impacts environnementaux et toxicologiques. Ces enjeux sont encadrés par une réglementation stricte : REACh (Registration, Evaluation, Authorization and Restriction of Chemicals) permettant d'assurer un haut niveau de protection sanitaire et environnementale. De nos jours, développer des explosifs ou molécules hautement énergétiques (High Energy Materials (HEM)) ayant un effet réduit sur l'homme et l'environnement est un sujet de préoccupation majeur. Ainsi, en collaboration avec Airbus Safran Lauchers (ASL), un programme de recherche a été mis en place, afin d'obtenir des outils optimisés pour la prédiction de la toxicité des HEMs et concevoir de nouvelles molécules HEMS non toxiques et réglementaires.Différentes méthodes in silico ont été utilisées dont des Relations Structure Activité Quantitatives (ou Quantitative Structure-Activity Relationship (QSAR)) et le Machine Learning. La recherche de similarité structurale parmi les molécules est un outil novateur sur lequel nous avons basé nos prédictions in silico. Cette similarité est obtenue grâce à un algorithme intelligent développé au sein du Pôle Rhône Alpin de Bio-Informatique de Lyon et qui a donné lieu à un brevet. Cet algorithme nous permet d'obtenir des prédictions plus précises basées sur des données expérimentales issues de directives européennes<br>For the last two decades, the military research has focused on the improvement of explosive performances, while taking into account their environmental and toxicological impacts. These issues are governed by strict regulations: REACh (Registration, Evaluation, Authorization and Restriction of Chemicals) to ensure a high level of health and environmental protection.Today, it's a major consideration to develop High Energetic Materials (HEM) or molecules who's hazard on human health and environment are reduced. Thus, in collaboration with Airbus Safran Lauchers (ASL), a research program was set up to obtain optimized tools for predicting the potential toxicity of HEM and to design new non-toxic and regulatory molecules.Different in silico methods have been used, including Quantitative Structure Activity Activity Relationships (QSARs) and Machine Learning.The search for structural similarity among molecules is an innovative tool on which we based our predictions in silico. This similarity is obtained thanks to an intelligent algorithm developed within the Pole Rhone Alpin de Bio-Informatique of Lyon which gave rise to a patent. This algorithm allows us to obtain more accurate predictions based on experimental data from European directives
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Rousselle, Denis. "Classification d’objets au moyen de machines à vecteurs supports dans les images de sonar de haute résolution du fond marin." Thesis, Rouen, INSA, 2016. http://www.theses.fr/2016ISAM0020.

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Cette thèse a pour objectif d'améliorer la classification d'objets sous-marins dans des images sonar haute résolution. En particulier, il s'agit de distinguer les mines des objets inoffensifs parmi une collection d'objets ressemblant à des mines. Nos recherches ont été dirigées par deux contraintes classiques en guerre de la mine : d'une part, le manque de données et d'autre part, le besoin de lisibilité des décisions. Nous avons donc constitué une base de données la plus représentative possible et simulé des objets dans le but de la compléter. Le manque d'exemples nous a mené à utiliser une représentation compacte, issue de la reconnaissance de visages : les Structural Binary Gradient Patterns (SBGP). Dans la même optique, nous avons dérivé une méthode d'adaptation de domaine semi-supervisée, basée sur le transport optimal, qui peut être facilement interprétable. Enfin, nous avons développé un nouvel algorithme de classification : les Ensemble of Exemplar-Maximum Excluding Ball (EE-MEB) qui sont à la fois adaptés à des petits jeux de données mais dont la décision est également aisément analysable<br>This thesis aims to improve the classification of underwater objects in high resolution sonar images. Especially, we seek to make the distinction between mines and harmless objects from a collection of mine-like objects. Our research was led by two classical constraints of the mine warfare : firstly, the lack of data and secondly, the need for readability of the classification. In this context, we built a database as much representative as possible and simulated objects in order to complete it. The lack of examples led us to use a compact representation, originally used by the face recognition community : the Structural Binary Gradient Patterns (SBGP). To the same end, we derived a method of semi-supervised domain adaptation, based on optimal transport, that can be easily interpreted. Finally, we developed a new classification algorithm : the Ensemble of Exemplar-Maximum Excluding Ball (EE-MEB) which is suitable for small datasets and with an easily interpretable decision function
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Books on the topic "Intelligence explosion"

