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With the tremendous increase in human population the demand of milk and meat is also increasing. This necessitates the centralization of animal husbandry sector both in developed and developing countries. More production has to be taken from fewer animals which seem to be possible only with the usage of advanced technologies. Earlier this seems to be impractical due to non-availability of such technologies to the farmers. Nowadays, computational power is very cheap and easily available. Artificial intelligence is one such field of computer science devoted to creating computing machines and systems that perform operations analogous to human learning and decision-making. Artificial intelligence includes the machine learning, deep learning and predictive analytics. With the implementation of various software and algorithms, the production has been increased from the farm animals along with better management and welfare. This paper illustrates the application domains and maneuvering of artificial intelligence in the animal husbandry sector. [ABSTRACT FROM AUTHOR]

This article, published in the journal Advanced Robotics, focuses on the topic of adaptive motion in animals and machines. It highlights the intelligence of animals and their ability to navigate and thrive in changing environments, supported by a combination of reflexive and cognitive processes. The article discusses how studying these interactions has led to the development of adaptive robotic behaviors, providing insights into animal behavior and natural intelligence. The special issue includes five papers that showcase cutting-edge research in this field, covering topics such as flying robots with flapping wings, quadrupedal locomotion, humanoid robots, musculoskeletal structures, and robotic sensory processing. The authors express their gratitude to the contributors and reviewers for their valuable contributions to the publication. [Extracted from the article]

Relational cognition—the ability to infer relationships that generalize to novel combinations of objects—is fundamental to human and animal intelligence. Despite this importance, it remains unclear how relational cognition is implemented in the brain due in part to a lack of hypotheses and predictions at the levels of collective neural activity and behavior. Here we discovered, analyzed, and experimentally tested neural networks (NNs) that perform transitive inference (TI), a classic relational task (if A > B and B > C, then A > C). We found NNs that (i) generalized perfectly, despite lacking overt transitive structure prior to training, (ii) generalized when the task required working memory (WM), a capacity thought to be essential to inference in the brain, (iii) emergently expressed behaviors long observed in living subjects, in addition to a novel order-dependent behavior, and (iv) expressed different task solutions yielding alternative behavioral and neural predictions. Further, in a large-scale experiment, we found that human subjects performing WM-based TI showed behavior inconsistent with a class of NNs that characteristically expressed an intuitive task solution. These findings provide neural insights into a classical relational ability, with wider implications for how the brain realizes relational cognition. Author summary: The ability to infer underlying relationships in the world—or "relational cognition"—is fundamental to both human and animal intelligence. Despite this importance, it remains generally unknown how relational abilities are realized in the brain. Addressing this lack of knowledge requires generating testable hypotheses regarding the activity of neurons when relationships are inferred. In this study, we discovered artificial neural networks that perform transitive inference (TI), a classic relational task that has the form "if A > B and B > C, then A > C." We found that these neural networks show behavioral patterns that match those seen in humans and animals performing TI, and, further, performed TI in different ways, thereby providing different testable hypotheses regarding neural activity. We also conducted a large-scale behavioral study in humans, which suggested systematic differences in biological accuracy across the neural networks. These findings provide new hypotheses and insights regarding how TI, and relational abilities more generally, can be realized in the brain. [ABSTRACT FROM AUTHOR]

Scientists have discovered that sperm whales may have a more complex system of communication than previously known. Researchers have classified the clicking sounds made by sperm whales into roughly two dozen distinct phrases, or codas. However, a recent study suggests that these interactions might be more comparable to a duet between veteran jazz musicians, indicating that sperm whales have the capacity to convey a much richer amount of information. The research is part of Project CETI, an initiative that aims to explore cross-species communication using advances in artificial intelligence and animal monitoring tools. While some scientists are skeptical about the possibility of deciphering whale communication, others believe that this research provides valuable insights into the structure of a species' communication system. [Extracted from the article]