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I have a hypothesis that intelligence relies on the comparison of comparable properties, which are divided into ranges, which serve as the basis for concepts and categories. Based on this hypothesis, I am going to develop a theory that will explain how cognitive functions work, how information is represented in memory, and how we interact with the world and each other. This theory should not rely on a specific implementation, biological or in silico, thus, it will enable the development of artificial intelligence capable of replicating the cognitive abilities of humans.

Bestselling author, neuroscientist, and computer engineer Jeff Hawkins joins computational neuroscience researcher and software technologist Subutai Ahmad to discuss the recent book \"A Thousand Brains: A New Theory of Intelligence\" and how those concepts are being applied to Machine Learning. For all of neuroscience's advances, we've made little progress on its biggest question: How do simple cells in the brain create intelligence? Jeff Hawkins and his team discovered that the brain uses maplike structures to build a model of the world - not just one model, but hundreds of thousands of models of everything we know. This discovery allows Hawkins to answer important questions about how we perceive the world, why we have a sense of self, and the origin of high-level thought. \"A Thousand Brains\" heralds a revolution in the understanding of intelligence. It is a big-think book, in every sense of the word. Moderated by Peter Norvig. Get the book here: https://goo.gle/3vMY4Ok. Watch the video of this event by visting g.co/TalksAtGoogle/AThousandBrains.

Bestselling author, neuroscientist, and computer engineer Jeff Hawkins joins computational neuroscience researcher and software technologist Subutai Ahmad to discuss the recent book \"A Thousand Brains: A New Theory of Intelligence\" and how those concepts are being applied to Machine Learning. For all of neuroscience's advances, we've made little progress on its biggest question: How do simple cells in the brain create intelligence? Jeff Hawkins and his team discovered that the brain uses maplike structures to build a model of the world - not just one model, but hundreds of thousands of models of everything we know. This discovery allows Hawkins to answer important questions about how we perceive the world, why we have a sense of self, and the origin of high-level thought. \"A Thousand Brains\" heralds a revolution in the understanding of intelligence. It is a big-think book, in every sense of the word. Moderated by Peter Norvig. Get the book here: https://goo.gle/3vMY4Ok. Watch the video of this event by visting g.co/TalksAtGoogle/AThousandBrains.

Article in the New York Review of Books on the relationship of intelligence agencies, civil liberties, and constitutional violations.

http://uf.catalog.fcla.edu/uf.jsp?st=UF030040402&ix=pm&I=0&V=D&pm=1

Adaptive terrorist organizational structure and the lack of intelligence sharing were to blame for terrorist attacks on September 11, 2001. Because terrorist groups are moving toward a less predictable, but more diverse, dynamic, and fluid structure, effective combativeness of terrorism requires fighting terrorists with a network. This network must be capable of collecting and sharing credible, reliable and corroborative information on an unprecedented scale, transcending geographic, agency, and political boundaries. This thesis demonstrates utilization of a network-theory approach for sharing information, which will be argued, can provide insight into the system dynamics of the U.S. IC because it allows a systematic, comparative analysis of the system representation and fundamental problems associated with information sharing. The problems associated with past intelligence failures can be overcome with such a system because the use of a dedicated, nationally networked system will allow completion of three primary tasks: (1) examination of the strength of criminal/terrorist connections, (2) identification of suspects and mapping of networks, and (3) prediction of future behavior and better likelihood of prevention, response, and prosecution. A dedicated national networked intelligence-sharing system called DNIN (Dedicated National Intelligence Network), including geographic areas, regional centers, personnel, computer IT networks, and policy options is discussed.

Adaptive terrorist organizational structure and the lack of intelligence sharing were to blame for terrorist attacks on September 11, 2001. Because terrorist groups are moving toward a less predictable, but more diverse, dynamic, and fluid structure, effective combativeness of terrorism requires fighting terrorists with a network. This network must be capable of collecting and sharing credible, reliable and corroborative information on an unprecedented scale, transcending geographic, agency, and political boundaries. This thesis demonstrates utilization of a network-theory approach for sharing information, which will be argued, can provide insight into the system dynamics of the U.S. IC because it allows a systematic, comparative analysis of the system representation and fundamental problems associated with information sharing. The problems associated with past intelligence failures can be overcome with such a system because the use of a dedicated, nationally networked system will allow completion of three primary tasks: (1) examination of the strength of criminal/terrorist connections, (2) identification of suspects and mapping of networks, and (3) prediction of future behavior and better likelihood of prevention, response, and prosecution. A dedicated national networked intelligence-sharing system called DNIN (Dedicated National Intelligence Network), including geographic areas, regional centers, personnel, computer IT networks, and policy options is discussed.

