Journal articles: 'Animal intelligence'

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BAIDINS, ANDREJS. "Animal intelligence." Nature 319, no. 6050 (January 1986): 172. http://dx.doi.org/10.1038/319172b0.

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POWERS, R. "Animal intelligence." Nature 320, no. 6058 (March 1986): 104. http://dx.doi.org/10.1038/320104d0.

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FREMLIN, J. H. "Animal intelligence." Nature 316, no. 6031 (August 1985): 760. http://dx.doi.org/10.1038/316760a0.

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Hoag, Hannah. "Animal intelligence." Nature 441, no. 7092 (May 2006): 544–45. http://dx.doi.org/10.1038/nj7092-544a.

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Caryl, P. G. "Animal intelligence." Behaviour Research and Therapy 25, no. 1 (1987): 78. http://dx.doi.org/10.1016/0005-7967(87)90135-5.

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Cheney, D. "ANIMAL BEHAVIOR:Only Unthinking Intelligence?" Science 283, no. 5400 (January 15, 1999): 333. http://dx.doi.org/10.1126/science.283.5400.333.

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Lattal, Kennon A. "Where is “Animal Intelligence”?" Behavior Analyst 15, no. 1 (April 1992): 85–87. http://dx.doi.org/10.1007/bf03392590.

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Poli, M. D. "Animal learning and intelligence." Human Evolution 3, no. 6 (December 1988): 487–502. http://dx.doi.org/10.1007/bf02436334.

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Bruce, Darryl. "Puzzling Over Animal Intelligence." Contemporary Psychology: A Journal of Reviews 42, no. 10 (October 1997): 879–82. http://dx.doi.org/10.1037/000083.

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Gutiérrez Luna, Víctor Hugo, and Juan Reyes Juárez. "¿Hay realmente inteligencia animal? Una revisión filosófica." Sincronía XXV, no. 80 (July 3, 2021): 225–47. http://dx.doi.org/10.32870/sincronia.axxv.n80.11b21.

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In the context of philosophical research on animal intelligence, there are different traditions that deny that nonhuman animals are intelligent. In this article we mention some of these traditions, such as Cartesian mechanism and behaviorism. However, we will focus our attention on the proposals of the analytical philosophers John McDowell and Donald Davidson as representative of this philosophical tradition. His main idea is that by not having a language like that of human beings, the rest of the animals cannot be rational and, therefore, not intelligent either. Our position is that such an analytical tradition flatly ignores the scientific and philosophical evidence against it. We will give some relevant data in favor of animal intelligence. In addition, we will give an account of a trend that is manifested with increasing force among ethologists according to which there is a continuity between animal and human intelligence, considering the latter as the result of an evolutionary process and, therefore, as a result of a series of skills acquired by different species at some point in their formation.

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Bylieva, Daria. "Artificial Intelligence as an Intermediary Between animals and Humans." Ideas and Ideals 16, no. 2-1 (June 26, 2024): 102–20. http://dx.doi.org/10.17212/2075-0862-2024-16.2.1-102-120.

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The development of technology has changed the position of animals in the modern world in various aspects. However, only the achievements of artificial intelligence in the field of natural languages indicated the possibility of reaching a new level of understanding and relationship with animals. Modern technologies have made it possible to isolate and fi x animal sounds and collect a huge array of audio and video data, and the experience of translation, even in the absence of parallel texts, has indicated the potential for using artificial intelligence to analyze animal sounds. Despite numerous difficulties, including those associated with the difference in the worldview of animals and humans, there are already precedents for translation from the language of animals. The article analyzes the possibilities of using artificial intelligence in conditions of limited data and its current use in the field of animal communication. If for domestic and farm animals, researchers rely on the interpretation of meanings or emotions, then for wild animals, scientists compare sounds and behavior, and rely on the potential of artificial intelligence in solving unstructured problems. Although a number of recent studies report high reliability of “translation” from the language of animals, the very possibility of testing the effectiveness is difficult. Nevertheless, the accelerating emergence of new solutions that facilitate the recognition of the voices of specific animals, the classification of sounds and actions of different animals, etc., indicate the possibility of a qualitative leap in the understanding of animals in the near future. Success in the field of interpretation of animal sounds can lead not only to progress in a large number of areas related to the animal world, but also to a change in the status and position of the animal. At the same time, the achievements raise ethical questions related to the possibility of using new technologies to the detriment of animals and people.

