Istnieje nowsza wersja tego artykułu opublikowanego 2024-11-30. Przeczytaj wersję najnowszą.

Czy oni tworzą sztuczną inteligencję? (Re)konstrukcja działania podstawowego w data science

Autor

DOI:

https://doi.org/10.18778/1733-8069.20.4.09

Słowa kluczowe:

społeczne światy, działanie podstawowe, sztuczna inteligencja, data science

Abstrakt

Data science (DS) zajmuje się budowaniem tzw. sztucznej inteligencji, czyli systemów komputerowych automatyzujących zadania na podstawie danych historycznych. Niniejszy artykuł jest pierwszą próbą zbadania DS z zastosowaniem ramy teoretycznej światów społecznych Adele E. Clarke. Głównym celem opracowania jest przedstawienie (re)konstrukcji działania podstawowego na przykładzie świata społecznego DS w Polsce. Refleksja metodologiczna nad tą (re)konstrukcją jest słabo rozwiniętym elementem badań nad światami społecznymi; niniejszy artykuł stara się ten proces wyeksplikować. Podstawą empiryczną jest trzyletnie badanie etnograficzne, przeprowadzone zgodnie z podejściem analizy sytuacyjnej Clarke. Wyniki metodologiczne prezentują niezbędność etnografii opartej na współpracy w (re)konstruowaniu działania podstawowego oraz znaczenie namacalnych elementów jako kluczowych dla zrozumienia tego działania. Substancjalne wyniki koncentrują się na spostrzeżeniu, że osoby zajmujące się data science nie określają swego działania z użyciem pojęcia sztucznej inteligencji.

Pobrania

Brak dostępnych danych do wyświetlenia.

Biogram autora

Remigiusz Żulicki - University of Lodz, Poland

Ph.D., works at the Department of Social Research Methods and Techniques, Institute of Sociology, Faculty of Economics and Sociology, University of Lodz. His research interests include social science research methodology, social worlds/arenas, critical data studies, and digital sociology. He harmonizes panel survey data regarding precarity – CNB-Young project – and investigates illegal trash dumping. He is an open-source and open-science enthusiast. Member of interdisciplinary Generative Artificial Intelligence Team at the University of Lodz.

Bibliografia

Alekseichenko Vladimir (2019a), 10 właściwych pytań przy wdrażaniu uczenia maszynowego, https://biznesmysli.pl/10-wlasciwych-pytan-przy-wdrazaniu-uczenia-maszynowego/ [accessed: 30.04.2019].
Google Scholar

Alekseichenko Vladimir (2019b), The difference between AI vs ML, https://www.linkedin.com/feed/update/urn:li:activity:6501030890754314240/ [accessed: 4.05.2019].
Google Scholar

Anderson Leon (2006), Analytic Autoethnography, “Journal of Contemporary Ethnography”, vol. 35(4), pp. 373–395, https://doi.org/10.1177/0891241605280449
Google Scholar DOI: https://doi.org/10.1177/0891241605280449

Andrus Calvin, Cook Jon, Sood Suresh (2017), Data Science: An Introduction, https://en.wikibooks.org/wiki/Data_Science:_An_Introduction [accessed: 21.03.2018].
Google Scholar

Angrosino Michael (2010), Badania etnograficzne i obserwacje, Warszawa: Wydawnictwo Naukowe PWN.
Google Scholar

Azam Anum (2014), The First Rule of Data Science, “Berkeley Science Review”, 27.04.2014, https://web.archive.org/web/20170922061629/https://berkeleysciencereview.com/article/first-rule-data-science/ [accessed: 26.01.2018].
Google Scholar

Baiju Nt (2014), What is a data scientist? 14 definitions of a data scientist!, https://web.archive.org/web/20171207002047/https://bigdata-madesimple.com/what-is-a-data-scientist-14-definitions-of-a-data-scientist/ [accessed: 11.01.2018].
Google Scholar

Batorski Dominik, Grzywińska Ilona (2018) Three dimensions of the public sphere on Facebook, “Information Communication and Society”, vol. 21(3), pp. 356–374, https://doi.org/10.1080/1369118X.2017.1281329
Google Scholar DOI: https://doi.org/10.1080/1369118X.2017.1281329

