Journal articles: 'Universal intelligence'

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Mirtz, Timothy. "UNIVERSAL INTELLIGENCE." Journal of Chiropractic Humanities 9 (January 1999): 55–71. http://dx.doi.org/10.1016/s1556-3499(13)60123-0.

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Legg, Shane, and Marcus Hutter. "Universal Intelligence: A Definition of Machine Intelligence." Minds and Machines 17, no. 4 (November 10, 2007): 391–444. http://dx.doi.org/10.1007/s11023-007-9079-x.

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Marinez-Lora, Ane, and Jeffery P. Braden. "Book Review: Universal Nonverbal Intelligence Test: University training guide, Universal Nonverbal Intelligence Test: University training CD, Universal Nonverbal Intelligence Test: Training video." Journal of Psychoeducational Assessment 18, no. 3 (September 2000): 289–92. http://dx.doi.org/10.1177/073428290001800309.

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Hernández-Orallo, José, and David L. Dowe. "Measuring universal intelligence: Towards an anytime intelligence test." Artificial Intelligence 174, no. 18 (December 2010): 1508–39. http://dx.doi.org/10.1016/j.artint.2010.09.006.

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Liu, Jing, Zhiwen Pan, Jingce Xu, Bing Liang, Yiqiang Chen, and Wen Ji. "Quality-time-complexity universal intelligence measurement." International Journal of Crowd Science 2, no. 2 (June 11, 2018): 99–107. http://dx.doi.org/10.1108/ijcs-04-2018-0007.

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Purpose With the development of machine learning techniques, the artificial intelligence systems such as crowd networks are becoming more autonomous and smart. Therefore, there is a growing demand for developing a universal intelligence measurement so that the intelligence of artificial intelligence systems can be evaluated. This paper aims to propose a more formalized and accurate machine intelligence measurement method. Design/methodology/approach This paper proposes a quality–time–complexity universal intelligence measurement method to measure the intelligence of agents. Findings By observing the interaction process between the agent and the environment, we abstract three major factors for intelligence measure as quality, time and complexity of environment. Originality/value This paper proposes a calculable universal intelligent measure method through considering more than two factors and the correlations between factors which are involved in an intelligent measurement.

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Ji, Wen, Jing Liu, Zhiwen Pan, Jingce Xu, Bing Liang, and Yiqiang Chen. "Quality-time-complexity universal intelligence measurement." International Journal of Crowd Science 2, no. 1 (March 5, 2018): 18–26. http://dx.doi.org/10.1108/ijcs-01-2018-0003.

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Purpose With development of machine learning techniques, the artificial intelligence systems such as crowd networks are becoming more and more autonomous and smart. Therefore, there is a growing demand to develop a universal intelligence measurement so that the intelligence of artificial intelligence systems can be evaluated. This paper aims to propose a more formalized and accurate machine intelligence measurement method. Design/methodology/approach This paper proposes a quality–time–complexity universal intelligence measurement method to measure the intelligence of agents. Findings By observing the interaction process between the agent and the environment, we abstract three major factors for intelligence measure as quality, time and complexity of environment. Practical implications In a crowd network, a number of intelligent agents are able to collaborate with each other to finish a certain kind of sophisticated tasks. The proposed approach can be used to allocate the tasks to the agents within a crowd network in an optimized manner. Originality/value This paper proposes a calculable universal intelligent measure method through considering more than two factors and the correlations between factors which are involved in an intelligent measurement.

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Wichert, Andreas. "Artificial intelligence and a universal quantum computer." AI Communications 29, no. 4 (May 30, 2016): 537–43. http://dx.doi.org/10.3233/aic-160699.

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O'Toole, George J. A. "Kahn's law: A universal principle of intelligence?" International Journal of Intelligence and CounterIntelligence 4, no. 1 (January 1990): 39–46. http://dx.doi.org/10.1080/08850609008435126.

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Hutter, Marcus. "Open Problems in Universal Induction & Intelligence." Algorithms 2, no. 3 (July 2, 2009): 879–906. http://dx.doi.org/10.3390/a2030879.