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Miller, Ralph R., and Francisco Arcediano. "Differentiating robotic behavior and artificial intelligence from animal behavior and biological intelligence: Testing structural accuracy." Behavioral and Brain Sciences 24, no. 6 (December 2001): 1070–71. http://dx.doi.org/10.1017/s0140525x01430123.

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We emphasize the feature of Webb's presentation that bears most directly on contemporary research with real animals. Many neuroscience modelers erroneously conclude that a model that performs like an animal must have achieved this goal through processes analogous with those used by the animal. A simulation failure justifies rejecting a model, but success does not justify acceptance. However, an important benefit of models, successful or otherwise, is to stimulate new research.

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Brown, Deborah. "Animal Automatism and Machine Intelligence." Res Philosophica 92, no. 1 (2015): 93–115. http://dx.doi.org/10.11612/resphil.2015.92.1.2.

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VASYLKIVSKYI, Mikola, Ganna VARGATYUK, and Olga BOLDYREVA. "INTELLIGENT RADIO INTERFACE WITH THE SUPPORT OF ARTIFICIAL INTELLIGENCE." Herald of Khmelnytskyi National University. Technical sciences 217, no. 1 (February 23, 2023): 26–32. http://dx.doi.org/10.31891/2307-5732-2023-317-1-26-32.

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The peculiarities of the implementation of the 6G intelligent radio interface infrastructure, which will use an individual configuration for each individual subscriber application and flexible services with lower overhead costs, have been studied. A personalized infrastructure consisting of an AI-enabled intelligent physical layer, an intelligent MAC controller, and an intelligent protocol is considered, followed by a potentially novel AI-based end-to-end (E2E) device. The intelligent controller is investigated, in particular the intelligent functions at the MAC level, which may become key components of the intelligent controller in the future. The joint optimization of these components, which will provide better system performance, is considered. It was determined that instead of using a complex mathematical method of optimization, it is possible to use machine learning, which has less complexity and can adapt to network conditions. A 6G radio interface design based on a combination of model-driven and data-driven artificial intelligence is investigated and is expected to provide customized radio interface optimization from pre-configuration to self-learning. The specifics of configuring the network scheme and transmission parameters at the level of subscriber equipment and services using a personalized radio interface to maximize the individual user experience without compromising the throughput of the system as a whole are determined. Artificial intelligence is considered, which will be a built-in function of the radio interface that creates an intelligent physical layer and is responsible for MAC access control, network management optimization (such as load balancing and power saving), replacing some non-linear or non-convex algorithms in receiver modules or compensation of shortcomings in non-linear models. Built-in intelligence has been studied, which will make the 6G physical layer more advanced and efficient, facilitate the optimization of structural elements of the physical layer and procedural design, including the possible change of the receiver architecture, will help implement new detection and positioning capabilities, which, in turn, will significantly affect the design of radio interface components. The requirements for the 6G network are defined, which provide for the creation of a single network with scanning and communication functions, which must be integrated into a single structure at the stage of radio interface design. The specifics of carefully designing a communication and scanning network that will offer full scanning capabilities and more fully meet all key performance indicators in the communications industry are explored.

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Raghuwanshi, Shudhanshu, Shubham Sharma, Sakshi Singh, and Shubham Kumar. "APPLICATION OF ARTIFICIAL INTELLIGENCE IN WILDLIFE DISEASE SURVEILLANCE." Journal of Nonlinear Analysis and Optimization 13, no. 01 (2023): 54–59. http://dx.doi.org/10.36893/jnao.2022.v13i02.054-059.

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Artificial intelligence (AI) is any mainframe or computer system capable of performing equally or better than a human in all situations. The use of AI has been adopted by a wide range of organizations including healthcare, industry, commerce, education, tourism, animal husbandry and conservation. AI has the advantage of being a valuable tool for animal management and conservation. Currently, AI is more of a priority in animal tracking than supernatural resources because there is no human capacity and there is a limit to which human AI predators work. Many AI tools have been established to manage livestock and wildlife. AI tools make tracking animals easier. A.I. AI applications have the potential to revolutionize the prediction and diagnosis of animal diseases, thereby improving animal health by improving disease management. The main focus of the research study is to investigate the application of artificial intelligence for prediction and diagnosis of animal diseases through comprehensive literature review.

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Thorndike, E. L. "Animal intelligence: An experimental study of the associate processes in animals." American Psychologist 53, no. 10 (1998): 1125–27. http://dx.doi.org/10.1037/0003-066x.53.10.1125.