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Hara, Takahiro, Vladimir I. Zadorozhny, and Erik Buchmann. Wireless sensor network technologies for the information explosion era. Springer, 2010.

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Webb, Gary. Dark alliance: The CIA, the Contras, and the crack cocaine explosion. Seven Stories Press, 1998.

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Dark alliance: The CIA, the contras, and the crack cocaine explosion. Seven Stories Press, 1999.

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Webb, Gary. Dark alliance: The CIA, the Contras, and the crack cocaine explosion. Seven Stories Press, 1998.

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United States. Congress. Senate. Select Committee on Intelligence. U.S. capability to monitor Soviet compliance with the Threshold Test Ban Treaty (TTBT) and the Treaty on Peaceful Nuclear Explosions (PNET): Report of the Select Committee on Intelligence, United States Senate. U.S. G.P.O., 1990.

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United States. Congress. Senate. Select Committee on Intelligence. U.S. capability to monitor Soviet compliance with the Threshold Test Ban Treaty (TTBT) and the Treaty on Peaceful Nuclear Explosions (PNET): Report of the Select Committee on Intelligence, United States Senate. U.S. G.P.O., 1990.

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United States. Congress. Senate. Select Committee on Intelligence. U.S. capability to monitor Soviet compliance with the Threshold Test Ban Treaty (TTBT) and the Treaty on Peaceful Nuclear Explosions (PNET): Report of the Select Committee on Intelligence, United States Senate. U.S. G.P.O., 1990.

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Intelligence, United States Congress Senate Select Committee on. U.S. capability to monitor Soviet compliance with the Threshold Test Ban Treaty (TTBT) and the Treaty on Peaceful Nuclear Explosions (PNET): Report of the Select Committee on Intelligence, United States Senate. U.S. G.P.O., 1990.

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Crichton, Michael. Rising Sun / The Andromeda Strain / Binary. Dealerfield, 1994.

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Crichton, Michael. Rising Sun / The Andromeda Strain / Binary. Cresset, 1994.

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Book chapters on the topic "Intelligence explosion"

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Zackova, Eva. "Intelligence Explosion Quest for Humankind." In Topics in Intelligent Engineering and Informatics. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-09668-1_3.

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Muehlhauser, Luke, and Anna Salamon. "Intelligence Explosion: Evidence and Import." In The Frontiers Collection. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32560-1_2.

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Loosemore, Richard, and Ben Goertzel. "Why an Intelligence Explosion is Probable." In The Frontiers Collection. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32560-1_5.

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Goertzel, Ben, Misgana Bayetta Belachew, Matthew Ikle’, and Gino Yu. "Controlling Combinatorial Explosion in Inference via Synergy with Nonlinear-Dynamical Attention Allocation." In Artificial General Intelligence. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41649-6_34.

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Ding, Yulin, and David Hemer. "An Optimised Algorithm to Tackle the Model Explosion Problem in CTL Model Update." In PRICAI 2010: Trends in Artificial Intelligence. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15246-7_54.

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Clarke, Edmund M. "Model Checking – My 27-Year Quest to Overcome the State Explosion Problem." In Logic for Programming, Artificial Intelligence, and Reasoning. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89439-1_13.

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Swan, Jerry, Eric Nivel, Neel Kant, Jules Hedges, Timothy Atkinson, and Bas Steunebrink. "Background." In The Road to General Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08020-3_2.