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Young, Ellie L., and Rosaura Assing. "Book Review: The Universal Nonverbal Intelligence Test." Journal of Psychoeducational Assessment 18, no. 3 (September 2000): 280–88. http://dx.doi.org/10.1177/073428290001800308.

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Dowe, David L., and José Hernández-Orallo. "How universal can an intelligence test be?" Adaptive Behavior 22, no. 1 (November 13, 2013): 51–69. http://dx.doi.org/10.1177/1059712313500502.

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Mashingaidze, Sivave. "Harmonizing intelligence terminologies in business: Literature review." Journal of Governance and Regulation 3, no. 4 (2014): 156–62. http://dx.doi.org/10.22495/jgr_v3_i4_c1_p8.

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The principal objective of this article is to do a literature review of different intelligence terminology with the aim of establishing the common attributes and differences, and to propose a universal and comprehensive definition of intelligence for common understanding amongst users. The findings showed that Competitive Intelligence has the broadest scope of intelligence activities covering the whole external operating environment of the company and targeting all levels of decision-making for instance; strategic intelligence, tactical intelligence and operative intelligence. Another terminology was found called Cyber IntelligenceTM which encompasses competitor intelligence, strategic intelligence, market intelligence and counterintelligence. In conclusion although CI has the broadest scope of intelligence and umbrella to many intelligence concepts, still Business Intelligence, and Corporate Intelligence are often used interchangeably as CI.

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Iantovics, Kountchev, and Crișan. "ExtrIntDetect—A New Universal Method for the Identification of Intelligent Cooperative Multiagent Systems with Extreme Intelligence." Symmetry 11, no. 9 (September 4, 2019): 1123. http://dx.doi.org/10.3390/sym11091123.

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In this research, we define a specific type of performance of the intelligent agent-based systems (IABSs) in terms of a difficult problem-solving intelligence measure. Many studies present the successful application of intelligent cooperative multiagent systems (ICMASs) for efficient, flexible and robust solving of difficult real-life problems. Based on a comprehensive study of the scientific literature, we conclude that there is no unanimous view in the scientific literature on machine intelligence, or on what an intelligence metric must measure. Metrics presented in the scientific literature are based on diverse paradigms. In our approach, we assume that the measurement of intelligence is based on the ability to solve difficult problems. In our opinion, the measurement of intelligence in this context is important, as it allows the differentiation between ICMASs based on the degree of intelligence in problem-solving. The recent OutIntSys method presented in the scientific literature can identify systems with outlier high and outlier low intelligence from a set of studied ICMASs. In this paper, a novel universal method called ExtrIntDetect, defined on the basis of a specific series of computing processes and analyses, is proposed for the detection of the ICMASs with statistical outlier low and high problem-solving intelligence from a given set of studied ICMASs. ExtrIntDetect eliminates the disadvantage of the OutIntSys method with respect to its limited robustness. The recent symmetric MetrIntSimil metric presented in the literature is capable of measuring and comparing the intelligence of large numbers of ICMASs and based on their respective problem-solving intelligences in order to classify them into intelligence classes. Systems whose intelligence does not statistically differ are classified as belonging to the same class of intelligent systems. Systems classified in the same intelligence class are therefore able to solve difficult problems using similar levels of intelligence. One disadvantage of the symmetric MetrIntSimil lies in the fact that it is not able to detect outlier intelligence. Based on this fact, the ExtrIntDetect method could be used as an extension of the MetrIntSimil metric. To validate and evaluate the ExtrIntDetect method, an experimental evaluation study on six ICMASs is presented and discussed.

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Lenherr, Nicola, René Pawlitzek, and Bruno Michel. "New universal sustainability metrics to assess edge intelligence." Sustainable Computing: Informatics and Systems 31 (September 2021): 100580. http://dx.doi.org/10.1016/j.suscom.2021.100580.

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Lee, Da-sheng, Yan-Tang Chen, and Shih-Lung Chao. "Universal workflow of artificial intelligence for energy saving." Energy Reports 8 (November 2022): 1602–33. http://dx.doi.org/10.1016/j.egyr.2021.12.066.