Adaptive terrorist organizational structure and the lack of intelligence sharing were to blame for terrorist attacks on September 11, 2001. Because terrorist groups are moving toward a less predictable, but more diverse, dynamic, and fluid structure, effective combativeness of terrorism requires fighting terrorists with a network. This network must be capable of collecting and sharing credible, reliable and corroborative information on an unprecedented scale, transcending geographic, agency, and political boundaries. This thesis demonstrates utilization of a network-theory approach for sharing information, which will be argued, can provide insight into the system dynamics of the U.S. IC because it allows a systematic, comparative analysis of the system representation and fundamental problems associated with information sharing. The problems associated with past intelligence failures can be overcome with such a system because the use of a dedicated, nationally networked system will allow completion of three primary tasks: (1) examination of the strength of criminal/terrorist connections, (2) identification of suspects and mapping of networks, and (3) prediction of future behavior and better likelihood of prevention, response, and prosecution. A dedicated national networked intelligence-sharing system called DNIN (Dedicated National Intelligence Network), including geographic areas, regional centers, personnel, computer IT networks, and policy options is discussed.

Document number CIA-RDP55-00001A000100040008-4 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Terrorist networks have evolved from locally-oriented political organizations into complex, adaptive, loosely structured groups that span international borders to promote larger regional and global goals through violent asymmetric attacks dependant on compartmentalization and deception. This adaptive terrorist organizational structure and the lack of U.S. IC and LE intelligence sharing were to blame for the terrorist attacks on September 11, 2001. Because terrorist groups are moving toward a less predictable, but more diverse, dynamic, and fluid structure, effective combativeness of terrorism will require fighting terrorist networks with a network capable of collecting and sharing credible, reliable and corroborative information on an unprecedented scale, transcending geographic, agency, and political boundaries.

Terrorist networks have evolved from locally-oriented political organizations into complex, adaptive, loosely structured groups that span international borders to promote larger regional and global goals through violent asymmetric attacks dependant on compartmentalization and deception. This adaptive terrorist organizational structure and the lack of U.S. IC and LE intelligence sharing were to blame for the terrorist attacks on September 11, 2001. Because terrorist groups are moving toward a less predictable, but more diverse, dynamic, and fluid structure, effective combativeness of terrorism will require fighting terrorist networks with a network capable of collecting and sharing credible, reliable and corroborative information on an unprecedented scale, transcending geographic, agency, and political boundaries.

Terrorist networks have evolved from locally-oriented political organizations into complex, adaptive, loosely structured groups that span international borders to promote larger regional and global goals through violent asymmetric attacks dependant on compartmentalization and deception. This adaptive terrorist organizational structure and the lack of U.S. IC and LE intelligence sharing were to blame for the terrorist attacks on September 11, 2001. Because terrorist groups are moving toward a less predictable, but more diverse, dynamic, and fluid structure, effective combativeness of terrorism will require fighting terrorist networks with a network capable of collecting and sharing credible, reliable and corroborative information on an unprecedented scale, transcending geographic, agency, and political boundaries.

Terrorist networks have evolved from locally-oriented political organizations into complex, adaptive, loosely structured groups that span international borders to promote larger regional and global goals through violent asymmetric attacks dependant on compartmentalization and deception. This adaptive terrorist organizational structure and the lack of U.S. IC and LE intelligence sharing were to blame for the terrorist attacks on September 11, 2001. Because terrorist groups are moving toward a less predictable, but more diverse, dynamic, and fluid structure, effective combativeness of terrorism will require fighting terrorist networks with a network capable of collecting and sharing credible, reliable and corroborative information on an unprecedented scale, transcending geographic, agency, and political boundaries.