Becker Howard S. (1953), Becoming a Marihuana User, “The American Journal of Sociology”, vol. 59(3), pp. 235–242.
Google Scholar DOI: https://doi.org/10.1086/221326

Biecek Przemysław (2015), Pogromcy Danych. Przetwarzanie danych w programie R, https://web.archive.org/web/20161205211608/http://pogromcydanych.icm.edu.pl/ [accessed: 28.12.2016].
Google Scholar

Big Data Borat (2013), @BigDataBorat: Data Science Is Statistics on Mac, https://twitter.com/bigdataborat/status/372350993255518208 [accessed: 19.02.2018].
Google Scholar

Boyd Danah, Crawford Kate (2011), Six Provocations for Big Data, “SSRN Electronic Journal”, s. 1–17, https://doi.org/10.2139/ssrn.1926431
Google Scholar DOI: https://doi.org/10.2139/ssrn.1926431

Cao Longbing (2017), Data Science: A Comprehensive Overview, “ACM Computing Surveys”, vol. 50(3), pp. 1–42, https://doi.org/10.1145/3076253
Google Scholar DOI: https://doi.org/10.1145/3076253

Charmaz Kathy (2006), Constructing grounded theory, London: Sage Publications.
Google Scholar

Clarke Adele E. (1997), A Social Worlds Research Adventure: The Case of Reproductive Science, [in:] Anselm L. Strauss, Juliet Corbin (eds.), Grounded Theory in Practice, Thousand Oaks: Sage Publications, pp. 63–94.
Google Scholar

Clarke Adele E. (2003), Situational Analyses: Grounded Theory Mapping After the Postmodern Turn, “Symbolic Interaction”, vol. 26(4), pp. 553–576.
Google Scholar DOI: https://doi.org/10.1525/si.2003.26.4.553

Clarke Adele E. (2005), Situational Analysis. Grounded Theory After the Postmodern Turn, London: Sage Publications.
Google Scholar

Clarke Adele E. (2015), From Grounded Theory to Situational Analysis. What’s New? Why? How?, [in:] Adele E. Clarke, Carrie Friese, Rachel S. Washburn (eds.), Situational Analysis in Practice. Mapping Research with Grounded Theory, Walnut Creek: Left Coast Press Inc., pp. 84–118.
Google Scholar

Clarke Adele E., Star Susan Leigh (2008), The Social Worlds Framework: A Theory/Method Package, [in:] Edward J. Hackett, Olga Amsterdamska, Michael Lynch, Judy Wajcman (eds.), The Handbook of Science and Technology Studies, Cambridge–London: The MIT Press, pp. 113–158.
Google Scholar

Clarke Adele E., Friese Carrie, Washburn Rachel S. (2015), Introducing Situational Analysis, [in:] Adele E. Clarke, Carrie Friese, Rachel S. Washburn (eds.), Situational Analysis in Practice. Mapping Research with Grounded Theory, Walnut Creek: Left Coast Press Inc., pp. 11–75.
Google Scholar DOI: https://doi.org/10.4324/9781315420134

Clarke Adele E., Friese Carrie, Washburn Rachel S. (2017), Situational Analysis: Grounded Theory After the Interpretive Turn, Los Angeles: Sage Publications.
Google Scholar

Cleveland William S. (2001), Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics, “International Statistical Review”, vol. 69(1), pp. 21–26, https://doi.org/10.1111/j.1751-5823.2001.tb00477.x
Google Scholar DOI: https://doi.org/10.1111/j.1751-5823.2001.tb00477.x

Collins Harry M., Evans Robert (2002), The Third Wave of Science Studies: Studies of Expertise and Experience, “Social Studies of Science”, vol. 32(2), pp. 235–296, https://doi.org/10.1177/0306312702032002003
Google Scholar DOI: https://doi.org/10.1177/0306312702032002003

Conway Drew (2010), The Data Science Venn Diagram, https://web.archive.org/web/20110225163125/http://www.dataists.com/2010/09/the-data-science-venn-diagram/ [accessed: 18.02.2018].
Google Scholar

Crawford Kate (2021), Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence, New Haven: Yale University Press.
Google Scholar DOI: https://doi.org/10.12987/9780300252392

CrowdFlower (2017), 2017 Data Scientist Report, https://visit.crowdflower.com/rs/416-ZBE-142/images/data-scientist-report-dec.pdf [accessed: 11.02.2018].
Google Scholar

