O Impacto Operacional e Social dos Grandes Modelos de Linguagem e Automação na Indústria / The Operational and Social Impact of Large Language Models and Automation in the Manufacturing Industry
Resumen
O objetivo deste trabalho é analisar o impacto operacional e social dos Grandes Modelos de Linguagem (LLMs) e da automação na indústria, identificando os desafios técnicos, legais e éticos envolvidos na integração dessas tecnologias em sistemas industriais. A pesquisa utiliza uma revisão integrativa da literatura, seguindo as diretrizes de Torraco (2005), com buscas realizadas nas bases Scopus e Perplexity AI. Foram selecionados e analisados estudos sobre a evolução histórica da automação e da inteligência artificial, enfatizando o surgimento dos LLMs e sua aplicação no contexto industrial. Os resultados destacam que, embora os LLMs representem um avanço significativo com potencial para revolucionar os processos produtivos, existem obstáculos como limitações técnicas, riscos de segurança e a necessidade de supervisão humana devido a limitações dos modelos, como alucinações. Conclui-se que a adoção dos LLMs na indústria requer um equilíbrio entre os benefícios e os riscos, enfatizando a importância da conformidade regulatória e da participação humana contínua para garantir a segurança e a eficiência dos sistemas industriais.
Palavras-chave: LLM. Automação Industrial. Inteligência Artificial. Engenharia de Produção. Impactos Sociais.
ABSTRACT
The objective of this work is to analyze the operational and social impact of Large Language Models (LLMs) and automation in the industry, identifying the technical, legal, and ethical challenges involved in integrating these technologies into industrial systems. The research employs an integrative literature review, following Torraco's (2005) guidelines, with searches conducted in the Scopus and Perplexity AI databases. Studies on the historical evolution of automation and artificial intelligence were selected and analyzed, emphasizing the emergence of LLMs and their application in the industrial context. The results highlight that, although LLMs represent a significant advancement with the potential to revolutionize production processes, there are obstacles such as technical limitations, security risks, and the need for human supervision due to model limitations like hallucinations. It is concluded that the adoption of LLMs in the industry requires a balance between benefits and risks, emphasizing the importance of regulatory compliance and continuous human participation to ensure the safety and efficiency of industrial systems.
Keywords: LLM. Industrial Automation. Artificial Intelligence. Production Engineering. Social Impacts
Referencias
ARAQUE-GONZÁLEZ, G.; SUÁREZ-HERNÁNDEZ, A.; GÓMEZ-VÁSQUEZ, M.; VÉLEZ-URIBE, J.; BERNAL-AVELLANEDA, A. Sustainable manufacturing in the fourth industrial revolution: A big data application proposal in the textile industry. Journal of Industrial Engineering and Management, v. 15, n. 4, p. 614-636, 2022. Disponível em: https://doi.org/10.3926/jiem.3922.
VARSHNEY, A. K.; GARG, A.; PANDEY, T. R.; SINGHAL, R. K.; SINGHAL, R.; SHARMA, H. The Development of Manufacturing Industry Revolutions from 1.0 to 5.0. Journal of Informatics Education and Research, v. 4, n. 1, p. 1230, 2024. ISSN 1526-4726.
ABDULRAHMAN, Al-Abassi; KARIMIPOUR, Hadis; HADDADPAJOUH, Hamed; DEHGHANTANHA, Ali; PARIZI, Reza M. Industrial Big Data Analytics: Challenges and Opportunities. In: 2019. p. 37-61. doi: 10.1007/978-3-030-38557-6_3.
YOON, Andre; KIM, Kyoung Yeon. World Economic Forum: Why Large Language Models are the future of manufacturing. World Economic Forum, 23 abr. 2024. Disponível em: https://www.weforum.org/stories/2024/04/why-large-language-models-are-so-important-for-the-future-of-the-manufacturing-industry/. Acesso em 06 set. de 2024.
TORRACO, R. Writing. Integrative Literature Reviews: Guidelines and Examples. Human Resource Development Review, v. 4, p. 356-367, 2005. Disponível em: https://doi.org/10.1177/1534484305278283.
WHITTEMORE, R.; KNAFL, K. The integrative review: updated methodology. Journal of Advanced Nursing, v. 52, n. 5, p. 546-553, 2005. Disponível em: https://doi.org/10.1111/j.1365-2648.2005.03621.x.
HOPIA, H.; LATVALA, E.; LIIMATAINEN, L. Reviewing the methodology of an integrative review. Scandinavian Journal of Caring Sciences, v. 30, n. 4, p. 662-669, 2016. Disponível em: https://doi.org/10.1111/scs.12327.