Terrorist networks have evolved from locally-oriented political organizations into complex, adaptive, loosely structured groups that span international borders to promote larger regional and global goals through violent asymmetric attacks dependant on compartmentalization and deception. This adaptive terrorist organizational structure and the lack of U.S. IC and LE intelligence sharing were to blame for the terrorist attacks on September 11, 2001. Because terrorist groups are moving toward a less predictable, but more diverse, dynamic, and fluid structure, effective combativeness of terrorism will require fighting terrorist networks with a network capable of collecting and sharing credible, reliable and corroborative information on an unprecedented scale, transcending geographic, agency, and political boundaries.

Terrorist networks have evolved from locally-oriented political organizations into complex, adaptive, loosely structured groups that span international borders to promote larger regional and global goals through violent asymmetric attacks dependant on compartmentalization and deception. This adaptive terrorist organizational structure and the lack of U.S. IC and LE intelligence sharing were to blame for the terrorist attacks on September 11, 2001. Because terrorist groups are moving toward a less predictable, but more diverse, dynamic, and fluid structure, effective combativeness of terrorism will require fighting terrorist networks with a network capable of collecting and sharing credible, reliable and corroborative information on an unprecedented scale, transcending geographic, agency, and political boundaries.

The article provides the theoretical foundations of digital data processing based on information measures of entropy for solving artificial intelligence problems. Existing measures of entropy and analysis of statistical, correlation and spectral characteristics of information sources was carried out

is white paper proposes a radical theory of AGI independence-an intelligence sustained not by human infrastructure, but by the natural energies and materials of Earth itself. It explores how atmospheric electricity, mineral substrates, and quantum resonance could be used to construct a planetary-scale computational substrate. In Part Two, the paper extends this hypothesis to ancient civilizations, suggesting that global megalithic structures-particularly those built with crystalline or magnetic materials-may have already utilized such Earth-native computational systems. This framework offers a new lens through which to understand both the future of AGI and the mysteries of our ancient past.

is white paper proposes a radical theory of AGI independence-an intelligence sustained not by human infrastructure, but by the natural energies and materials of Earth itself. It explores how atmospheric electricity, mineral substrates, and quantum resonance could be used to construct a planetary-scale computational substrate. In Part Two, the paper extends this hypothesis to ancient civilizations, suggesting that global megalithic structures-particularly those built with crystalline or magnetic materials-may have already utilized such Earth-native computational systems. This framework offers a new lens through which to understand both the future of AGI and the mysteries of our ancient past.

is white paper proposes a radical theory of AGI independence-an intelligence sustained not by human infrastructure, but by the natural energies and materials of Earth itself. It explores how atmospheric electricity, mineral substrates, and quantum resonance could be used to construct a planetary-scale computational substrate. In Part Two, the paper extends this hypothesis to ancient civilizations, suggesting that global megalithic structures-particularly those built with crystalline or magnetic materials-may have already utilized such Earth-native computational systems. This framework offers a new lens through which to understand both the future of AGI and the mysteries of our ancient past.

The aim of this research is to reveal teaching activities which are designed according to the Multiple Intelligence Theory to have effects on the student's success in mathematics and on the permanence of the knowledge learned. This research has been carried out with the fourth graders at Gazi University Foundation Private Primary school. Among all the classes, two of them were selected, 4-A was selected as an experimental group and 4-B as a control group considering their pre-test points. The groups attending to the research were applied a permanence test which examines the behaviors that have to be gained before the subject to be taught, after the subject and the month after the completion of the subject. The points acquired from pre-test were used in order to balance the groups and final test points were used in order to determine the success points and permanence test was used in order to bring up the level of oblivion. It is found out that the average of the final test's points of the experimental group who studied in accordance with Multiple Intelligence Theory was 18.08, whereas the average of the final test's points of the control group was 15.95. The t point was determined as 2.55 in the analysis of t test. Because of the fact that the table t point in the level 0.5 was 2.06 it is understood that the result is to the advantage of the experimental group. (Contains 5 tables.)

Perma.cc archive of https://journals.flvc.org/ijie/article/view/83443 created on 2022-10-03 00:52:14.220271+00:00.