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Kistler, John M. "Hunting, Animal Intelligence, and Other Works." Reference Librarian 41, no. 86 (July 14, 2004): 133–42. http://dx.doi.org/10.1300/j120v41n86_12.

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S, Kiruthika, Sakthi P, Sanjay K, Vikraman N, Premkumar T, Yoganantham R, and Raja M. "Smart Agriculture Land Crop Protection Intrusion Detection Using Artificial Intelligence." E3S Web of Conferences 399 (2023): 04006. http://dx.doi.org/10.1051/e3sconf/202339904006.

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Human-wildlife conflict is the term used to describe when human activity results in a negative outcome for people, their resources, wild animals, or their habitat. Human population growth encroaches on wildlife habitat, resulting in a decrease in resources. In particular habitats, there are numerous forms of human and domesticated animal death or injury as a result of conflict. Farmers and the animals that invade farmland suffer greatly as a result. Our project’s primary objective is to lessen human-animal conflict and loss. The embedded system and image processing technique are utilized in the project. Python is used to perform image processing techniques like segmentation, statistical and feature extraction using expectation maximization, and classification using CNN. The classification is used to determine whether the land is empty or if animals are present. A buzzer sound is produced, a light electric current is passed to the fence, and a message alerting the farmer to the animal’s entry into the farmland is transmitted. This prevents the animal from entering the field and enables the landowner to take the necessary steps to get the animal back to the forest. The result is serially sent to the controller broad from the control board.

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Patel, Hrishitva, and Adil Sana. "Role of Computer Science (Artificial Intelligence) In Poultry Management." Devotion Journal of Community Service 3, no. 12 (October 25, 2022): 2068–88. http://dx.doi.org/10.36418/dev.v3i12.250.

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The precise control of animals is the focus of a new strategy to enhance animal welfare in the poultry industry. We notice that good welfare circumstances significantly impact the health of the birds and the quality of the poultry products, which affects economic effectiveness in the production of poultry. An innovation that can aid farmers in more successfully controlling the environment and birds' health is using technology solutions in various animal production systems. Additionally, as public concern over chicken breeding and welfare increases, resolutions are being developed to improve control and monitoring in this area of animal agriculture. PLF (precision livestock farming) uses various techniques to gather real-time data about birds. By spotting diseases and stressful conditions in the early stages and enabling action to be taken swiftly enough to avoid the negative impacts, PLF can assist prevent reducing animal wellbeing. To enhance precision livestock farming, this review links the potential uses of the most recent technology to monitor laying hens and broilers

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Sliwa, Julia. "Toward collective animal neuroscience." Science 374, no. 6566 (October 22, 2021): 397–98. http://dx.doi.org/10.1126/science.abm3060.

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Glock, Hans-Johann. "Agency, Intelligence and Reasons in Animals." Philosophy 94, no. 04 (September 2, 2019): 645–71. http://dx.doi.org/10.1017/s0031819119000275.

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AbstractWhat kind of activity are non-human animals capable of? A venerable tradition insists that lack of language confines them to ‘mere behaviour’. This article engages with this ‘lingualism’ by developing a positive, bottom-up case for the possibility of animal agency. Higher animals cannot just act, they can act intelligently, rationally, intentionally and for reasons. In developing this case I draw on the interplay of behaviour, cognition and conation, the unduly neglected notion of intelligence and its connection to rationality, the need to recognize that reasons are objective conditions, and the difference between the ability to act for reasons and the capacity to reflect on reasons.

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HUANG, Yun, Liang CHENG, Yili QU, Qingbai ZHAO, and Zhijin ZHOU. "Animal innovation: The simplest flash of intelligence." Advances in Psychological Science 25, no. 5 (2017): 799. http://dx.doi.org/10.3724/sp.j.1042.2017.00799.

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Jellinger, K. A. "Comparative Cognition: Experimental Exploration of Animal Intelligence." European Journal of Neurology 14, no. 1 (January 2007): e53-e53. http://dx.doi.org/10.1111/j.1468-1331.2006.01600.x.

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Jellinger, K. A. "Comparative Cognition: Experimental Exploration of Animal Intelligence." European Journal of Neurology 14, no. 7 (July 2007): e34-e34. http://dx.doi.org/10.1111/j.1468-1331.2007.01840.x.

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Darwiche, Adnan. "Human-level intelligence or animal-like abilities?" Communications of the ACM 61, no. 10 (September 26, 2018): 56–67. http://dx.doi.org/10.1145/3271625.