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de Vent, N. R., J. A. Agelink van Rentergem, M. C. Kerkmeer, H. M. Huizenga, B. A. Schmand, and J. M. J. Murre. "Universal Scale of Intelligence Estimates (USIE): Representing Intelligence Estimated From Level of Education." Assessment 25, no. 5 (July 7, 2016): 557–63. http://dx.doi.org/10.1177/1073191116659133.

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In clinical neuropsychology, it is often necessary to estimate a patient’s premorbid level of cognitive functioning in order to evaluate whether his scores on cognitive tests should be considered abnormal. In practice, test results from before the onset of brain pathology are rarely available, and the patient’s level of education is used instead as an estimate of his premorbid level. Unfortunately, level of education may be expressed on many different scales of education, which are difficult to use interchangeably. Here, we introduce a new scale that has the capacity to replace existing scales and can be used interchangeably with any of them: the Universal Scale of Intelligence Estimates (USIE). To achieve this, we propose to map all levels of existing educational scales to standard IQ scores. This USIE point estimate is supplemented with an estimation interval. We assert that USIE offers some important benefits for clinical practice and research.

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Amram, Yosi Joseph. "The Intelligence of Spiritual Intelligence: Making the Case." Religions 13, no. 12 (November 24, 2022): 1140. http://dx.doi.org/10.3390/rel13121140.

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Spiritual Intelligence (SI) is the ability to draw on and embody spiritual qualities and resources to enhance daily functioning and wellbeing. Despite Gardner’s rejection of SI, the proliferation of SI models and research supporting the advantages of SI across a wide field of endeavors, and correlation with neurological structures and genetic indicators strongly support its validity as an intelligence. This article reviewed key models of SI, and studies supporting its adaptability and biological correlates. Puzzling results in some areas warrant exploration, especially the degree to which various models converge and the extent to which a single SI model may be truly universal.

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Greco, Salvatore, Radko Mesiar, and Fabio Rindone. "Discrete bipolar universal integrals." Fuzzy Sets and Systems 252 (October 2014): 55–65. http://dx.doi.org/10.1016/j.fss.2014.02.002.

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Steinert-Threlkeld, Shane. "An Explanation of the Veridical Uniformity Universal." Journal of Semantics 37, no. 1 (December 23, 2019): 129–44. http://dx.doi.org/10.1093/jos/ffz019.

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Abstract A semantic universal, which we here dub the Veridical Uniformity Universal, has recently been argued to hold of responsive verbs (those that take both declarative and interrogative complements). This paper offers a preliminary explanation of this universal: verbs satisfying it are easier to learn than those that do not. This claim is supported by a computational experiment using artificial neural networks, mirroring a recent proposal for explaining semantic universals of quantifiers. This preliminary study opens up many avenues for future work on explaining semantic universals more generally, which are discussed in the conclusion.

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Shao, Helen, Francisco Villaescusa-Navarro, Shy Genel, David N. Spergel, Daniel Anglés-Alcázar, Lars Hernquist, Romeel Davé, Desika Narayanan, Gabriella Contardo, and Mark Vogelsberger. "Finding Universal Relations in Subhalo Properties with Artificial Intelligence." Astrophysical Journal 927, no. 1 (March 1, 2022): 85. http://dx.doi.org/10.3847/1538-4357/ac4d30.

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Abstract We use a generic formalism designed to search for relations in high-dimensional spaces to determine if the total mass of a subhalo can be predicted from other internal properties such as velocity dispersion, radius, or star formation rate. We train neural networks using data from the Cosmology and Astrophysics with MachinE Learning Simulations project and show that the model can predict the total mass of a subhalo with high accuracy: more than 99% of the subhalos have a predicted mass within 0.2 dex of their true value. The networks exhibit surprising extrapolation properties, being able to accurately predict the total mass of any type of subhalo containing any kind of galaxy at any redshift from simulations with different cosmologies, astrophysics models, subgrid physics, volumes, and resolutions, indicating that the network may have found a universal relation. We then use different methods to find equations that approximate the relation found by the networks and derive new analytic expressions that predict the total mass of a subhalo from its radius, velocity dispersion, and maximum circular velocity. We show that in some regimes, the analytic expressions are more accurate than the neural networks. The relation found by the neural network and approximated by the analytic equation bear similarities to the virial theorem.