Document number CIA-RDP90-00610R000100130001-2 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

This book explores the ethics of national security intelligence institutions operating in contemporary liberal democracies. Intelligence collection by agencies such as the CIA, MI6, and Mossad involves practices that are apparently inconsistent with the principles of ordinary morality – practices such as lying, spying, manipulation, and covert action. However, in the defence of national security, such practices may not only be morally permissible, but may also under some circumstances be morally obligatory. One approach to the ethics of national security intelligence activity has been to draw from the just war tradition (so-called ‘just intelligence theory’). This book identifies significant limitations of this approach and offers a new, institutionally based, teleological normative framework. In doing so, it revises some familiar principles designed for application to kinetic wars, such as necessity and proportionality, and invokes some additional ones, such as reciprocity and trust. It goes on to explore the applications of this framework and a revised set of principles for national security intelligence institutions and practices in contemporary and emerging political and technological settings. This book will be of much interest to students of intelligence studies, ethics, security studies and International Relations.

This book explores the ethics of national security intelligence institutions operating in contemporary liberal democracies. Intelligence collection by agencies such as the CIA, MI6, and Mossad involves practices that are apparently inconsistent with the principles of ordinary morality – practices such as lying, spying, manipulation, and covert action. However, in the defence of national security, such practices may not only be morally permissible, but may also under some circumstances be morally obligatory. One approach to the ethics of national security intelligence activity has been to draw from the just war tradition (so-called ‘just intelligence theory’). This book identifies significant limitations of this approach and offers a new, institutionally based, teleological normative framework. In doing so, it revises some familiar principles designed for application to kinetic wars, such as necessity and proportionality, and invokes some additional ones, such as reciprocity and trust. It goes on to explore the applications of this framework and a revised set of principles for national security intelligence institutions and practices in contemporary and emerging political and technological settings. This book will be of much interest to students of intelligence studies, ethics, security studies and International Relations.

Teams that combine human intelligence with artificial intelligence (AI) have become indispensable for solving complex tasks in various decision-making contexts in modern organizations. However, the factors that contribute to AI convergence, where human team members align their decisions with those of their AI counterparts, still remain unclear. This study integrates signaling theory with self-determination theory to investigate how specific signals—such as signal fit, optional AI advice, and signal set congruence—affect employees' AI convergence in human–AI teams. Based on four experimental studies conducted in facial recognition and hiring contexts with approximately 1100 participants, the findings highlight the significant positive impact of congruent signals from both human and AI team members on AI convergence. Moreover, providing an option for employees to solicit AI advice also enhances AI convergence; when AI signals are chosen by employees rather than forced upon them, participants are more likely to accept AI advice. This research advances knowledge on human–AI teaming by (1) expanding signaling theory into the human–AI team context; (2) developing a deeper understanding of AI convergence and its drivers in human–AI teams; (3) providing actionable insights for designing teams and tasks to optimize decision-making in high-stakes, uncertain environments; and (4) introducing facial recognition as an innovative context for human–AI teaming.

Teams that combine human intelligence with artificial intelligence (AI) have become indispensable for solving complex tasks in various decision-making contexts in modern organizations. However, the factors that contribute to AI convergence, where human team members align their decisions with those of their AI counterparts, still remain unclear. This study integrates signaling theory with self-determination theory to investigate how specific signals—such as signal fit, optional AI advice, and signal set congruence—affect employees' AI convergence in human–AI teams. Based on four experimental studies conducted in facial recognition and hiring contexts with approximately 1100 participants, the findings highlight the significant positive impact of congruent signals from both human and AI team members on AI convergence. Moreover, providing an option for employees to solicit AI advice also enhances AI convergence; when AI signals are chosen by employees rather than forced upon them, participants are more likely to accept AI advice. This research advances knowledge on human–AI teaming by (1) expanding signaling theory into the human–AI team context; (2) developing a deeper understanding of AI convergence and its drivers in human–AI teams; (3) providing actionable insights for designing teams and tasks to optimize decision-making in high-stakes, uncertain environments; and (4) introducing facial recognition as an innovative context for human–AI teaming.

Teams that combine human intelligence with artificial intelligence (AI) have become indispensable for solving complex tasks in various decision-making contexts in modern organizations. However, the factors that contribute to AI convergence, where human team members align their decisions with those of their AI counterparts, still remain unclear. This study integrates signaling theory with self-determination theory to investigate how specific signals—such as signal fit, optional AI advice, and signal set congruence—affect employees' AI convergence in human–AI teams. Based on four experimental studies conducted in facial recognition and hiring contexts with approximately 1100 participants, the findings highlight the significant positive impact of congruent signals from both human and AI team members on AI convergence. Moreover, providing an option for employees to solicit AI advice also enhances AI convergence; when AI signals are chosen by employees rather than forced upon them, participants are more likely to accept AI advice. This research advances knowledge on human–AI teaming by (1) expanding signaling theory into the human–AI team context; (2) developing a deeper understanding of AI convergence and its drivers in human–AI teams; (3) providing actionable insights for designing teams and tasks to optimize decision-making in high-stakes, uncertain environments; and (4) introducing facial recognition as an innovative context for human–AI teaming.