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Matzel, Louis D., and Stefan Kolata. "Selective attention, working memory, and animal intelligence." Neuroscience & Biobehavioral Reviews 34, no. 1 (January 2010): 23–30. http://dx.doi.org/10.1016/j.neubiorev.2009.07.002.

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Mackintosh, N. J. "Approaches to the study of animal intelligence." British Journal of Psychology 79, no. 4 (November 1988): 509–25. http://dx.doi.org/10.1111/j.2044-8295.1988.tb02749.x.

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Delsol, Michel. "Mémoire, conscience, intelligence dans le règne animal ?" Laval théologique et philosophique 62, no. 1 (September 19, 2006): 81–90. http://dx.doi.org/10.7202/013574ar.

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R, MRS ARUNAPRIYA. "Animal Detection based Smart Farming in Animal Repellent Using AI and Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (May 12, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem33888.

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The combination of artificial intelligence (AI) and deep learning in agriculture has ushered in a new era in agriculture with new solutions designed to solve many problems. This paper presents an animal killing system for smart agriculture that uses artificial intelligence and deep learning to reduce the growing problem of animal damage. As the world population continues to grow, increasing food availability is important; Therefore, it is critical to protect crops from wild animals and pests. Deforestation due to livestock farming has become one of the largest human-wildlife conflicts due to human interference with habitats and deforestation. Wild animals can kill farmers working in the fields, causing major crop losses. Farmers suffered huge crop losses due to wild animals such as elephants, wild boars and deer attacking agriculture. Protecting crops from wild animals is one of the biggest concerns of today's farmers. There are many ways to solve this problem, both lethal (such as shooting and trapping) and non-lethal. (such as railings, pesticides, organic matter, netting or electric fencing). The sensor rotates around the lens and uses DCNN software to detect the target. If an animal is found, it sends a message to the Animal Repellent Module with information on the type of ultrasound that should be produced based on the animal. The development of drones with controlled flight control, as well as lightweight and powerful hyperspectral snapshot cameras that can be used to calculate crop biomass growth and fertilization status, responds to complex agricultural management strategies. KEYWORDS: Animal detection, VGG-Net, Bi-LSTM, convolutional neural network, activity recognition, video surveillance, wild animal monitoring, alert system.

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Morota, Gota, Dong Ha, and James Chen. "19 How can Artificial Intelligence Accelerate Phenotyping Efforts in Animal Breeding?" Journal of Animal Science 100, Supplement_3 (September 21, 2022): 11–12. http://dx.doi.org/10.1093/jas/skac247.020.

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Abstract With the development of high-throughput technologies, biology has become a large-scale and data-rich field, especially for genomics and phenomics. Concurrent with this expansion in the availability of high-throughput technologies, their use in the livestock sectors has accordingly increased. This expansion has occurred most notably in the field of animal breeding, where high-throughput technologies hold promise as a means for more efficiently connecting animal phenotypes with pedigree and genomics to drive genetic improvement of production and health-related traits. The rate of genetic gain is closely related to the quantity and quality of phenotyping data. However, there are still considerable manual tasks that are involved in phenotyping processes. Precision livestock farming or smart farming uses sensing technology that is supported by artificial intelligence and machine learning to monitor morphometric changes in animal growth dynamics and animal activity status. In particular, the development of two sensing technologies, computer vision and wearable sensor systems, plays a pivotal role in accelerating phenotyping efforts by providing non-intrusive measurements of animals with high temporal and spatial resolution. The presentation will provide a recent update on current approaches to artificial intelligence and machine learning in computer vision and wearable sensor systems to monitor the body mass and behaviors of animals.

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GIRISH, P. S., SANTHOSH K, KARTIKEYA K, PRATHVI PALEKAR, HARIKRISHNA C. H, and SURESH RATHOD. "Artificial intelligence based muzzle recognition technology for individual identification of animals." Indian Journal of Animal Sciences 90, no. 7 (October 29, 2020): 1070–73. http://dx.doi.org/10.56093/ijans.v90i7.106684.

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India is witnessing raising interest in animal identification and traceability in recent years. Identification and tagging of productive bovines across India with the internationally accepted 12 digit unique animal identification number using bar coded ear tags under the national program of Department of Animal Husbandry and Dairying, Government of India has brought about visible change in the mindset of the stakeholders. Country is realizing the benefits of the system and steadily embracing it. But the animal identification verification system is lacking in the country and this gap can be filled by the artificial intelligence based muzzle identification technique reported in this work. Field test indicated 98% successful identification of all accepted images and 100% successful identification of all test animals. None of the images were cross assigned to any other individual. Mobile based operations without requirement of any consumables and laboratory test makes the technique field-friendly. System can be handy to agencies involved in animal identification and traceability in India and abroad.