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AbstractRecent years have seen an explosion in academic, industrial, and popular interest in AI, as exemplified by machine learning and primarily driven by the widely-reported successes of deep- and reinforcement learning (e.g. [314, 315, 351]). Deep learning is essentially predicated on the notion that, with a sufficiently large training set, the statistical correlations captured by training will actually be causal [310]. However, in the absence of convergence theorems to support this, it remains a hypothesis. Indeed, insofar as there is evidence, it increasingly indicates to the contrary, since the application of enormous volumes of computational effort has still failed to deliver models with the generalization capability of an infant. There is accordingly increasing discussion about what further conceptual or practical insights might be required [57]. At the time of writing, the very definition of deep learning is in flux, with one Turing Award laureate defining it as “a way to try to make machines intelligent by allowing computers to learn from examples” and another as “differentiable programming”. We argue in the following that deep learning is highly unlikely to yield intelligence, at the very least while it equates intelligence with “solving a regression problem”.
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Shi, Ruotong, and Weibin Zhang. "Application of Computer Environment Simulation Technology in Explosion Proof Equipment Experiment." In 2021 International Conference on Applications and Techniques in Cyber Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79200-8_88.

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Love, Alan C. "Chance, Evolution, and the Metaphysical Implications of Paleontological Practice." In Abrahamic Reflections on Randomness and Providence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75797-7_7.

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AbstractFor several decades, a debate has been waged over how to interpret the significance of fossils from the Burgess Shale and Cambrian Explosion. Stephen Jay Gould argued that if the “tape of life” was rerun, then the resulting lineages would differ radically from what we find today, implying that humans are a happy accident of evolution. Simon Conway Morris argued that if the “tape of life” was rerun, the resulting lineages would be similar to what we now observe, implying that intelligence would still emerge from an evolutionary process. Recent methodological innovations in paleontological practice call into question both positions and suggest that global claims about the history of life, whether in terms of essential contingency or predictable convergence, are unwarranted.
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Volkov, Victor, and Yuri Kryvchenko. "Transition of Combustion to Explosion and Decision Support Systems for Explosion Protection." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-54215-3_28.

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Conference papers on the topic "Intelligence explosion"

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Wang, Yi, Wenyao Zhang, and JianGuo Ning. "Streamline-based Visualization of 3D Explosion Fields." In 2011 Seventh International Conference on Computational Intelligence and Security (CIS). IEEE, 2011. http://dx.doi.org/10.1109/cis.2011.271.

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Hargil, Patricia, and Chris Kapuscinski. "Application Explosion: What's the right business model?" In 2010 14th International Conference on Intelligence in Next Generation Networks (ICIN): "Weaving Applications Into the Network Fabric". IEEE, 2010. http://dx.doi.org/10.1109/icin.2010.5640942.

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Wu, Jie, Yanbin Qi, and Yue Zhang. "Design and Analysis of Car Explosion-Proof Tire Safety Wheel Hub." In 2016 International Conference on Artificial Intelligence and Engineering Applications. Atlantis Press, 2016. http://dx.doi.org/10.2991/aiea-16.2016.58.

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Feng, Jie, Ming-Hai Wang, Shun cheng Liu, and Xiao-lu Wu. "Error Analysis of Explosion-Height Controlling Method Based on Geomagnetism Information." In 2009 International Conference on Computational Intelligence and Software Engineering. IEEE, 2009. http://dx.doi.org/10.1109/cise.2009.5367102.

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Gao, Yongge, and Yongchen Yang. "The Mechanism Study of Serious Gas Explosion Accident at Coal Face." In 2009 Second International Symposium on Computational Intelligence and Design. IEEE, 2009. http://dx.doi.org/10.1109/iscid.2009.206.

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Yang, Liu, and Yang Dan. "Coal Dust Explosion Studies on Closed Room by Numerical Emulation Technique." In 2012 4th International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2012. http://dx.doi.org/10.1109/cicn.2012.62.