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Wu, Xiaohang, Yelin Huang, Zhenzhen Liu, Weiyi Lai, Erping Long, Kai Zhang, Jiewei Jiang, et al. "Universal artificial intelligence platform for collaborative management of cataracts." British Journal of Ophthalmology 103, no. 11 (September 2, 2019): 1553–60. http://dx.doi.org/10.1136/bjophthalmol-2019-314729.

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PurposeTo establish and validate a universal artificial intelligence (AI) platform for collaborative management of cataracts involving multilevel clinical scenarios and explored an AI-based medical referral pattern to improve collaborative efficiency and resource coverage.MethodsThe training and validation datasets were derived from the Chinese Medical Alliance for Artificial Intelligence, covering multilevel healthcare facilities and capture modes. The datasets were labelled using a three-step strategy: (1) capture mode recognition; (2) cataract diagnosis as a normal lens, cataract or a postoperative eye and (3) detection of referable cataracts with respect to aetiology and severity. Moreover, we integrated the cataract AI agent with a real-world multilevel referral pattern involving self-monitoring at home, primary healthcare and specialised hospital services.ResultsThe universal AI platform and multilevel collaborative pattern showed robust diagnostic performance in three-step tasks: (1) capture mode recognition (area under the curve (AUC) 99.28%–99.71%), (2) cataract diagnosis (normal lens, cataract or postoperative eye with AUCs of 99.82%, 99.96% and 99.93% for mydriatic-slit lamp mode and AUCs >99% for other capture modes) and (3) detection of referable cataracts (AUCs >91% in all tests). In the real-world tertiary referral pattern, the agent suggested 30.3% of people be ‘referred’, substantially increasing the ophthalmologist-to-population service ratio by 10.2-fold compared with the traditional pattern.ConclusionsThe universal AI platform and multilevel collaborative pattern showed robust diagnostic performance and effective service for cataracts. The context of our AI-based medical referral pattern will be extended to other common disease conditions and resource-intensive situations.

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Fives, Christopher J., and Rosemary Flanagan. "A Review of the Universal Nonverbal Intelligence Test (UNIT)." School Psychology International 23, no. 4 (November 2002): 425–48. http://dx.doi.org/10.1177/0143034302234005.

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Booth, Margaret Zoller. "Swazi Concepts of intelligence: The Universal versus the Local." Ethos 30, no. 4 (December 2002): 376–400. http://dx.doi.org/10.1525/eth.2002.30.4.376.

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Potapov, Alexey, and Sergey Rodionov. "Universal empathy and ethical bias for artificial general intelligence." Journal of Experimental & Theoretical Artificial Intelligence 26, no. 3 (April 8, 2014): 405–16. http://dx.doi.org/10.1080/0952813x.2014.895112.

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Reed, Mellissa Testerman, and R. Steve McCallum. "Construct validity of the universal nonverbal intelligence test (UNIT)." Psychology in the Schools 32, no. 4 (October 1995): 277–90. http://dx.doi.org/10.1002/1520-6807(199510)32:4<277::aid-pits2310320406>3.0.co;2-m.

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Gavane, Vaibhav. "A Measure of Real-Time Intelligence." Journal of Artificial General Intelligence 4, no. 1 (March 1, 2013): 31–48. http://dx.doi.org/10.2478/jagi-2013-0003.

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Abstract We propose a new measure of intelligence for general reinforcement learning agents, based on the notion that an agent’s environment can change at any step of execution of the agent. That is, an agent is considered to be interacting with its environment in real-time. In this sense, the resulting intelligence measure is more general than the universal intelligence measure (Legg and Hutter, 2007) and the anytime universal intelligence test (Hernández-Orallo and Dowe, 2010). A major advantage of the measure is that an agent’s computational complexity is factored into the measure in a natural manner. We show that there exist agents with intelligence arbitrarily close to the theoretical maximum, and that the intelligence of agents depends on their parallel processing capability. We thus believe that the measure can provide a better evaluation of agents and guidance for building practical agents with high intelligence.