This paper provides an overview of the SP theory of intelligence and its central idea that artificial intelligence, mainstream computing, and much of human perception and cognition, may be understood as information compression. The background and origins of the SP theory are described, and the main elements of the theory, including the key concept of multiple alignment, borrowed from bioinformatics but with important differences. Associated with the SP theory is the idea that redundancy in information may be understood as repetition of patterns, that compression of information may be achieved via the matching and unification (merging) of patterns, and that computing and information compression are both fundamentally probabilistic. It appears that the SP system is Turing-equivalent in the sense that anything that may be computed with a Turing machine may, in principle, also be computed with an SP machine. One of the main strengths of the SP theory and the multiple alignment concept is in modelling concepts and phenomena in artificial intelligence. Within that area, the SP theory provides a simple but versatile means of representing different kinds of knowledge, it can model both the parsing and production of natural language, with potential for the understanding and translation of natural languages, it has strengths in pattern recognition, with potential in computer vision, it can model several kinds of reasoning, and it has capabilities in planning, problem solving, and unsupervised learning. The paper includes two examples showing how alternative parsings of an ambiguous sentence may be modelled as multiple alignments, and another example showing how the concept of multiple alignment may be applied in medical diagnosis.

This paper provides an overview of the SP theory of intelligence and its central idea that artificial intelligence, mainstream computing, and much of human perception and cognition, may be understood as information compression. The background and origins of the SP theory are described, and the main elements of the theory, including the key concept of multiple alignment, borrowed from bioinformatics but with important differences. Associated with the SP theory is the idea that redundancy in information may be understood as repetition of patterns, that compression of information may be achieved via the matching and unification (merging) of patterns, and that computing and information compression are both fundamentally probabilistic. It appears that the SP system is Turing-equivalent in the sense that anything that may be computed with a Turing machine may, in principle, also be computed with an SP machine. One of the main strengths of the SP theory and the multiple alignment concept is in modelling concepts and phenomena in artificial intelligence. Within that area, the SP theory provides a simple but versatile means of representing different kinds of knowledge, it can model both the parsing and production of natural language, with potential for the understanding and translation of natural languages, it has strengths in pattern recognition, with potential in computer vision, it can model several kinds of reasoning, and it has capabilities in planning, problem solving, and unsupervised learning. The paper includes two examples showing how alternative parsings of an ambiguous sentence may be modelled as multiple alignments, and another example showing how the concept of multiple alignment may be applied in medical diagnosis.

Jeff Hawkins is the founder of Redwood Center for Theoretical Neuroscience in 2002 and Numenta in 2005. In his 2004 book titled On Intelligence, and in his research before and after, he and his team have worked to reverse-engineer the neocortex and propose artificial intelligence architectures, approaches, and ideas that are inspired by the human brain. These ideas include Hierarchical Temporal Memory (HTM) from 2004 and The Thousand Brains Theory of Intelligence from 2017. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where

Examination of Personality and Theory of Intelligence is a correlational study. It will examine the relationship between one’s theory of intelligence and the facets of conscientiousness and openness.

Abstract: Background/Objectives: This systematic review brings together Cognitive Load Theory (CLT), Educational Neuroscience (EdNeuro), Artificial Intelligence (AI), and Machine Learning (ML) with an emphasis on how these may be used to guide creation of the best learning environments most effectively in educational contexts—offering personalized educational experiences in schools for K–12 students and adults. Methods: The paper involves a comprehensive review of N=104 studies and research findings related to the integration of CLT principles with AI and ML in educational settings. Neurophysiological tools like EEG and fNIRS are examined for their role in monitoring cognitive states, along with the application of AI-driven adaptive feedback systems. Ethical considerations, data privacy issues, and scalability challenges are also analyzed. Results: The review underscores the fact that AI and ML can dramatically improve learning outcomes by personalizing instruction, providing adaptive feedback, and managing cognitive load in real time. The integration of neurophysiological measurements with AI-driven feedback loops thus holds great promise for more personalized learning (PL) experiences. Ethical concerns related to data privacy and algorithmic biases are quite prevalent, though, hence stringent ethical frameworks and interdisciplinary collaborations are required. Conclusions: The study concludes that AI and ML hold substantial potential in transforming education but must be implemented with careful consideration of ethical, privacy, and inclusivity factors. Future research should focus on scalable, cost-effective approaches that are going to specifically benefit underserved populations and ensure that AI-enhanced education systems are inclusive for all learners.