Dalton Craig M., Taylor Linnet, Thatcher Jim (2016), Critical Data Studies: A dialog on data and space, “Big Data & Society”, vol. 3(1), https://doi.org/10.1177/2053951716648346
Google Scholar DOI: https://doi.org/10.1177/2053951716648346

Davenport Thomas H., Patil D.J. (2012), Data Scientist: The Sexiest Job of the 21st Century, “Harvard Business Review”, https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century [accessed: 3.10.2016].
Google Scholar

Dean Jeff (2019), Deep Learning to Solve Challenging Problems (Google I/O’19), https://www.youtube.com/watch?v=rP8CGyDbxBY [accessed: 11.06.2019].
Google Scholar

Delapenha Lauren (2017), 42 Essential Quotes by Data Science Thought Leaders, https://www.kdnuggets.com/2017/05/42-essential-quotes-data-science-thought-leaders.html [accessed: 6.02.2018].
Google Scholar

Desai Jules, Watson David, Wang Vincent, Taddeo Mariarosaria, Floridi Luciano (2022), The epistemological foundations of data science: a critical analysis, “SSRN Electronic Journal”, pp. 1–26, https://doi.org/10.2139/ssrn.4008316
Google Scholar DOI: https://doi.org/10.2139/ssrn.4008316

Dijck José van (2014), Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology, “Surveillance and Society”, vol. 12(2), pp. 197–208, https://doi.org/10.24908/ss.v12i2.4776
Google Scholar DOI: https://doi.org/10.24908/ss.v12i2.4776

Donoho David (2017), 50 Years of Data Science, “Journal of Computational and Graphical Statistics”, vol. 26(4), pp. 745–766, https://doi.org/10.1080/10618600.2017.1384734
Google Scholar DOI: https://doi.org/10.1080/10618600.2017.1384734

Doran Derek (2018), Data Scientist, [in:] Laurie A. Schintler, Connie L. McNeely (eds.), Encyclopedia of Big Data, Cham: Springer International Publishing, pp. 1–4, https://doi.org/10.1007/978-3-319-32001-4_61-1
Google Scholar DOI: https://doi.org/10.1007/978-3-319-32001-4_61-1

Elish M.C., Boyd Danah (2018), Situating methods in the magic of Big Data and AI, “Communication Monographs”, vol. 85(1), pp. 57–80, https://doi.org/10.1080/03637751.2017.1375130
Google Scholar DOI: https://doi.org/10.1080/03637751.2017.1375130

Gallopoulos Efstratios, Houstis Elias, Rice J.R. (1994), Computer as thinker/doer: problem-solving environments for computational science, “IEEE Computational Science and Engineering”, vol. 1(2), pp. 11–23, https://doi.org/10.1109/99.326669
Google Scholar DOI: https://doi.org/10.1109/99.326669

Gerson Elihu M. (1983), Scientific Work and Social Worlds, “Knowledge: Creation, Diffusion, Utilization”, vol. 4(3), pp. 357–377.
Google Scholar DOI: https://doi.org/10.1177/107554708300400302

Gold Raymond L. (1958), Roles in Sociological Field Observations, “Social Forces”, vol. 36(3), pp. 217–223, https://doi.org/10.2307/2573808
Google Scholar DOI: https://doi.org/10.2307/2573808

Goodfellow Ian, Bengio Yoshua, Courville Aaron (2016), Deep Learning, http://www.deeplearningbook.org/ [accessed: 5.11.2017].
Google Scholar

Granville Vincent (2014), 16 analytic disciplines compared to data science, https://web.archive.org/web/20140808055923/http://www.datasciencecentral.com/group/resources/forum/topics/16-analytic-disciplines-compared-to-data-science [accessed: 2.01.2017].
Google Scholar

Grommé Francisca, Ruppert Evelyn, Cakici Baki (2018), Data scientists: a new faction of the transnational field of statistics, [in:] Hannah Knox, Dawn Nafus (eds.), Ethnography for a data-saturated world, Manchester: Manchester University Press, pp. 33–61.
Google Scholar DOI: https://doi.org/10.7765/9781526127600.00009

Hughes Everet C. (1958), Men and Their Work, London: The Free Press.
Google Scholar