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A, Shevchenko, and Sosnitsky A. "Universalization of the intelligence definition problem." Artificial Intelligence 26, jai2021.26(1) (June 30, 2021): 10–20. http://dx.doi.org/10.15407/jai2021.01.010.

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The main problem today in the research and development of AI is the lack of a scientific definition of Intelligence, since it is impossible to do something incomprehensible. This fundamentally delegitimizes all developments in this area and science as a whole as a product of exclusively intellectual activity, and any scientific use of the term «Intelligence» in its strict sense is unreasonable. In this paper, this problem is solved by transition to a more general universal paradigm of cognition, which allowed us to deduce the desired definition and universal formalism of Intelligence in its strong sense. Unlike previous publications, the ontology and properties of Intelligence are specified here as necessary components of Intelligence, which are subject to subsequent concretization and materialization in different niches of existence. The results of the work are of both fundamental and applied general scientific importance for all technical and humanitarian applications of Intelligence

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Kratsios, Anastasis. "The Universal Approximation Property." Annals of Mathematics and Artificial Intelligence 89, no. 5-6 (January 22, 2021): 435–69. http://dx.doi.org/10.1007/s10472-020-09723-1.

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AbstractThe universal approximation property of various machine learning models is currently only understood on a case-by-case basis, limiting the rapid development of new theoretically justified neural network architectures and blurring our understanding of our current models’ potential. This paper works towards overcoming these challenges by presenting a characterization, a representation, a construction method, and an existence result, each of which applies to any universal approximator on most function spaces of practical interest. Our characterization result is used to describe which activation functions allow the feed-forward architecture to maintain its universal approximation capabilities when multiple constraints are imposed on its final layers and its remaining layers are only sparsely connected. These include a rescaled and shifted Leaky ReLU activation function but not the ReLU activation function. Our construction and representation result is used to exhibit a simple modification of the feed-forward architecture, which can approximate any continuous function with non-pathological growth, uniformly on the entire Euclidean input space. This improves the known capabilities of the feed-forward architecture.

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Vovk, Vladimir, and Dusko Pavlovic. "Universal probability-free prediction." Annals of Mathematics and Artificial Intelligence 81, no. 1-2 (April 19, 2017): 47–70. http://dx.doi.org/10.1007/s10472-017-9547-9.

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A.I., Shevchenko, and Sosnitsky A.V. "Universalization of the intelligence definition problem." Artificial Intelligence 24, no. 3-4 (December 27, 2019): 27–38. http://dx.doi.org/10.15407/jai2019.03-04.027.

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The main problem today in the research and development of AI is the lack of a scientific definition of Intelligence, since it is impossible to do something incomprehensible. This fundamentally delegitimizes all developments in this area and science as a whole as a product of exclusively intellectual activity, and any scientific use of the term "Intellect" in its strict sense is unreasonable. In this paper, this problem is solved by transition to a more general universal paradigm of cognition, which allowed us to deduce the desired definition and universal formalism of Intelligence in its strong sense. Unlike previous publications, the ontology and properties of Intelligence are specified here as necessary components of Intelligence, which are subject to subsequent concretization and materialization in different niches of existence. The results of the work are of both fundamental and applied general scientific importance for all technical and humanitarian applications of Intelligence.

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Fornasier, Mateus de Oliveira. "ARTIFICIAL INTELLIGENCE AND LABOR." Revista da Faculdade Mineira de Direito 24, no. 47 (June 21, 2021): 396–421. http://dx.doi.org/10.5752/p.2318-7999.2021v24n47p396-421.