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Sara, Dem Vi, MDD Maharani, Hafiza Farwa Amin, and Yaya Sudarya Triana. "Application of Artificial Intelligence in Modern Ecology for Detecting Plant Pests and Animal Diseases." International Journal of Quantitative Research and Modeling 2, no. 2 (June 6, 2021): 83–90. http://dx.doi.org/10.46336/ijqrm.v2i2.149.

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Climate change could lead to an increase in diseases in plants and animals. Plant pathogens have caused devastating production losses, such as in tropical countries. The development of algorithms that match the accuracy of plant and animal disease detection in predicting the toxicity of substances has continued through a massive database. Data and information from 10,000 substances from more than 800,000 animal tests have been carried out to generate the algorithms. Plant and animal disease detection using artificial intelligent in the modern ecological era is important and needed. Diseases in animals are still found in several Ruminant-Slaughterhouses. The purpose of the study is to identify the leverage attributes for using of Artificial Intelligent (AI) in detecting plant pests and animal diseases. The use of Multidimensional Scaling (MDS) produces a leverage attribute for the use of AI in detecting plant pests and animal diseases. The results showed that leverage attributes found were: Prediction of the presence of proteins structures produced by pathogens with a Root Mean Square (RMS) value of 4.5123; and Plant and Animal Disease Data will be opened with an RMS value of 4.2555. The findings of this study in the real world are to produce the development of smart agricultural applications in detecting plant pests and animal diseases as an early warning system. In addition, the application is also useful for eco-tourism managers who have a natural close relationship with plants and animals, so that ecological security in the modern ecological era, can be better maintained.

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M. Mijwil, Maad, Oluwaseun Adelaja, Amr Badr, Guma Ali, Bosco Apparatus Buruga, and Pramila Pudasaini. "Innovative Livestock: A Survey of Artificial Intelligence Techniques in Livestock Farming Management." Wasit Journal of Computer and Mathematics Science 2, no. 4 (December 31, 2023): 99–106. http://dx.doi.org/10.31185/wjcms.206.

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Modern technology has recently become a meaningful part of all life sectors, as software, sensors, smart machines, and expert systems are successfully integrated into the physical environment. This technology relies in its work on artificial intelligence techniques to make the right decisions at the right time. These technologies have a significant role in improving productivity, product quality, and industry outputs by significantly reducing human labour and errors that humans may cause. Artificial intelligence techniques are increasingly being integrated into animal husbandry and animal revolution management because they provide advantages and means that serve agriculturalists. These techniques monitor the emotional state of animals, milk production and herd management, feeding habits, the movement of animals, and their health status. AI-powered sensors can monitor the health of livestock and detect early signs of illness or stress to which they are exposed. Also, these techniques contribute to assisting agriculturalists in customising feeding programs, reducing waste, and improving product quality. This article will discuss the role of artificial intelligence techniques in animal control, farm management, disease surveillance, and sustainable resource optimisation practices.

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Thomas, Evan. "Descartes on the Animal Within, and the Animals Without." Canadian Journal of Philosophy 50, no. 8 (November 2020): 999–1014. http://dx.doi.org/10.1017/can.2020.44.

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AbstractDescartes held that animals are material automata without minds. However, this raises a puzzle. Descartes’s argument for this doctrine relies on the claims that animals lack language and general intelligence. But these claims seem compatible with the view that animals have minds. As a solution to this puzzle, I defend what I call the introspective-analogical interpretation. According to this interpretation, Descartes employs introspection to show that certain human behaviors do not depend on thought but rather on automatic bodily processes. Descartes then argues that animal behavior resembles only those behaviors that are automatic in humans. Analogy thus supports the view that the behaviors of animals do not depend on thought but are, rather, automatic. And if animal behavior is automatic, then animals are best regarded as automata.

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Pearson, Chris. "Between Instinct and Intelligence: Harnessing Police Dog Agency in Early Twentieth-Century Paris." Comparative Studies in Society and History 58, no. 2 (March 29, 2016): 463–90. http://dx.doi.org/10.1017/s0010417516000141.