Hyndman Rob (2014), Am I a data scientist?, https://robjhyndman.com/hyndsight/am-i-a-data-scientist/ [accessed: 5.01.2018].
Google Scholar

Iwasiński Łukasz (2020), Theoretical Bases of Critical Data Studies, “Zagadnienia Informacji Naukowej – Studia Informacyjne”, vol. 58(1A(115A)), pp. 96–109, https://doi.org/10.36702/zin.726
Google Scholar DOI: https://doi.org/10.36702/zin.726

Jarvis Jeremy (2014), @jeremyjarvis: A Data Scientist Is a Statistician Who Lives in San Francisco, https://twitter.com/jeremyjarvis/status/428848527226437632 [accessed: 7.12.2017].
Google Scholar

Jesionek Robert (2017), Uczenie maszynowe i sztuczna inteligencja w opiniach polskich CIO, https://digitalandmore.pl/uczenie-maszynowe-i-sztuczna-inteligencja-w-opiniach-polskich-cio/ [accessed: 24.04.2018].
Google Scholar

Junker Buford H. (1960), Field Work: An Introduction to the Social Sciences, Chicago: University of Chicago Press.
Google Scholar

Kacperczyk Anna (2016), Społeczne światy. Teoria – empiria – metody badań: na przykładzie społecznego świata wspinaczki, Łódź: Wydawnictwo Uniwersytetu Łódzkiego.
Google Scholar DOI: https://doi.org/10.18778/7969-714-4

Kaggle (2017), 2017: The State of Data Science & Machine Learning, https://web.archive.org/web/20180222175627/https://www.kaggle.com/surveys/2017 [accessed: 27.03.2018].
Google Scholar

Kitchin Rob (2014), Big Data, new epistemologies and paradigm shifts, “Big Data & Society”, vol. 1(1), pp. 1–12, https://doi.org/10.1177/2053951714528481
Google Scholar DOI: https://doi.org/10.1177/2053951714528481

Kling Rob, Gerson Elihu M. (1978), Patterns of Segmentation and Intersection in the Computing World, “Symbolic Interaction”, vol. 1(2), pp. 24–43, https://doi.org/10.1525/si.1978.1.2.24
Google Scholar DOI: https://doi.org/10.1525/si.1978.1.2.24

Konecki Krzysztof (2020), Uwagi na temat tego, co jest postrzegane jako ważne i nieważne w socjologii, “Przegląd Socjologii Jakościowej”, vol. XVI, no. 2, pp. 188–207, https://doi.org/10.18778/1733-8069.16.2.11
Google Scholar DOI: https://doi.org/10.18778/1733-8069.16.2.11

Kozinets Robert V. (2003), The Field behind the Screen: Using Netnography for Marketing Research in Online Communities, “Journal of Marketing Research”, vol. 39(1), pp. 61–72, https://doi.org/10.1509/jmkr.39.1.61.18935
Google Scholar DOI: https://doi.org/10.1509/jmkr.39.1.61.18935

Krzysztofek Kazimierz (2015), Technologie cyfrowe w dyskursach o przyszłości pracy, “Studia Socjologiczne”, vol. 4(219), pp. 5–31, https://journals.pan.pl/Content/91277/mainfile.pdf [accessed: 2.11.2018].
Google Scholar

Kuncewicz Łukasz (2019), Lukasz Kuncewicz on LinkedIn: „Data Science Job Interview – How the Questions Will Change in 5 Years?, https://www.linkedin.com/feed/update/urn:li:activity:6556607403457155074 [accessed: 31.07.2019].
Google Scholar

Laney Douglas (2001), 3-D Data Management: Controlling Data Volume, Velocity and Variety, https://web.archive.org/web/20120813181324/https://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf [accessed: 14.03.2016].
Google Scholar

Lanthier Mark (2011), An Introduction to Computer Science and Problem Solving, [in:] Mark Lanthier (ed.), COMP 1405, Ottawa: Carleton University, pp. 1–38.
Google Scholar

Lassiter Luke Eric (2005), The Chicago Guide to Collaborative Ethnography, Chicago–London: The University of Chicago Press.
Google Scholar DOI: https://doi.org/10.7208/chicago/9780226467016.001.0001