RETMA gives definition of “automation”. Electrical Engineering, v. 75, n. 8, p. 770-771, ago. 1956. doi: 10.1109/EE.1956.6442099
SINGH, Shubham; NAMEKAR, Swapnil. A review on automation of industries. International Journal of Engineering Applied Sciences and Technology, v. 4, n. 12, p. 298-300, abr. 2020. ISSN 2455-2143.
FLOYD, L. Opportunities in Automation and Control [From the Editor's Desk]. IEEE Industry Applications Magazine, v. 17, n. 2, p. 2, mar./abr. 2011. doi: 10.1109/MIAS.2010.939805.
IVANOV, Stanislav; KUYUMDZHIEV, Mihail; WEBSTER, Craig. Automation fears: Drivers and solutions. Technology in Society, v. 63, 2020, p. 101431. ISSN 0160-791X. Disponível em: https://doi.org/10.1016/j.techsoc.2020.101431.
INTERNATIONAL FEDERATION OF ROBOTICS. Executive Summary World Robotics 2019: Industrial Robots. 2019. Disponível em: https://ifr.org/downloads/press2018/Executive%20Summary%20WR%202019%20Industrial%20Robots.pdf.
IVANOV, Stanislav; KUYUMDZHIEV, Mihail; WEBSTER, Craig. Automation fears: Drivers and solutions. Technology in Society, v. 63, 2020, p. 101431. ISSN 0160-791X. Disponível em: https://doi.org/10.1016/j.techsoc.2020.101431.
BAINBRIDGE, Lisanne. Ironies of automation. Automatica, v. 19, n. 6, p. 775-779, 1983. ISSN 0005-1098. Disponível em: https://doi.org/10.1016/0005-1098(83)90046-8.
HUANG, M.-H.; RUST, R. T. Artificial Intelligence in Service. Journal of Service Research, v. 21, n. 2, p. 155-172, 2018. Disponível em: https://doi.org/10.1177/1094670517752459.
FREY, Carl Benedikt; OSBORNE, Michael A. The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, v. 114, p. 254-280, 2017. ISSN 0040-1625. Disponível em: https://doi.org/10.1016/j.techfore.2016.08.019.
AUTOR, David; SALOMONS, Anna. Is Automation Labor-Displacing? Productivity Growth, Employment, and the Labor Share. NBER Working Paper Series, n. 24871, jul. 2018. Disponível em: http://www.nber.org/papers/w24871.
JONES, L.; GOLAN, D.; HANNA, Sammy; RAMACHANDRAN, Manoj. Artificial intelligence, machine learning and the evolution of healthcare: A bright future or cause for concern? Bone & Joint Research, v. 7, p. 223-225, 1 mar. 2018. doi: 10.1302/2046-3758.73.BJR-2017-0147.R1.
ABRAHAM, Tara H. (Physio)logical circuits: The intellectual origins of the McCulloch–Pitts neural networks. Journal of the History of the Behavioral Sciences, v. 38, n. 1, p. 3-25, 2002. Disponível em: https://doi.org/10.1002/jhbs.1094.
MIRA MIRA, José. Symbols versus connections: 50 years of artificial intelligence. Neurocomputing, v. 71, n. 4–6, p. 671-680, 2008. ISSN 0925-2312. Disponível em: https://doi.org/10.1016/j.neucom.2007.06.009.
HARTMAN, Patrick J. Practical applications of artificial intelligence in naval engineering. Naval Engineers Journal, v. 100, n. 6, p. 32-40, nov. 1988. Disponível em: https://doi.org/10.1111/j.1559-3584.1988.tb00825.x.
DÖRRICH, Marion; FAN, Mingcheng; KIST, Andreas. Impact of Mixed Precision Techniques on Training and Inference Efficiency of Deep Neural Networks. IEEE Access, v. 11, p. 57627-57634, 2023. doi: 10.1109/ACCESS.2023.3284388
EL-HAD, Mohamed. Artificial Intelligence Background, Definitions, Challenges and Benefits. مجلة الجمعیة المصریة لنظم المعلومات وتکنولوجیا الحاسبات, v. 31, p. 124-132, 2023. doi: 10.21608/jstc.2023.297957.
HOU, Xinyi; ZHAO, Yanjie; LIU, Yue; YANG, Zhou; WANG, Kailong; LI, Li; LUO, Xiapu; LO, David; GRUNDY, John; WANG, Haoyu. Large Language Models for Software Engineering: A Systematic Literature Review. arXiv preprint arXiv:2308.10620v6 [cs.SE], 10 abr. 2024. Disponível em: https://arxiv.org/abs/2308.10620v6.