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AbstractThis article analyzes the introduction of police dogs in early twentieth-century Paris, which formed part of the transnational extension of police powers and their specialization. Within a context of widespread fears of crime and new and contested understandings of animal psychology, police officers, journalists, and canophiles promoted the dogs as inexpensive yet effective agents who could help the police contain the threat posed by criminals. This article responds to a growing number of studies on nonhuman agency by examining how humans in a particular place and time conceptualized and harnessed animal abilities. I argue that while nonhuman agency is an illuminating and important analytical tool, there is a danger that it might become monolithic and static. With these concerns in mind, I show how examining historical actors' conceptualizations of animal abilities takes us closer to the historical stakes and complexities of mobilizing purposeful and capable animals, and provides a better understanding of the constraints within which animals act. Attitudes toward police dogs were entwined with broader discussions of human and animal intelligence. Concerns that dogs' abilities and intelligence were contingent and potentially reversible qualities resembled contemporary biomedical fears that base instincts, desires, and impulses could overwhelm human intelligence and morality, resulting in individual and collective degeneration. To many, it seemed that police dogs' intelligence had not tamed their aggressive instincts, and these worries partly explain the demise of the first wave of police dogs in Paris after World War I.

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Gong, Yutang. "Animal speech and singing synthesis model based on So-VITS-SVC." Applied and Computational Engineering 68, no. 1 (June 6, 2024): 165–70. http://dx.doi.org/10.54254/2755-2721/68/20241430.

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Currently, when researchers in deep learning and neural network technology have made significant progress, the author makes a new bold attempt to apply the technical principles of speech and singing synthesis with artificial intelligence to the field of animal speech and singing synthesis, using So-VITS-SVC4.0 framework, which was originally designed for human voice synthesis. Taking dogs as an example of a species and putting datasets of their sounds to use, the author is committed to capturing its sound characteristics and vocalization through model training and generating synthetic sounds with a high degree of similarity. This research may not only contribute to a deeper understanding of how animals communicate, but also open up new possibilities for animal sound art and music creation. With the continuous progress and improvement of technology, synthetic animal speech and singing by artificial intelligence may play an increasingly important role in zoological research and entertainment, bringing new perspectives and possibilities for communication between humans and animals.

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Strickland, Eliza. "Special report : Can we copy the brain? - From animal intelligence to artificial intelligence." IEEE Spectrum 54, no. 6 (June 2017): 40–45. http://dx.doi.org/10.1109/mspec.2017.7934230.

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Sakamoto, Naoaki, and Takahisa Murata. "Analytical technologies of animal behavior using artificial intelligence." Folia Pharmacologica Japonica 157, no. 2 (2022): 156. http://dx.doi.org/10.1254/fpj.21111.

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Garrison, M. E. Betsy, Annie Daniel, Jacquelyn Wiersma-Mosley, Cathy Williams, Mellissa Crosswhite, and Isabelle Caldwell. "71 Infusing Animal Science Curricula with Cultural Intelligence." Journal of Animal Science 100, Supplement_1 (March 8, 2022): 45–46. http://dx.doi.org/10.1093/jas/skac028.085.

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Abstract Increasingly, universities across the United States are expecting their undergraduate programs to meet, not only, discipline-specific, knowledge content learning outcomes, but also, soft skills and general education objectives, as well. It is not unusual today that outcomes about diversity, equity and inclusion (DEI) are found in either or both program-level and general education learning objectives, including capstone courses. Cultural intelligence is an effective approach to accomplish DEI goals. The purpose of the presentation is two-fold: (1) to discuss the empirical evidence about increasing cultural intelligence in academic programs outside of animal science and (2) the transference of that work into animal science. Curricula to foster cultural intelligence among students includes focusing on deep cultural awareness (implicit biases, privileges and prejudices), diversity education (guest speakers, textbooks, documentaries), and high-impact educational experiences. One of the best strategies is ensuring that all courses are taught by culturally intelligent educators. Increasing a single aspect of cultural intelligence, particularly cultural awareness, improves the environment for students from underrepresented populations which leads to better retention and graduation rates. Five teaching modules with multiple lessons within each, were developed, based on a five-level, progressive model of cultural intelligence, beginning with cultural awareness and ending with cultural proficiency, to infuse into animal science curricula. The first module comprises a basic understanding of cultural concepts and recognizing cultural implications of behavior. The second module comprises elements of culture and their impact on human behavior, including animal care. The third module comprises an integration of cultural knowledge. The fourth module involves social and economic contexts. The fifth module involves negotiating across culture and an intrinsic desire for inclusivity. The presentation will be interactive: the lessons will be demonstrated; assessments developed shared; and the faculty and graduate teaching assistant training sessions experienced. Today’s employment-ready graduates must be culturally intelligent to be able to think critically and solve complex problems in an increasingly diverse and global workplace.