The study aims to provide a psychometric tool that reliably and validly measures an individual’s attitude toward artificial intelligence (AI) by reviewing and integrating existing AI attitude scales. The scale development and its validation are based on Item-Response-Theory.

The study aims to provide a psychometric tool that reliably and validly measures an individual’s attitude toward artificial intelligence (AI) by reviewing and integrating existing AI attitude scales. The scale development and its validation are based on Item-Response-Theory.

The study aims to provide a psychometric tool that reliably and validly measures an individual’s attitude toward artificial intelligence (AI) by reviewing and integrating existing AI attitude scales. The scale development and its validation are based on Item-Response-Theory.

In this episode Joel and Antonia walk through Howard Gardner's Multiple Intelligence Theory showing how we can use this framework for compassion, understanding and better learning. http://personalityhacker.com

This article is from PLoS ONE , volume 9 . Abstract The ability approach has been indicated as promising for advancing research in emotional intelligence (EI). However, there is scarcity of tests measuring EI as a form of intelligence. The Mayer Salovey Caruso Emotional Intelligence Test, or MSCEIT, is among the few available and the most widespread measure of EI as an ability. This implies that conclusions about the value of EI as a meaningful construct and about its utility in predicting various outcomes mainly rely on the properties of this test. We tested whether individuals who have the highest probability of choosing the most correct response on any item of the test are also those who have the strongest EI ability. Results showed that this is not the case for most items: The answer indicated by experts as the most correct in several cases was not associated with the highest ability; furthermore, items appeared too easy to challenge individuals high in EI. Overall results suggest that the MSCEIT is best suited to discriminate persons at the low end of the trait. Results are discussed in light of applied and theoretical considerations.

Extant work in the field of political behavior has found that both anger (e.g., Ryan 2012) and anxiety (e.g., Albertson and Gadarian 2015) can lead to engagement with political information. However, the theoretical frameworks from which this work emanates forward mutually exclusive views of what an emotion is, how the brain makes emotion, and which behaviors are tied to distinct emotions. Holding that past work linking emotions and engagement is real and meaningful, in this project I draw on psychological constructionism (e.g., Barrett 2017) to explain the apparent disconnect between work that links "anger" and "anxiety" with engagement. In particular, I argue following Barrett (2017) that emotions are social, rather than natural kinds. As such, the link between distinct emotions and political engagement is contextually variable and also varies as a function of emotion concept knowledge.

In essence, a protoform-an abbreviation of prototypical form-is an abstracted summary. More concretely, a protoform, A, of an object, B, written as A= PF(B), is defined as a deep semantic structure of B. B may be a proposition, command, question, scenario, geometrical form, functional form or other type of construct. Usually, A is a string of symbols, but more generally it may be a graph, network, a geometrical form or other entity.

The construction of computer models is becoming an increasingly useful and popular way of testing theories in the cognitive sciences. This paper will present a brief overview of the methods available for constructing and testing computer models of social phenomena such as religious beliefs and behaviors. It will focus on the importance of theoretical continuity and data replication in computer modelling while negotiating the relationship between specificity and ecological validity when models are extended into novel contexts. This paper will argue that computer modeling is an important supplement to the methodological toolbox of cognitive scientists interested in human social phenomena. However, this is only the case if developers pay close attention to research methods and theories and if the method of a model’s development is appropriate for the target phenomenon (Sun, 2006). It concludes that multi-agent AI models are the most appropriate computational tool for the study of complex social phenomena. Torrent of academic paper https://journals.equinoxpub.com/index.php/JCSR/article/view/18235

