Relevant bibliographies by topics / Artificial intelligence computer programmes

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Academic literature on the topic 'Artificial intelligence computer programmes'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 February 2022

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Journal articles on the topic "Artificial intelligence computer programmes"

1

Ganapathy, Apoorva, and Taposh Kumar Neogy. "Artificial Intelligence Price Emulator: A Study on Cryptocurrency." Global Disclosure of Economics and Business 6, no. 2 (2017): 115–22. http://dx.doi.org/10.18034/gdeb.v6i2.558.

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The cryptocurrency Artificial intelligence price emulator is a software programmed to collect cryptocurrency market data, analyze the data and predict the market price using the collected data. Computer emulators are programmed to mimic and copy behaviors or other software/hardware. The reason for emulation is to get to a particular result as quickly as possible. Machine learning is the ability of computers to read and process data while learning from the data with human interference or influence. This work focused majorly on how cryptocurrency market prices can be emulated using Artificial Intelligence with machine learning abilities. It also looked into the advantages of using the software for crypto investors. Some of which is the reduced time of research, reduction of risk, among others.
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2

Ali, Mohammed. "The Human Intelligence vs. Artificial Intelligence: Issues and Challenges in Computer Assisted Language Learning." International Journal of English Linguistics 8, no. 5 (2018): 259. http://dx.doi.org/10.5539/ijel.v8n5p259.

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In this study, the researcher has advocated the importance of human intelligence in language learning since software or any Learning Management System (LMS) cannot be programmed to understand the human context as well as all the linguistic structures contextually. This study examined the extent to which language learning is perilous to machine learning and its programs such as Artificial Intelligence (AI), Pattern Recognition, and Image Analysis used in much assistive learning techniques such as voice detection, face detection and recognition, personalized assistants, besides language learning programs. The researchers argue that language learning is closely associated with human intelligence, human neural networks and no computers or software can claim to replace or replicate those functions of human brain. This study thus posed a challenge to natural language processing (NLP) techniques that claimed having taught a computer how to understand the way humans learn, to understand text without any clue or calculation, to realize the ambiguity in human languages in terms of the juxtaposition between the context and the meaning, and also to automate the language learning process between computers and humans. The study cites evidence of deficiencies in such machine learning software and gadgets to prove that in spite of all technological advancements there remain areas of human brain and human intelligence where a computer or its software cannot enter. These deficiencies highlight the limitations of AI and super intelligence systems of machines to prove that human intelligence would always remain superior.
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Anki, Prasnurzaki, and Alhadi Bustamam. "Measuring the accuracy of LSTM and BiLSTM models in the application of artificial intelligence by applying chatbot programme." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 1 (2021): 197. http://dx.doi.org/10.11591/ijeecs.v23.i1.pp197-205.

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Python programme contains a question and answer system that derived from data sets that have used and implemented the chatbot in this modern era. where the data collected is in the form of corpuses containing extensive metadata-rich fictional conversations derived from extracted film scripts, commonly called cornell movie dialogue corpus. The various models have been used chatbots in python programmes, and LSTM and BiLSTM models were specifically used in this study. Where the form of accuracy will be reported as a result of the implementation of LSTM and BiLSTM models in the chatbot programme. The programme performance will be influenced by the data from the model selection, because the level of accuracy is determined by the target programme being taken. So this is the main factor that determines which model to choose. Based on considerations required for choosing the programme model, in the end the LSTM and the BiLSTM models are chosen and will be applied to the programme. Based on the LSTM and BiLSTM chatbot programmes that have been tested, it can be concluded that the best parameters come from a pair of BiLSTM chatbots using the BiLTSM model with an average accuracy value of 0.995217.
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Qin, Zhi Zhen, Tasneem Naheyan, Morten Ruhwald, et al. "A new resource on artificial intelligence powered computer automated detection software products for tuberculosis programmes and implementers." Tuberculosis 127 (March 2021): 102049. http://dx.doi.org/10.1016/j.tube.2020.102049.

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5

Horn, Berthold K. P., David Marr, John Hollerbach, et al. "Research in Progress at the Massachusetts Institute of Technology Artificial Intelligence Laboratory." AI Magazine 1, no. 1 (2017): 21. http://dx.doi.org/10.1609/aimag.v1i1.87.

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The MIT AI Laboratory has a long tradition of research in most aspects of Artificial Intelligence. Currently, the major foci include computer vision, manipulation, learning, English-language understanding, VLSI design, expert engineering problem solving, common-sense reasoning, computer architecture, distributed problem solving, models of human memory, programmer apprentices, and human education.
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Pettersen, Lene. "Why Artificial Intelligence Will Not Outsmart Complex Knowledge Work." Work, Employment and Society 33, no. 6 (2018): 1058–67. http://dx.doi.org/10.1177/0950017018817489.

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The potential role of artificial intelligence in improving organisations’ performance and productivity has been promoted regularly and vociferously since the 1960s. Artificial intelligence is today reborn out of big business, similar to the occurrences surrounding big data in the 1990s, and expectations are high regarding AI’s potential role in businesses. This article discusses different aspects of knowledge work that tend to be ignored in the debate about whether or not artificial intelligence systems are a threat to jobs. A great deal of knowledge work concerns highly complex problem solving and must be understood in contextual, social and relational terms. These aspects have no generic nor universal rules and solutions and, thus, cannot be easily replaced by artificial intelligence or programmed into computer systems, nor are they constructed based on models of the rational brain. In this respect, this article draws on philosopher Herbert Dreyfus’ thesis regarding artificial intelligence.
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7

Olson, Eric T. "The Ontological Basis of Strong Artificial Life." Artificial Life 3, no. 1 (1997): 29–39. http://dx.doi.org/10.1162/artl.1997.3.1.29.