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This article studies the impacts that the new economy, resulting from automation, represents for Law. Its hypothesis is that a new social security, based on universal basic income, funded by taxing the use of automation tools, should replace systems based mainly on the employment relationship; and principles related to transparency, explicability and non-discrimination should create obligations for developers and users of AI-powered worker selection tools. Methodology: hypothetical-deductive procedure method, with a transdisciplinary and qualitative approach, and bibliographic review research technique. Results: i) Labor regulation must be planned beyond substitution, focusing on a new economy, in which formal jobs, inserted in a paradigm of social and economic protection, are eroding, and the great challenge will be to protect decent work standards concurrently with the enlargement of dignity for non employed workers; ii) A universal basic income funded by taxes on automation would be interesting, but problematic from the point of view of solidarity in its costing, since the stress of national economies can cause fear on governments. And globally, such taxation, if adopted differently, can cause great tax competition between countries; iii) Transparency obligations are necessary to mitigate bias in hiring tools, but not self-sufficient, because the bias is complex, mainly due to the multiplicity of discriminatory factors and to the opacity of the logic in machine learning.

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Yang, Zheming, and Wen Ji. "Meta measurement of intelligence with crowd network." International Journal of Crowd Science 4, no. 3 (June 13, 2020): 295–307. http://dx.doi.org/10.1108/ijcs-03-2020-0008.

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Purpose The multiple factors of intelligence measurement are critical in intelligent science. The intelligence measurement is typically built as a model based on multiple factors. The different agent is generally difficult to measure because of the uncertainty between multiple factors. The purpose of this paper is to solve the problem of uncertainty between multiple factors and propose an effective method for universal intelligence measurement for the different agents. Design/methodology/approach In this paper, the authors propose a universal intelligence measurement method based on meta-analysis for crowd network. First, the authors get study data through keywords in the database and delete the low-quality data. Second, they compute the effect value by odds ratio, relative risk and risk difference. Then, they test the homogeneity by Q-test and analyze the bias by funnel plots. Third, they select the fixed effect and random effect as a statistical model. Finally, through the meta-analysis of time, complexity and reward, the weight of each factor in the intelligence measurement is obtained and then the meta measurement model is constructed. Findings This paper studies the relationship among time, complexity and reward through meta-analysis and effectively combines the measurement of heterogeneous agents such as human, machine, enterprise, government and institution. Originality/value This paper provides a universal intelligence measurement model for crowd network. And it can provide a theoretical basis for the research of crowd science.

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Harvey, David. "Universal Alienation." tripleC: Communication, Capitalism & Critique. Open Access Journal for a Global Sustainable Information Society 16, no. 2 (May 4, 2018): 424–39. http://dx.doi.org/10.31269/triplec.v16i2.1026.

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This article is part of a debate between David Harvey, Michael Hardt and Toni Negri. It takes Marx’s bicentenary as occasion for an update of his concept of alienation. The paper asks: how are we to interpret universal alienation and from whence does it come? Marx radically reformulated the concept of alienation in the Grundrisse. The humanism of the early Marx can be re-rooted and reconceptualised in the scientific mode proposed in the Grundrisse. In the Grundrisse, the universality of alienation is specific to capitalism’s historical evolution. Today, alienation exists almost everywhere. It exists at work in production, at home in consumption, and it dominates much of politics and daily life. Such trends intensify through the application of information technologies and artificial intelligence. Widespread alienation has resulted in Occupy movements as well as right-wing populism and bigoted nationalist and racist movements. Donald Trump is the President of alienation. The circulation of capital as totality consists of the three key moments of production, circulation and distribution. A lot of contemporary economic struggles are now occurring at the point of realisation rather than at the point of production. Protests are therefore today often expressions of broad-based discontent. Our future is dictated by the need to redeem our debts. Under such conditions democracy becomes a sham. The big question is what forms of social movement can help us get out of the state-finance nexus. The theory of objective alienation along with an understanding of its subjective consequences is one vital key to unlock the door of a progressive politics for the future.

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Lemarchand, Guillermo A., and Jon Lomberg. "Universal Cognitive Maps and the Search for Intelligent Life in the Universe." Leonardo 42, no. 5 (October 2009): 396–402. http://dx.doi.org/10.1162/leon.2009.42.5.396.

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For almost 50 years the Search for Extraterrestrial Intelligence (SETI) research program was pursued under the hypothesis of the universality of the physical laws in the cosmos. The authors call attention to some epistemological issues that make it necessary to seek other aesthetic, spiritual and ethical “cognitive universals.” They propose the participation of a broader community of scholars from natural, social, artistic and humanistic disciplines to explore all the possible “universal cognitive maps” that eventually might favor the detection of extraterrestrial intelligent life.

