Academic literature on the topic 'Artifical intelligence'

Baruque, Bruno, Bernabe Dorronsoro, José A. Gámez та ін. Advances in Artificial Intelligence: 16th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2015 Albacete, Spain, November ... Springer, 2015.

Korf, Richard. "Artificial Intelligence Search Algorithms." In Algorithms and Theory of Computation Handbook, Second Edition, Volume 2. Chapman and Hall/CRC, 2009. http://dx.doi.org/10.1201/9781584888215-c22.

"Mind and machine—artificial intelligence." In Diving Into the Bitstream. Routledge, 2012. http://dx.doi.org/10.4324/9780203153277-15.

"Computational Intelligence on Medical Imaging with Artificial Neural Networks." In Computational Intelligence in Medical Imaging. Chapman and Hall/CRC, 2009. http://dx.doi.org/10.1201/9781420060614-5.

"Deformable Organisms: An Artificial Life Framework for Automated Medical Image Analysis." In Computational Intelligence in Medical Imaging. Chapman and Hall/CRC, 2009. http://dx.doi.org/10.1201/9781420060614-19.

"Application of Artificial Intelligence to Predictive Microbiology Rosa Marı´a Garcı´a-Gimeno, Ce´sar Herva´s-Martinez,." In Novel Food Processing Technologies. CRC Press, 2004. http://dx.doi.org/10.1201/9780203997277-33.

"of comprehension involved, as well as of processes of production, has been under-taken by cognitive psychologists, and workers in artificial intelligence concerned with the computer simulation of production and comprehension. From the per-spective of CLS, the most important result of work on comprehension is the stress which has been placed upon its active nature: you do not simply ‘decode’ an utter-ance, you arrive at an interpretation through an active process of matching fea-tures of the utterance at various levels with representations you have stored in your long-term memory. These representations are prototypes for a very diverse collection of things – the shapes of words, the grammatical forms of sentences, the typical structure of a narrative, the properties of types of object and person, the expected sequence of events in a particular situation type, and so forth. Some of these are linguistic, and some of them are not. Anticipating later discussion, let us refer to these prototypes collectively as ‘members’ resources’, or MR for short. The main point is that comprehension is the outcome of interactions between the utterance being interpreted, and MR. Not surprisingly, cognitive pyschology and artificial intelligence have given little attention to the social origins or significance of MR. I shall argue later that attention to the processes of production and comprehension is essential to an under-standing of the interrelations of language, power and ideology, and that this is so because MR are socially determined and ideologically shaped, though their ‘common sense’ and automatic character typically disguises that fact. Routine and unselfconscious resort to MR in the ordinary business of discourse is, I shall sug-gest, a powerful mechanism for sustaining the relations of power which ultimately underlie them." In Pragmatics and Discourse. Routledge, 2005. http://dx.doi.org/10.4324/9780203994597-8.

"possible expenditure of whatever resource (time, money, energy . . . ) it takes. Efficiency with respect to relative goals is a matter of striking a balance between degree of achievement and expenditure. In the special case where the expenditure is fixed – say all the time available is going to be spent anyhow – efficiency con-sists in achieving the goal to the highest possible degree. Most discussions of information processing, whether in experimental psycho-logy or in artificial intelligence, have been concerned with the realisation of abso-lute goals. ‘Problem solving’ has become the paradigm of information processing. The problems considered have a fixed solution; the goal of the information-processing device is to find this solution; efficiency consists in finding it at the minimal cost. However, not all cognitive tasks fit this description; many tasks con-sist not in reaching an absolute goal, but in improving on an existing state of affairs. Hence, cognitive efficiency may have to be characterised differently for dif-ferent devices. Simpler information-processing devices, whether natural, such as a frog, or artificial, such as an electronic alarm system, process only very specific informa-tion: for example, metabolic changes and fly movements for frogs, noises and other vibrations for alarm systems. Their information-processing activity consists in mon-itoring changes in the values of a few variables. They could be informally described as engaged in answering a few set questions: ‘Is there a fly-like object within reach?’, ‘Is there a large body moving in the room?’ More complex information-processing devices, by contrast, can define and monitor new variables or formu-late and answer new questions. For the simpler devices, efficiency consists in answering their set questions at the minimal processing cost. Efficiency cannot be so easily defined for more com-plex devices such as human beings. For such devices, efficient information pro-cessing may involve formulating and trying to answer new questions despite the extra processing costs incurred. Formulating and answering specific questions must then be seen as subservient to a more general and abstract goal. It is in relation to this general goal that the efficiency of complex information-processing devices must be characterised. On the general goal of human cognition, we have nothing better to offer than rather trivial speculative remarks. However, these remarks have important and non-trivial consequences. It seems that human cognition is aimed at improving the individual’s knowledge of the world. This means adding more information, infor-mation that is more accurate, more easily retrievable, and more developed in areas of greater concern to the individual. Information processing is a permanent life-long task. An individual’s overall resources for information processing are, if not quite fixed, at least not very flexible. Thus, long-term cognitive efficiency consists in improving one’s knowledge of the world as much as possible given the avail-able resources. What, then, is short-term cognitive efficiency – efficiency, say, in the way your mind spends the next few seconds or milliseconds? This is a more concrete question, and one that is harder to answer. At every moment, many different cog-nitive tasks could be performed, and this for two reasons: first, human sensory." In Pragmatics and Discourse. Routledge, 2005. http://dx.doi.org/10.4324/9780203994597-24.