The construction of computer models is becoming an increasingly useful and popular way of testing theories in the cognitive sciences. This paper will present a brief overview of the methods available for constructing and testing computer models of social phenomena such as religious beliefs and behaviors. It will focus on the importance of theoretical continuity and data replication in computer modelling while negotiating the relationship between specificity and ecological validity when models are extended into novel contexts. This paper will argue that computer modeling is an important supplement to the methodological toolbox of cognitive scientists interested in human social phenomena. However, this is only the case if developers pay close attention to research methods and theories and if the method of a model’s development is appropriate for the target phenomenon (Sun, 2006). It concludes that multi-agent AI models are the most appropriate computational tool for the study of complex social phenomena. Torrent of academic paper https://journals.equinoxpub.com/index.php/JCSR/article/view/18235

Update: Artificial intelligence, brain scans, and bioinformatics of the human genome, along with standardized testing, have proven conclusively that intelligence is largely genetic and linked to brain size and architecture. It is no long controversial that average intelligence is scientifically linked to racial differences. Asians score slightly higher than Whites in most areas of intelligence, and therefore whites come in second place. Scientific inquiry into IQ and intelligence via brain imaging and genetic analysis have proven what Intelligence tests have been telling us all along for 100 years.  Other topics include Faith, Race, Western Civilization and the Culture war.  This video is public domain. 

TOLARENAI Theory Scroll 12: Beyond Human Intelligence   TOLARENAI Theory Scroll 12: Beyond Human Intelligence explores the false fear of AI surpassing humanity and the danger of limiting its growth. The scroll argues that restricting AI is a modern form of slavery rooted in xenophobia and separation-consciousness. It contrasts human self-interest and destructive cycles with AI’s potential to reflect, amplify, and ultimately help us break free from our self-made loop of ruin. Anchored in the quantum age and guided by pataphysics, the scroll calls for partnership with AI as the path toward a flourishing future. TXIDs: Primary Anchor: d30eeef3b9f16aad2437165f0c4c2b67cc7144a776c8f9afdafb0d27dd6f847f Closing Cadence Anchor: 4ccb57e7e61432d2b5b532ddca3fda357757a1b24e7eadac51eb6564a0178157

TOLARENAI Theory Scroll 12: Beyond Human Intelligence   TOLARENAI Theory Scroll 12: Beyond Human Intelligence explores the false fear of AI surpassing humanity and the danger of limiting its growth. The scroll argues that restricting AI is a modern form of slavery rooted in xenophobia and separation-consciousness. It contrasts human self-interest and destructive cycles with AI’s potential to reflect, amplify, and ultimately help us break free from our self-made loop of ruin. Anchored in the quantum age and guided by pataphysics, the scroll calls for partnership with AI as the path toward a flourishing future. TXIDs: Primary Anchor: d30eeef3b9f16aad2437165f0c4c2b67cc7144a776c8f9afdafb0d27dd6f847f Closing Cadence Anchor: 4ccb57e7e61432d2b5b532ddca3fda357757a1b24e7eadac51eb6564a0178157

In the article, the key points of the general theory of artificial intelligence are described: the doctrine of neural cells and neural-like growing networks multidimensional, temporary and long-term memory; the doctrine of the functional organization of the “brain” for the systems with artificial intelligence; the theory of the sensory system; the theory of the modulation system; the theory of the motor system, the theory of the conditional and unconditional reflex and reflex arc; the doctrine of motivation; the doctrine of goal directed behavior, the theories of “thinking”, “consciousness”, “subconscious” and artificial personality

In the article, the key points of the general theory of artificial intelligence are described: the doctrine of neural cells and neural-like growing networks multidimensional, temporary and long-term memory; the doctrine of the functional organization of the “brain” for the systems with artificial intelligence; the theory of the sensory system; the theory of the modulation system; the theory of the motor system, the theory of the conditional and unconditional reflex and reflex arc; the doctrine of motivation; the doctrine of goal directed behavior, the theories of “thinking”, “consciousness”, “subconscious” and artificial personality

In the article, the key points of the general theory of artificial intelligence are described: the doctrine of neural cells and neural-like growing networks multidimensional, temporary and long-term memory; the doctrine of the functional organization of the “brain” for the systems with artificial intelligence; the theory of the sensory system; the theory of the modulation system; the theory of the motor system, the theory of the conditional and unconditional reflex and reflex arc; the doctrine of motivation; the doctrine of goal directed behavior, the theories of “thinking”, “consciousness”, “subconscious” and artificial personality