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Zanaty, E. A., and Ashraf Afifi. "SUPPORT VECTOR MACHINES (SVMs) WITH UNIVERSAL KERNELS." Applied Artificial Intelligence 25, no. 7 (August 2011): 575–89. http://dx.doi.org/10.1080/08839514.2011.595280.

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Della Penna, Giuseppe, Daniele Magazzeni, and Fabio Mercorio. "A universal planning system for hybrid domains." Applied Intelligence 36, no. 4 (June 29, 2011): 932–59. http://dx.doi.org/10.1007/s10489-011-0306-z.

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Gamez, David. "Measuring Intelligence in Natural and Artificial Systems." Journal of Artificial Intelligence and Consciousness 08, no. 02 (April 28, 2021): 285–302. http://dx.doi.org/10.1142/s2705078521500090.

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A systematic understanding of the relationship between intelligence and consciousness can only be achieved when we can accurately measure intelligence and consciousness. In other work, I have suggested how the measurement of consciousness can be improved by reframing the science of consciousness as a search for mathematical theories that map between physical and conscious states. This paper discusses the measurement of intelligence in natural and artificial systems. While reasonable methods exist for measuring intelligence in humans, these can only be partly generalized to non-human animals and they cannot be applied to artificial systems. Some universal measures of intelligence have been developed, but their dependence on goals and rewards creates serious problems. This paper sets out a new universal algorithm for measuring intelligence that is based on a system’s ability to make accurate predictions. This algorithm can measure intelligence in humans, non-human animals and artificial systems. Preliminary experiments have demonstrated that it can measure the changing intelligence of an agent in a maze environment. This new measure of intelligence could lead to a much better understanding of the relationship between intelligence and consciousness in natural and artificial systems, and it has many practical applications, particularly in AI safety.

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Nam Hoon Kang. "Artificial Intelligence and the Right to a Universal Basic Income." MARXISM 21 13, no. 4 (November 2016): 12–34. http://dx.doi.org/10.26587/marx.13.4.201611.001.

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Wu, Xiaohang, Yelin Huang, Zhenzhen Liu, Weiyi Lai, Erping Long, Kai Zhang, Jiewei Jiang, et al. "A universal artificial intelligence platform for collaborative management of cataracts." Lancet 394 (October 2019): S22. http://dx.doi.org/10.1016/s0140-6736(19)32358-x.

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Wilhoit, Brian E., and R. Steve McCallum. "Profile Analysis of the Universal Nonverbal Intelligence Test Standardization Sample." School Psychology Review 31, no. 2 (June 1, 2002): 263–81. http://dx.doi.org/10.1080/02796015.2002.12086155.

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Buckley, J. J. "Sugeno type controllers are universal controllers." Fuzzy Sets and Systems 53, no. 3 (February 1993): 299–303. http://dx.doi.org/10.1016/0165-0114(93)90401-3.

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Guillemin, Pierre. "Universal motor control with fuzzy logic." Fuzzy Sets and Systems 63, no. 3 (May 1994): 339–48. http://dx.doi.org/10.1016/0165-0114(94)90220-8.

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Dai, Rui, Minghao Chen, and Hiroshi Morita. "Fuzzy differential equations for universal oscillators." Fuzzy Sets and Systems 347 (September 2018): 89–104. http://dx.doi.org/10.1016/j.fss.2018.01.013.

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Osherson, Daniel N., Michael Stob, and Scott Weinstein. "A universal method of scientific inquiry." Machine Learning 9, no. 2-3 (July 1992): 261–71. http://dx.doi.org/10.1007/bf00992678.

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Grigoryeva, Lyudmila, and Juan-Pablo Ortega. "Echo state networks are universal." Neural Networks 108 (December 2018): 495–508. http://dx.doi.org/10.1016/j.neunet.2018.08.025.

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Klement, Erich Peter, Radko Mesiar, Fabio Spizzichino, and Andrea Stupňanová. "Universal integrals based on copulas." Fuzzy Optimization and Decision Making 13, no. 3 (February 7, 2014): 273–86. http://dx.doi.org/10.1007/s10700-014-9182-4.