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Whiten, Andrew, and Carel P. van Schaik. "The evolution of animal ‘cultures’ and social intelligence." Philosophical Transactions of the Royal Society B: Biological Sciences 362, no. 1480 (January 24, 2007): 603–20. http://dx.doi.org/10.1098/rstb.2006.1998.

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Decades-long field research has flowered into integrative studies that, together with experimental evidence for the requisite social learning capacities, have indicated a reliance on multiple traditions (‘cultures’) in a small number of species. It is increasingly evident that there is great variation in manifestations of social learning, tradition and culture among species, offering much scope for evolutionary analysis. Social learning has been identified in a range of vertebrate and invertebrate species, yet sustained traditions appear rarer, and the multiple traditions we call cultures are rarer still. Here, we examine relationships between this variation and both social intelligence—sophisticated information processing adapted to the social domain—and encephalization. First, we consider whether culture offers one particular confirmation of the social (‘Machiavellian’) intelligence hypothesis that certain kinds of social life (here, culture) select for intelligence: ‘you need to be smart to sustain culture’. Phylogenetic comparisons, particularly focusing on our own study animals, the great apes, support this, but we also highlight some paradoxes in a broader taxonomic survey. Second, we use intraspecific variation to address the converse hypothesis that ‘culture makes you smart’, concluding that recent evidence for both chimpanzees and orang-utans support this proposition.

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Dewsbury, Donald A. "Animal intelligence: A construct neither defined nor measured." Behavioral and Brain Sciences 10, no. 04 (December 1987): 661. http://dx.doi.org/10.1017/s0140525x00055047.

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Gibson, Kathleen. "Animal social complexity: Intelligence, culture, and individualized societies." American Journal of Human Biology 16, no. 1 (2003): 102–3. http://dx.doi.org/10.1002/ajhb.10224.

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Moreno Redondo, Rosa María. "Animal Representation in Recent Anglophone Science Fiction: Uplifting and Anthropomorphism in Nnedi Okorafor’s "Lagoon" and Adam Roberts’s "Bête"." Oceánide 12 (February 9, 2020): 78–83. http://dx.doi.org/10.37668/oceanide.v12i.28.

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Science fiction in the last decades has often empowered machines and provided humans with enhanced characteristics through the use of technology (the limits of artificial intelligence and transhumanism are frequent themes in recent narratives), but animal empowerment has also been present through the concept of uplifting, understood as the augmentation of animal intelligence through technology. Uplifting implies providing animals with the capacity to speak and reason like humans. However, it could be argued that such implementation fails to acknowledge animal cognition in favour of anthropomorphized schemes of thought. Humankind’s lack of recognition of different animal types of communication has been portrayed in fiction and often implies the adaptation of the animal Other to human needs and expectations, creating a post-animal that communicates its needs to the reader through borrowed words. The main objective of this article is to analyze the use of uplifting as a strategy to give voice to animals in two science fiction novels written in English, both published in the twenty-first century: Lagoon (2014) by Nigerian-American Nnedi Okorafor and Bête (2014) by British author Adam Roberts. This article examines, from ecocritical and human-animal studies (HAS) perspectives, the differencesand similarities in the exploration of the theme in both novels, which are often related to humankind’s willingness or refusal to regard the Other as equal.

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Neethirajan, Suresh. "Affective State Recognition in Livestock—Artificial Intelligence Approaches." Animals 12, no. 6 (March 17, 2022): 759. http://dx.doi.org/10.3390/ani12060759.

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Farm animals, numbering over 70 billion worldwide, are increasingly managed in large-scale, intensive farms. With both public awareness and scientific evidence growing that farm animals experience suffering, as well as affective states such as fear, frustration and distress, there is an urgent need to develop efficient and accurate methods for monitoring their welfare. At present, there are not scientifically validated ‘benchmarks’ for quantifying transient emotional (affective) states in farm animals, and no established measures of good welfare, only indicators of poor welfare, such as injury, pain and fear. Conventional approaches to monitoring livestock welfare are time-consuming, interrupt farming processes and involve subjective judgments. Biometric sensor data enabled by artificial intelligence is an emerging smart solution to unobtrusively monitoring livestock, but its potential for quantifying affective states and ground-breaking solutions in their application are yet to be realized. This review provides innovative methods for collecting big data on farm animal emotions, which can be used to train artificial intelligence models to classify, quantify and predict affective states in individual pigs and cows. Extending this to the group level, social network analysis can be applied to model emotional dynamics and contagion among animals. Finally, ‘digital twins’ of animals capable of simulating and predicting their affective states and behaviour in real time are a near-term possibility.