Lohr Steve (2009), For Today’s Graduate, Just One Word: Statistics, “The New York Times”, http://www.nytimes.com/2009/08/06/technology/06stats.html [accessed: 15.02.2018].
Google Scholar

Loukides Mike (2010), What is data science?, https://www.oreilly.com/ideas/what-is-data-science [accessed: 8.09.2016].
Google Scholar

Lowrie Ian (2016), Caring for Computers: How Russian Data Scientists Refashion Their Laptops, “Anthropology Now”, vol. 8(2), pp. 25–33, https://doi.org/10.1080/19428200.2016.1202578
Google Scholar DOI: https://doi.org/10.1080/19428200.2016.1202578

Lowrie Ian (2017), Algorithmic rationality: Epistemology and efficiency in the data sciences, “Big Data & Society”, vol. 4(1), pp. 1–13, https://doi.org/10.1177/2053951717700925
Google Scholar DOI: https://doi.org/10.1177/2053951717700925

Lowrie Ian (2018), Becoming a Real Data Scientist. Expertise, Flexibility and Lifelong Learning, [in:] Hannah Knox, Dawn Nafus (eds.), Ethnography for a data-saturated world, Manchester: Manchester University Press, pp. 62–81.
Google Scholar DOI: https://doi.org/10.7765/9781526127600.00010

Marcus George E. (1995), Ethnography in/of the World System: The Emergence of Multi-Sited Ethnography, “Annual Review of Anthropology”, vol. 24, pp. 95–117.
Google Scholar DOI: https://doi.org/10.1146/annurev.anthro.24.1.95

Martin Vivian B. (2006), The Postmodern Turn: Shall Classic Grounded Theory Take That Detour? A Review Essay, “The Grounded Theory Review”, vol. 5(2/3), pp. 119–129.
Google Scholar

Mead George H. (1972), The Philosophy of the Act, Chicago: University of Chicago Press.
Google Scholar

Naur Peter (1974), Concise Survey of Computer Methods, Lund: Studentlitteratur.
Google Scholar

Nowosad Jakub (2019), Elementarz programisty. Wstęp do programowania używając R, Poznań: Space A., https://jakubnowosad.com/elp/ [accessed: 16.03.2020].
Google Scholar

Nunns James (2017), How Python rose to the top of the data science world, “Computer Business Review”, https://www.techmonitor.ai/technology/data/python-rose-top-data-science-world [accessed: 2.10.2018]
Google Scholar

O’Neil Cathy, Schutt Rachel (2015), Badanie danych: raport z pierwszej linii działań, Gliwice: Wydawnictwo Helion.
Google Scholar

Plummer Ken (2012), My Multiple Sick Bodies: Symbolic Interactionism, Autoethnography and Embodiment, [in:] Bryan S. Turner (ed.), Routledge Handbook of Body Studies, New York: Routledge, pp. 75–93.
Google Scholar

R Core Team (2021), R: A Language and Environment for Statistical Computing, https://www.r-project.org/ [accessed: 3.12.2021].
Google Scholar

Schoenfeld Alan H. (1992), Learning to Think Mathematically: Problem Solving, Metacognition, and Sense Making in Mathematics, [in:] Douglas Grouws (ed.), Handbook of Research on Mathematics Teaching and Learning, New York: Macmillan Publishers Limited, pp. 334–370.
Google Scholar

Seim Josh (2021), Participant Observation, Observant Participation, and Hybrid Ethnography, “Sociological Methods & Research”, vol. 53(1), pp. 1–32, https://doi.org/10.1177/0049124120986209
Google Scholar DOI: https://doi.org/10.1177/0049124120986209

Shaw Zed A. (2014), Learn Python the Hard Way: A Very Simple Introduction to the Terrifyingly Beautiful World of Computers and Code, Donnelley: Addison-Wesley.
Google Scholar

Shibutani Tamotsu (1955), Reference Groups as Perspectives, “American Journal of Sociology”, vol. 60(6), pp. 562–569, https://doi.org/10.1086/221630
Google Scholar DOI: https://doi.org/10.1086/221630

St. Germain James H. de (2008), Problem Solving, https://www.cs.utah.edu/~germain/PPS/Topics/problem_solving.html [accessed: 27.01.2019].
Google Scholar

Strauss Anselm L. (1978), A Social World Perspective, [in:] Norman Denzin (ed.), Studies in Symbolic Interaction, vol. 1, Greenwitch: JAI Press, pp. 119–128.
Google Scholar