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This article concerns the claim that it is possible to create living organisms, not merely models that represent organisms, simply by programming computers (“virtual” strong alife). I ask what sort of things these computer-generated organisms are supposed to be (where are they, and what are they made of?). I consider four possible answers to this question: (a) The organisms are abstract complexes of pure information; (b) they are material objects made of bits of computer hardware; (c) they are physical processes going on inside the computer; and (d) they are denizens of an entire artificial world, different from our own, that the programmer creates. I argue that (a) could not be right, that (c) collapses into (b), and that (d) would make strong alife either absurd or uninteresting. Thus, “virtual” strong alife amounts to the claim that, by programming a computer, one can literally bring bits of its hardware to life.
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8

Kuganesan, Anchana. "Artificial Intelligence Can Improve the Healthcare System." Sciential - McMaster Undergraduate Science Journal, no. 2 (March 29, 2019): 37–39. http://dx.doi.org/10.15173/sciential.v1i2.2097.

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Artificial intelligence (AI) is a computer system used to model human cognitive functions, intelligence, and behaviour. Components include both, a virtual and a physical aspect. Virtual aspects of AI include algorithms and neural networks instilled within the system to execute its assignments. Physical components include the entity in conjunction with a code. 1 AI is currently being developed by Nvidia Corporation, Alphabet, Twilio, Amazon, Micron Technology, Microsoft Corp., Baidu, Intel Corp., Facebook, and Tencent. 2 Expanding AI into the health care system can be beneficial for preventative care, patient safety, and reducing treatment costs for families. AI has proven to be useful in machine learning, thus, it can be programmed to complete specific tasks. By performing tasks such as data interpretation, the amount of time that it takes for a physician to consult patients regarding their results will be reduced. In addition, AI is capable of analyzing medical images to identify tumours and it has previously been used in various other branches of medicine such as neurology and cardiology. Overall, AI has great potential to improve the health care industry in North America and worldwide. However, potential violations while utilizing personal patient data must be addressed whilst modifying this technology.
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9

Abdulllah, Shahino Mah. "Artificial Intelligence (AI) and Its Associated Ethical Issues." ICR Journal 10, no. 1 (2019): 124–26. http://dx.doi.org/10.52282/icr.v10i1.78.

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The adoption of AI in many industries has been regarded by some as a threat to low- and middle-skill workers, as it will drastically cut down reliance on the human workforce. Besides unemployment, there are also concerns about rising economic inequality caused by AI-driven companies. With fewer employees, these companies could gain a disproportionate advantage over conventional companies that still depend on normal, shift-based systems. There is also the issue that some AI bots have achieved the capability to interact with humans and build relationships through conversations. This influential communication could eventually enable these bots to affect human behaviour and possibly trigger certain actions. Significantly, therefore, such intelligent machines are not immune to mistakes and confusion since not all possible examples of real world interaction are covered during their training; this weakness could be manipulated to fulfil certain ends. Also, Al cannot be expected to be entirely fair and neutral, since it is dependent on human programmers, who have their own interests and whims.
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10

ASTLEY, SUE, IAN HUTT, PETER MILLER, et al. "AUTOMATION IN MAMMOGRAPHY: COMPUTER VISION AND HUMAN PERCEPTION." International Journal of Pattern Recognition and Artificial Intelligence 07, no. 06 (1993): 1313–38. http://dx.doi.org/10.1142/s0218001493000649.

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Mammographic screening programmes generate large numbers of highly variable, complex images, most of which are unequivocally normal. When present, abnormalities may be small or subtle. Two processes critical to the success of screening programmes are the perception of potential abnormalities and the subsequent analy-sis of each detected lesion to determine its clinical significance. The consequences of errors are costly, and in many screening centres, films are read by two radiologists in an attempt to reduce errors. The prime objective of our research is to improve the accuracy of the detection and analysis of breast lesions by providing radiologists with computer-aided digital image analysis tools. In this paper we focus on the detection and analysis of mammographic microcalcifications. We describe a philosophy of research aimed at generating useful computer-based aids for radiologists. Firstly, it is necessary to accurately identify specific tasks which are difficult for the human observer. Having correctly identified a problem, appropriate computer vision methods must be developed and their performance evaluated. It is then important to determine effective ways of using such methods to aid radiologists, and it is essential to prove that the effect on radiologists’ performance is entirely beneficial. We present results of experiments to determine factors affecting radiologists’ perception of microcalcifications, and to investigate the effects of attention-cueing on detection performance. Our results show that radiologists’ performance can be significantly improved with the use of prompts generated from automatically-detected microcalcification clusters. We describe a new method for the delineation of mammographic abnormalities based on the analysis of multiple high quality X-ray projections of excised lesions. Biopsy specimens are secured inside a rigid tetrahedron, the edges of which provide a reference frame to which the locations of features can be related. A three-dimensional representation of an abnormality can be formed and rotated to resemble its appearance in the original mammogram.
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