THANGARASA, V.; GUPTA, A.; MARSHALL, W.; LI, T.; LEONG, K.; DECOSTE, D.; LIE, S.; SAXENA, S. SPDF: Sparse Pre-training and Dense Fine-tuning for Large Language Models. In: Conference on Uncertainty in Artificial Intelligence, 2023.
AGERRI, Rodrigo; ARTOLA, Xabier; BELOKI, Zuhaitz; RIGAU, German; SOROA, Aitor. Big data for Natural Language Processing: A streaming approach. Knowledge-Based Systems, v. 79, p. 36-42, 2015. ISSN 0950-7051. Disponível em: https://doi.org/10.1016/j.knosys.2014.11.007.
BADRINARAYANAN, Vijay; KENDALL, Alex; CIPOLLA, Roberto. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, v. 39, p. 2481-2495, 2015. Disponível em: https://doi.org/10.1109/TPAMI.2016.2644615.
WANG, Yue; LE, Hung; GOTMARE, Akhilesh; BUI, Nam Dinh; LI, Jia; HOI, Steven C. CodeT5+: Open Code Large Language Models for Code Understanding and Generation. In: Conference on Empirical Methods in Natural Language Processing, 2023.
FU, Z.; LAM, Wai; YU, Q.; SO, Anthony M.; HU, S.; LIU, Z.; COLLIER, Nigel. Decoder-Only or Encoder-Decoder? Interpreting Language Model as a Regularized Encoder-Decoder. arXiv preprint arXiv:2304.04052, 2023. Disponível em: https://arxiv.org/abs/2304.04052.
HU, Krystal. Reuters: ChatGPT sets record for fastest-growing user base - analyst note. Reuters, 2 fev. 2023. Disponível em: https://www.reuters.com.
SCHENDSTOK, Matt; WERTZ, Sydney Schreiner. Occupational exposure to artificial intelligence by geography and education. Office of Economic Policy Working Paper 2024-02, U.S. Department of the Treasury, abr. 2024. Disponível em: https://home.treasury.gov/system/files/136/AI-Combined-PDF.pdf.
WANG, Qi; LIU, Xuanqi; HUANG, Ke-Wei. Investigating employees’ occupational risks and benefits resulting from artificial intelligence: An empirical analysis. Information & Management, v. 61, n. 8, p. 104036, 2024. ISSN 0378-7206. Disponível em: https://doi.org/10.1016/j.im.2024.104036.
PIZZINELLI, Carlo; PANTON, Augustus J.; TAVARES, Marina Mendes; CAZZANIGA, Mauro; LI, Longji. Labor Market Exposure to AI: Cross-country Differences and Distributional Implications. IMF Working Paper No. 2023/216, International Monetary Fund, 4 out. 2023. ISBN 9798400254802.
TOLAN, Songül; PESOLE, Annarosa; MARTÍNEZ-PLUMED, Fernando; FERNÁNDEZ-MACÍAS, Enrique; HERNÁNDEZ-ORALLO, José; GÓMEZ, Emilia. Measuring the occupational impact of AI: tasks, cognitive abilities and AI benchmarks. Journal of Artificial Intelligence Research, v. 71, p. 191-236, 2021. Disponível em: https://doi.org/10.1613/jair.1.12345.
URLANA, Ashok; KUMAR, Charaka Vinayak; SINGH, Ajeet Kumar; GARLAPATI, Bala Mallikarjunarao; CHALAMALA, Srinivasa Rao; MISHRA, Rahul. LLMs with Industrial Lens: Deciphering the Challenges and Prospects -- A Survey. arXiv preprint arXiv:2402.14558 [cs.CL], 22 fev. 2024. Disponível em: https://doi.org/10.48550/arXiv.2402.14558.
LEE, Jay; SU, Hanqi. A Unified Industrial Large Knowledge Model Framework in Smart Manufacturing. arXiv preprint arXiv:2312.14428v2 [cs.LG], 15 maio 2024. Disponível em: https://doi.org/10.48550/arXiv.2312.14428.
XIA, Yuchen; SHENOY, Manthan; JAZDI, Nasser; WEYRICH, Michael. Towards autonomous system: flexible modular production system enhanced with large language model agents. arXiv preprint arXiv:2304.14721v4 [cs.RO], 24 jul. 2023. Disponível em: https://doi.org/10.48550/arXiv.2304.14721.
GONZÁLEZ-SANTAMARTA, Miguel Á.; RODRÍGUEZ-LERA, Francisco J.; GUERRERO-HIGUERAS, Ángel Manuel; MATELLÁN-OLIVERA, Vicente. Integration of Large Language Models within Cognitive Architectures for Autonomous Robots. arXiv preprint arXiv:2309.14945v2 [cs.RO], 23 mar. 2024. Disponível em: https://doi.org/10.48550/arXiv.2309.14945.