Strauss Anselm L. (1982), Social Worlds and Legitimation Processes, [in:] Norman Denzin (ed.), Studies in Symbolic Interaction, vol. 4, Greenwitch: JAI Press, pp. 171–190.
Google Scholar

Strauss Anselm L. (1984), Social Worlds and Their Segmentation Processes, [in:] Norman Denzin (ed.), Studies in Symbolic Interaction, vol. 5, Greenwitch: JAI Press, pp. 123–139.
Google Scholar

Taylor David (2016), Battle of the Data Science Venn Diagrams, https://web.archive.org/web/20170428061035/https://www.kdnuggets.com/2016/10/battle-data-science-venn-diagrams.html [accessed: 14.12.2017].
Google Scholar

Thieme Nick (2018), R generation, “Significance”, vol. 15(4), pp. 14–19, https://doi.org/10.1111/j.1740-9713.2018.01169.x
Google Scholar DOI: https://doi.org/10.1111/j.1740-9713.2018.01169.x

Thomas Suzanne L., Nafus Dawn, Sherman Jamie (2018), Algorithms as fetish: Faith and possibility in algorithmic work, “Big Data & Society”, vol. 5(1), pp. 1–11, https://doi.org/10.1177/2053951717751552
Google Scholar DOI: https://doi.org/10.1177/2053951717751552

Trzpiot Grażyna (2017), Rozumienie Data Science, [in:] Grażyna Trzpiot (ed.), Statystyka a Data Science, Katowice: Wydawnictwo Uniwersytetu Ekonomicznego w Katowicach, pp. 6–30.
Google Scholar

Tufekci Zeynep (2015), Algorithmic Harms Beyond Facebook and Google: Emergent Challenges of Computational Agency, “Telecomm & High Tech”, vol. 203, pp. 203–218.
Google Scholar

Unruh David R. (1980), The Nature of Social Worlds, “The Pacific Sociological Review”, vol. 23(3), pp. 271–296, https://doi.org/10.2307/1388823
Google Scholar DOI: https://doi.org/10.2307/1388823

Uri Therese (2015), The Strengths and Limitations of Using Situational Analysis Grounded Theory as Research Methodology, “Journal of Ethnographic & Qualitative Research”, vol. 10(1), pp. 135–151.
Google Scholar

Vail D. Angus (1999), The Commodification of Time in Two Art Worlds, “Symbolic Interaction”, vol. 22(4), pp. 325–344.
Google Scholar DOI: https://doi.org/10.1016/S0195-6086(00)87400-7

Wacquant Loïc (2004), Body and Soul: Notebooks of an Apprentice Boxer, New York: Oxford University Press.
Google Scholar

Wickham Hadley (2018), You Can’t Do Data Science in a GUI, https://www.youtube.com/watch?v=cpbtcsGE0OA [accessed: 27.11.2018].
Google Scholar

Wickham Hadley, Grolemund Garrett (2017), R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, Beijing–Boston–Farnham–Sebastopol–Tokyo: O’Reilly.
Google Scholar

Xie Yihui, Allaire Joseph J., Grolemund Garrett (2018), R Markdown: The Definitive Guide, Boca Raton: Chapman and Hall/CRC.
Google Scholar DOI: https://doi.org/10.1201/9781138359444

Zuboff Shoshana (2019), The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power, New York: PublicAffairs.
Google Scholar

Żulicki Remigiusz (2022), Data science: najseksowniejszy zawód XXI wieku w Polsce. Big data, sztuczna inteligencja i PowerPoint, Łódź: Wydawnictwo Uniwersytetu Łódzkiego.
Google Scholar DOI: https://doi.org/10.18778/8331-110-4

Opublikowane

2024-11-30

Wersje

Jak cytować

Żulicki, R. (2024). Czy oni tworzą sztuczną inteligencję? (Re)konstrukcja działania podstawowego w data science . Przegląd Socjologii Jakościowej, 20(4), 190–213. https://doi.org/10.18778/1733-8069.20.4.09

Podobne artykuły

1 2 3 4 5 6 7 8 9 10 > >> 

Możesz również Rozpocznij zaawansowane wyszukiwanie podobieństw dla tego artykułu.