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Kovacs, Kristof, Kate C. Plaisted, and Nicholas J. Mackintosh. "Difficulties differentiating dissociations." Behavioral and Brain Sciences 29, no. 2 (April 2006): 138–39. http://dx.doi.org/10.1017/s0140525x06349035.

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We welcome Blair's argument that the relationship between fluid cognition and other aspects of intelligence should be an important focus of research, but are less convinced by his arguments that fluid intelligence is dissociable from general intelligence. This is due to confusions between (a) crystallized skills and g, and (b) universal and differential constructs.

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Liu, Yu, Qingling Zhu, Guoxin Zhao, and Shuchao Ma. "Three-dimensional loading control of a pneumatic three-universal–prismatic–universal robot." International Journal of Advanced Robotic Systems 18, no. 5 (September 1, 2021): 172988142110449. http://dx.doi.org/10.1177/17298814211044932.

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Multidimensional force loading has been widely used in the fields of component and material testing. The pneumatic-driven multidimensional force loading parallel mechanism can meet the requirements of complex force loading. A three-dimensional loading robot based on a pneumatic three-universal–prismatic–universal parallel mechanism is designed to apply time-varying three-dimensional loads on a target. Based on the principle of vector superposition, inverse and forward kinematics are deduced. A second-order mathematical model of a metal seal pneumatic cylinder driven by a flow proportional valve is established. Based on the immersion and invariance technique, the leakage flow in the cylinder is taken as the interference term and estimated. Meanwhile, because of the strong nonlinearity of the actuator, based on suitable disturbance estimation, the control rate of the system is designed through the sliding mode surface, and the stability of the control algorithm is analyzed on the basis of the Lyapunov stability theory. The experimental results show that the immersion and invariance controller exhibits a better control performance than the proportional–integral–differential controller: the steady-state control mean square error is reduced by approximately 21% on average and the dynamic (0.2 Hz) tracking mean square error is approximately 10.35 N.

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Obradovic, Dragan D., Nebojsa Deniç, and Dragisa V. Obradovic. "Computer Intelligence in Higher Education." BOHR International Journal of Computational Intelligence and Communication Network 1, no. 1 (2022): 10–14. http://dx.doi.org/10.54646/bijcicn.002.

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The role of artificial intelligence (AI) systems is constantly increasing in the creation and production of this knowledge. Software and hardware complexes of universal humanoid intelligence and artificial superintelligence are being created with maximum intensity. Progress in this field in the last 15 years is reflected precisely in the realization that the human intellect does not arise simply from a few methods and techniques for solving problems, schemes, and reasoning mechanisms, but requires the use of specific knowledge depending on the specific problem area. Education, thanks to the application of modern technologies, is no longer a privilege, but a basic human right.

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Deepthi, Godavarthi, and A. Mary Sowjanya. "Query-Based Retrieval Using Universal Sentence Encoder." Revue d'Intelligence Artificielle 35, no. 4 (August 31, 2021): 301–6. http://dx.doi.org/10.18280/ria.350404.

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In Natural language processing, various tasks can be implemented with the features provided by word embeddings. But for obtaining embeddings for larger chunks like sentences, the efforts applied through word embeddings will not be sufficient. To resolve such issues sentence embeddings can be used. In sentence embeddings, complete sentences along with their semantic information are represented as vectors so that the machine finds it easy to understand the context. In this paper, we propose a Question Answering System (QAS) based on sentence embeddings. Our goal is to obtain the text from the provided context for a user-query by extracting the sentence in which the correct answer is present. Traditionally, infersent models have been used on SQUAD for building QAS. In recent times, Universal Sentence Encoder with USECNN and USETrans have been developed. In this paper, we have used another variant of the Universal sentence encoder, i.e. Deep averaging network in order to obtain pre-trained sentence embeddings. The results on the SQUAD-2.0 dataset indicate our approach (USE with DAN) performs well compared to Facebook’s infersent embedding.

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Journal articles on the topic 'Universal intelligence'

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