"he need never have made himself before she spoke. What she expects, rightly, is that her utterance will act as a prompt, making him recall parts of the book that he had previously forgotten, and construct the assumptions needed to understand the allusion. In both these examples Mary makes assumptions about what assumptions are, or will be, manifest to Peter. Peter trusts that the assumptions he spontaneously makes about the church and about Sense and Sensibility, which help him understand Mary’s utterances, are those she expected him to make. To communicate success-fully, Mary had to have some knowledge of Peter’s cognitive environment. As a result of their successful communication, their mutual cognitive environment is enlarged. Note that symmetrical co-ordination and mutual knowledge do not enter into the picture at all. The most fundamental reason for adopting the mutual-knowledge framework, as for adopting the code model, is the desire to show how successful communi-cation can be guaranteed, how there is some failsafe algorithm by which the hearer can reconstruct the speaker’s exact meaning. Within this framework the fact that communication often fails is explained in one of two ways: either the code mech-anism has been imperfectly implemented, or there has been some disruption due to ‘noise’. A noiseless, well-implemented code mechanism should guarantee per-fect communication. In rejecting the mutual-knowledge framework, we abandon the possibility of using a failsafe algorithm as a model of human communication. But since it is obvious that the communication process takes place at a risk, why assume that it is governed by a failsafe procedure? Moreover, if there is one conclusion to be drawn from work on artificial intelligence, it is that most cognitive processes are so complex that they must be modelled in terms of heuristics rather than failsafe algorithms. We assume, then, that communication is governed by a less-than-perfect heuristic. On this approach, failures in communication are to be expected: what is mysterious and requires explanation is not failure but success. As we have seen, the notion of mutual manifestness is not strong enough to salvage the code theory of communication. But then, this was never one of our aims. Instead of taking the code theory for granted and concluding that mutual knowledge must therefore exist, we prefer to look at what kind of assumptions people are actually in a position to make about each other’s assumptions, and then see what this implies for an account of communication. Sometimes, we have direct evidence about other people’s assumptions: for instance, when they tell us what they assume. More generally, because we mani-festly share cognitive environments with other people, we have direct evidence about what is manifest to them. When a cognitive environment we share with other people is mutual, we have evidence about what is mutually manifest to all of us. Note that this evidence can never be conclusive: the boundaries of cogni-tive environments cannot be precisely determined, if only because the threshold between very weakly manifest assumptions and inaccessible ones is unmarked. From assumptions about what is manifest to other people, and in particular about what is strongly manifest to them, we are in a position to derive further,." In Pragmatics and Discourse. Routledge, 2005. http://dx.doi.org/10.4324/9780203994597-22.

Celik, Anil, та Burak Yildirim. "Turkish Profanity Detection Enhanced by Artificial Intelligence". У 2020 28th Signal Processing and Communications Applications Conference (SIU). IEEE, 2020. http://dx.doi.org/10.1109/siu49456.2020.9302119.

Kjellgren, Alexander, Per Kettil, Mats Karlsson та Rasmus Rempling. "Opportunities in Civil Projects with Artificial Intelligence". У IABSE Symposium, Istanbul 2023: Long Span Bridges. International Association for Bridge and Structural Engineering (IABSE), 2023. http://dx.doi.org/10.2749/istanbul.2023.0022.

Chao, Chian-Hsueng. "Ethics Issues in Artificial Intelligence." In 2019 International Conference on Technologies and Applications of Artificial Intelligence (TAAI). IEEE, 2019. http://dx.doi.org/10.1109/taai48200.2019.8959925.

Lukic, Bojan, Jasper Sprockhoff, Alexander Ahlbrecht, Siddhartha Gupta та Umut Durak. "Iterative Scenario-Based Testing in an Operational Design Domain for Artificial Intelligence Based Systems in Aviation". У ASIM Workshop STS/GMMS/EDU 2023. ARGESIM Publisher Vienna, 2023. http://dx.doi.org/10.11128/arep.21.a2108.

Wang, Chi-Shiang, Hung-Chun Chne, and Jung-Hsien Chiang. "Discovering What You Cared by Intelligent Recommender System." In 2019 International Conference on Technologies and Applications of Artificial Intelligence (TAAI). IEEE, 2019. http://dx.doi.org/10.1109/taai48200.2019.8959944.

Terenzi, Benedetta, Valeria Menchetelli, Giacomo Pagnotta, and Ludovica Avallone. "Connection between AI and product design - Potentials and critical issues in the text-to-image software-assisted design experience." In Intelligent Human Systems Integration (IHSI 2024) Integrating People and Intelligent Systems. AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1004511.

Winters, R. Michael, Ankur Kalra, and Bruce N. Walker. "Hearing Artificial Intelligence: Sonification Guidelines & Results From a Case-study in Melanoma Diagnosis." In ICAD 2019: The 25th International Conference on Auditory Display. Department of Computer and Information Sciences, Northumbria University, 2019. http://dx.doi.org/10.21785/icad2019.021.

Lee, Chang-Shing, Mei-Hui Wang, Yi-Lin Tsai, et al. "FML-based Intelligent Agent for Robotic e-Learning and Entertainment Application." In 2019 International Conference on Technologies and Applications of Artificial Intelligence (TAAI). IEEE, 2019. http://dx.doi.org/10.1109/taai48200.2019.8959880.

Mu, Shenglin, Satoru Shibata, Tomonori Yamamoto, Kanya Tanaka, Shota Nakashima, and Tung-kuan Liu. "Experimental Study on Speed Control of Ultrasonic Motor using Intelligent IMC-PID Control." In 2019 International Conference on Technologies and Applications of Artificial Intelligence (TAAI). IEEE, 2019. http://dx.doi.org/10.1109/taai48200.2019.8959888.

Huang, Han-Chun, and Pou-Jen Ku. "Intelligent technology enhances the friendliness of the pharmacy care service : Identification in drug prescription." In 2019 International Conference on Technologies and Applications of Artificial Intelligence (TAAI). IEEE, 2019. http://dx.doi.org/10.1109/taai48200.2019.8959839.