WANG, Yen-Jen; ZHANG, Bike; CHEN, Jianyu; SREENATH, Koushil. Prompt a Robot to Walk with Large Language Models. arXiv preprint arXiv:2309.09969v3 [cs.RO], 15 out. 2024. Disponível em: https://doi.org/10.48550/arXiv.2309.09969.
FAKIH, Mohamad; DHARMAJI, Rahul; MOGHADDAS, Yasamin; QUIROS ARAYA, Gustavo; OGUNDARE, Oluwatosin; AL FARUQUE, Mohammad Abdullah. LLM4PLC: Harnessing Large Language Models for Verifiable Programming of PLCs in Industrial Control Systems. arXiv preprint arXiv:2401.05443v1 [cs.SE], 8 jan. 2024.
KOZIOLEK, Heiko; GRUENER, Sten; ASHIWAL, Virendra. ChatGPT for PLC/DCS Control Logic Generation. arXiv preprint arXiv:2305.15809 [cs.SE], 25 maio 2023.
LEVANDUSKI, Michael. Can ChatGPT Be Used For Control System Programming? Control, 25 abr. 2023. Disponível em: https://control.com/technical-articles/can-openai-chatgpt-generative-ai-be-used-for-control-system-programming.
DOWDESWELL, B.; SINHA, R.; MACDONELL, S. G. Finding faults: A scoping study of fault diagnostics for industrial cyber-physical systems. Journal of Systems and Software, v. 168, p. 1-16, 2020. doi: 10.1016/j.jss.2020.110638.
FRIEL, Robert; SANYAL, Atindriyo. Chainpoll: A high efficacy method for LLM hallucination detection. arXiv preprint arXiv:2310.18344 [cs.CL], 22 out. 2023. Disponível em: https://doi.org/10.48550/arXiv.2310.18344.
ROYCHOWDHURY, Sohini; ALVAREZ, Andres; MOORE, Brian; KREMA, Marko; GELPI, Maria Paz; RODRIGUEZ, Federico Martin; RODRIGUEZ, Angel; CABREJAS, Jose Ramon; MARTINEZ SERRANO, Pablo; AGRAWAL, Punit; MUKHERJEE, Arijit. Hallucination-minimized Data-to-answer Framework for Financial Decision-makers. arXiv preprint arXiv:2311.07592 [cs.CL], 9 nov. 2023. Disponível em: https://doi.org/10.48550/arXiv.2311.07592.
OGUNDARE, Oluwatosin; QUIROS ARAYA, Gustavo; AKROTIRIANAKIS, Ioannis; SHUKLA, Ankit. Resiliency Analysis of LLM generated models for Industrial Automation. arXiv preprint arXiv:2308.12129 [cs.SE], 23 ago. 2023. Disponível em: https://doi.org/10.48550/arXiv.2308.12129.
YANG, Linyao; CHEN, Hongyang; LI, Zhao; DING, Xiao; WU, Xindong. Give Us the Facts: Enhancing Large Language Models with Knowledge Graphs for Fact-aware Language Modeling. arXiv preprint arXiv:2306.11489v2 [cs.CL], 30 jan. 2024. Disponível em: https://doi.org/10.48550/arXiv.2306.11489.
EVERITT, Tom; LEA, Gary; HUTTER, Marcus. AGI Safety Literature Review. arXiv preprint arXiv:1805.01109v2 [cs.AI], 21 maio 2018. Disponível em: https://doi.org/10.48550/arXiv.1805.01109.
MEINCKE, Lennart; GIROTRA, Karan; NAVE, Gideon; TERWIESCH, Christian; ULRICH, Karl T. Using Large Language Models for Idea Generation in Innovation. The Wharton School Research Paper, set. 2024. Disponível em: https://ssrn.com/abstract=4526071.
BAZETT, Trefor. ChatGPT is destroying my math exams. YouTube, 2 jul. 2024. Disponível em: https://www.youtube.com/watch?v=example-link.
DOI: http://dx.doi.org/10.12819/2025.21.1.7
Enlaces refback
- No hay ningún enlace refback.
Este obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0 Internacional.
Atribuição (BY): Os licenciados têm o direito de copiar, distribuir, exibir e executar a obra e fazer trabalhos derivados dela, conquanto que deem créditos devidos ao autor ou licenciador, na maneira especificada por estes.
Não Comercial (NC): Os licenciados podem copiar, distribuir, exibir e executar a obra e fazer trabalhos derivados dela, desde que sejam para fins não-comerciais
Sem Derivações (ND): Os licenciados podem copiar, distribuir, exibir e executar apenas cópias exatas da obra, não podendo criar derivações da mesma.
ISSN 1806-6356 (Impresso) e 2317-2983 (Eletrônico)