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Gao, Rui, and Bairong Wang. "Numerical simulation of normal reflection shock wave of near-surface nuclear explosion." In 2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID). IEEE, 2021. http://dx.doi.org/10.1109/aiid51893.2021.9456486.

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Mao, Jiawei, Xuhong Gao, Liqiang Liu, Dinghua Liu, Shaowei Lu, and Jin Li. "Design and Experiment of the Explosion-proof Electric Mobile Elevating Work Platforms." In 2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS). IEEE, 2020. http://dx.doi.org/10.1109/icaiis49377.2020.9194895.

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Liu, Jie, Ning Bai, Dong An, et al. "Failure Analysis on Explosion of High Temperature Superheater Tube for Supercritical Boiler." In AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture. ACM, 2021. http://dx.doi.org/10.1145/3495018.3501106.

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Jiang, Tao, Xinlei Ye, Ning Cheng, Zheng Li, and Caiping Zhang. "Neural Network-based Optimization of Rescue Performance of Nitrogen Explosion Type Hydraulic Breaker." In 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM). IEEE, 2021. http://dx.doi.org/10.1109/aiam54119.2021.00047.

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Reports on the topic "Intelligence explosion"

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Ratanamalaya, Richard S. Personal Electronic Devices and the ISR Data Explosion: The Impact of Cyber Cameras on the Intelligence Community. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ad1012788.

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Greinert, Jens. Mine Monitoring in the German Baltic Sea 2020; Dumped munition monitoring AL548, 03rd – 16th November 2020, Kiel (Germany) – Kiel (Germany) „MineMoni-II 2020“. GEOMAR Helmholtz Centre for Ocean Research Kiel, 2021. http://dx.doi.org/10.3289/cr_al548.

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Abstract:
ALKOR cruise AL548 took place as part of the EMFF (European Maritime and Fisheries Fund)-funded project BASTA (Boost Applied munition detection through Smart data inTegration and AI workflows; https://www.basta-munition.eu) and as continuation of the munition monitoring started within the BMBF-funded project UDEMM (Environmental Monitoring for the Delaboration of Munition in the Sea; https://udemm.geomar.de/). In October 2018, a first cruise (POS530 MineMoni2018) was conducted, to gather data for a broad baseline study in the German Baltic Sea. Results show a moderate contamination level on regional and coastal scale, but indicate higher levels for specific local areas. Within UDEMM, expertise was developed to detect, exactly locate and monitor munition (e.g. torpedoes, sea mines, ground mines) on the seafloor using optical and hydroacoustic means. In addition, chemical analyses of dissolved contaminants in the water and sediments was performed. Data acquired during this cruise are used in BASTA, which aims for enhanced munition detection via AUV-based artificial intelligence applied on multi-sensor datasets. At the same time, the project ExPloTect (Ex-situ, near-real-time exPlosive compound deTection in seawater) (also EMFF-funded) addresses the need for an innovative approach to detect explosive compounds in seawater. A prototype system was used and successfully tested for the first time during this cruise. The main focus was placed onto the two already known dumpsites Kolberger Heide and Lübeck Bight. Additionally, new areas Falshöft (Schleswig-Holstein) and Cadet Channel, Trollegrund and Großklützhöved (Mecklenburg-Vorpommern) were explored. In each area high-resolution multibeam mapping was performed and contact lists, indicating potential munition objects were produced on board. AUV surveys were conducted to ground-truth possible contacts via detailed photograph and magnetometer mapping. This was complemented with towed video (TV)-CTD profiles. The transits to and between those sites were planned along former constraint routes during WWII. These routes were main targets of the British Air Force and mines and bombs can be expected along these ways. During transits water samples were taken with on a CTD- (conductivity, temperature, depth) rosette-mounted Niskin bottles in regular distances, in order to obtain a comprehensive understanding munition compounds (inter alia trinitrotoluene (TNT)) measurements across the German Baltic Sea.
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