Ciclul de viață al inteligenței artificiale

Sfetcu, Nicolae Ciclul de viață al inteligenței artificiale. IT & C, 2022, vol. 1, n. 2, pp. 10-26. [Journal article (Paginated)]

[thumbnail of IT&C-1-2-Tehnologia_informatiei-Ciclul_de_viata_al_IA-Nicolae_Sfetcu.pdf]
Preview
Text
IT&C-1-2-Tehnologia_informatiei-Ciclul_de_viata_al_IA-Nicolae_Sfetcu.pdf

Download (456kB) | Preview

English abstract

The life cycle of an AI system includes several interrelated phases, from its design and development (including subphases such as requirements analysis, data collection, training, testing, integration), installation, implementation, operation, maintenance and disposal. Given the complexity of artificial intelligence (and information systems in general), several models and methodologies can be defined to manage this complexity, especially in the design and development phases, such as agile, waterfall or spiral software development , rapid and incremental prototyping. The AI lifecycle defines the phases an organization should follow to take advantage of AI techniques and specifically machine learning models to achieve practical business value.

Romanian abstract

Ciclul de viață al unui sistem al inteligenței artificiale include mai multe faze interdependente, de la proiectarea și dezvoltarea acestuia (inclusiv subfaze precum analiza cerințelor, colectarea datelor, instruire, testare, integrare), instalare, implementare, operare, întreținere și eliminare. Având în vedere complexitatea sistemelor inteligenței artificiale (și în general cele de informații), se pot defini mai multe modele și metodologii pentru a gestiona această complexitate, în special în fazele de proiectare și dezvoltare, cum ar fi dezvoltare de software agilă, cascadă sau spirală, prototipare rapidă și incrementală. Ciclul de viață al inteligenței artificiale definește fazele pe care ar trebui să le urmeze o organizație pentru a profita de tehnicile inteligenței artificiale și în special de modelele de învățare automată pentru a obține valoare practică de afaceri.

Item type: Journal article (Paginated)
Keywords: ciclul de viață, inteligența artificială, proiectare, analiza cerințelor, colectarea datelor, instruire, testare, integrare
Subjects: L. Information technology and library technology > LP. Intelligent agents.
Depositing user: Nicolae Sfetcu
Date deposited: 05 Oct 2023 07:16
Last modified: 05 Oct 2023 07:16
URI: http://hdl.handle.net/10760/44894

References

L.A. Adamic – N. Glance, The political blogosphere and the 2004 US election: divided they blog, Proceedings of the 3rd International Workshop on Link discovery 2005, pp. 36-43

Aslay et al., Maximising the diversity of exposure in a social network, IEEE International Conference on Data Mining 2018, pp. 863-868

Caliskan Islam et al., Semantics derived automatically from language corpora necessarily contain human biases, arXiv preprint arXiv:1608.07187 2016

Carter – H.V. Auken., Small firm bankruptcy, Journal of Small Business Management 2006, 44: pp. 493-512

De Biase, Homo pluralis. Essere umani nell’era tecnologica. Codice, Torino 2016

Garimella et al., Reducing controversy by connecting opposing views, Proceedings of the Tenth ACM International Conference on Web Search and Data Mining 2017, pp. 81-90

Guidotti et al., A survey of methods for explaining black box models, ACM computing surveys (CSUR) 2018, pp. 1-42

Hegselmann – U. Krause, Opinion dynamics and bounded confidence: models, analysis and simulation, «Journal of Artificial Societies and Social Simulation» V (2002) 3

Lowry – G. Macpherson, A blot on the profession, British medical journal (Clinical research ed.) 1988, pp. 657

Pasquale, The black box society, Harvard University Press 2015

Pedreschi et al., Discrimination-aware data mining, Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining 2008, pp. 560-568

Pedreschi et al., Meaningful explanations of Black Box AI decision systems. Proceedings of the 33rd AAAI Conference on Artificial Intelligence 2019, 9780-9784

Plous, The Psychology of Judgment and Decision Making. McGraw-Hill, New York 1993

M.T. Ribeiro et al., “Why should I trust you?” Explaining the predictions of any classifier, Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining 2016, pp. 1135-1144

A.L. Schmidt et al., Polarisation of the vaccination debate on Facebook, National Center for Biotechnology Information 36 (2018) 25, pp. 3606-3612

Sirbu et al., Algorithmic bias amplifies opinion fragmentation and polarisation: A bounded confidence model, «PLoS ONE» 14(3), 2019

Surowiecki, The wisdom of crowds, Anchor Books, New York 2004

Artificial Intelligence for Europe, Communication from the Commission to the European Parliament, the European Council, the Council, the European Economic and social committee and the committee of the Regions, Brussels 2018

Divina Frau-Meigs, Societal costs of “fake news” in the Digital Single Market, Study requested by the IMCO committee, 2018

Žiga TURK, Technology as Enabler of Fake News and a Potential Tool to Combat It, In-Depth Analysis requested by the IMCO committee, 2018

XAI (2019-2024, ERC Advanced Grants 2018) Science and technology for the explanation of AI decision making. https://xai-project.eu/

SoBigData (2015-2024, H2020-Excellent Science Research Infrastructures) Integrated Infrastructure for Social Mining & Big Data Analytics. A research infrastructure at the second stage of “Advanced community”, aggregating 32 partners of 12 EU Countries. http://www.sobigdata.eu/

Humane-AI (2019-2020, H2020-FETFLAG-2018-01 Coordination Action) Toward AI Systems That Augment and Empower Humans by Understanding Us, our Society and the World Around Us. https://www.humane-ai.eu/

Sfetcu, Nicolae (2020). Introducere în inteligența artificială, MultiMedia Publishing, ISBN 978-606-033-659-4, https://www.telework.ro/ro/e-books/introducere-in-inteligenta-artificiala/. Licența CC-BY 3.0


Downloads

Downloads per month over past year

Actions (login required)

View Item View Item