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Debauche, Olivier, Meryem Elmoulat, Saïd Mahmoudi, Jérôme Bindelle, and Frédéric Lebeau. "Farm Animals’ Behaviors and Welfare Analysis with IA Algorithms: A Review." Revue d'Intelligence Artificielle 35, no. 3 (June 30, 2021): 243–53. http://dx.doi.org/10.18280/ria.350308.

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Numerous bibliographic reviews related to the use of AI for the behavioral detection of farm animals exist, but they only focus on a particular type of animal. We believe that some techniques were used for some animals that could also be used for other types of animals. The application and comparison of these techniques between animal species are rarely done. In this paper, we propose a review of machine learning approaches used for the detection of farm animals’ behaviors such as lameness, grazing, rumination, and so on. The originality of this paper is matched classification in the midst of sensors and algorithms used for each animal category. First, we highlight the most implemented approaches for different categories of animals (cows, sheep, goats, pigs, horses, and chickens) to inspire researchers interested to conduct investigation and employ the methods we have evaluated and the results we have obtained in this study. Second, we describe the current trends in terms of technological development and new paradigms that will impact the AI research. Finally, we critically analyze what is done and we draw new pathways of research to advance our understanding of animal’s behaviors.

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Oliver, Kelly. "Sexual Difference, Animal Difference: Derrida and Difference “Worthy of Its Name”." Hypatia 24, no. 2 (2009): 54–76. http://dx.doi.org/10.1111/j.1527-2001.2009.01032.x.

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I challenge the age-old binary opposition between human and animal, not as philosophers sometimes do by claiming that humans are also animals, or that animals are capable of suffering or intelligence, but rather by questioning the very category of “the animal” itself. This category groups a nearly infinite variety of living beings into one concept measured in terms of humans—animals are those creatures that are not human. In addition, I argue that the binary opposition between human and animal is intimately linked to the binary opposition between man and woman. Furthermore, I suggest that thinking through animal differences or differences among various living creatures opens up the possibility of thinking beyond the dualist notion of sexual difference and enables thinking toward a multiplicity of sexual differences.

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Li, Xiangtao, Jie Zhang, and Minghao Yin. "Animal migration optimization: an optimization algorithm inspired by animal migration behavior." Neural Computing and Applications 24, no. 7-8 (June 15, 2013): 1867–77. http://dx.doi.org/10.1007/s00521-013-1433-8.

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Nicol, C. J. "Farm animal cognition." Animal Science 62, no. 3 (June 1996): 375–91. http://dx.doi.org/10.1017/s1357729800014934.

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AbstractAlthough there may be task-specific differences in performance between wild and domestic animals, there is no evidence for any generally reduced cognitive capacity in domestic animals. It is not possible to compare intelligence between species or breeds without recognizing the contribution of differences in attention and motivation, and domestic animals often perform better on learning tasks than wild animals because they are less fearful. Considerable flexibility and complexity in behaviour can arise from context-specific decisions that may not require learning. Examples include alarm calling and maternal behaviour in chickens. However, the majority of intelligent behaviour shown by farm animals is dominated by learned associations, sometimes in response to remarkably subtle cues. Seemingly straightforward learning abilities may result in surprising emergent properties. An understanding of these properties may enable us to investigate how farm animals interact socially, and whether they form concepts. Other abilities, such as imitation and the re-organization of spatial information, do not appear to depend on associative learning. The study offarm animal cognition tells us little about the issue of animal consciousness but, none the less, plays an important role in the animal welfare debate. The types of cognitive abilities animals have provide clues as to the types of situations in which (given the benefit of the doubt) they might suffer.

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Bao, Jun, and Qiuju Xie. "Artificial intelligence in animal farming: A systematic literature review." Journal of Cleaner Production 331 (January 2022): 129956. http://dx.doi.org/10.1016/j.jclepro.2021.129956.

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Dewsbury, Donald A. "Celebrating E. L. Thorndike a century after Animal Intelligence." American Psychologist 53, no. 10 (1998): 1121–24. http://dx.doi.org/10.1037/0003-066x.53.10.1121.

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Journal articles on the topic 'Animal intelligence'

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