arXiv HTML resource

Boardwalk Empire: How Generative AI is Revolutionizing Economic Paradigms

The relentless pursuit of technological advancements has ushered in a new era where artificial intelligence (AI) is not only a powerful tool but also a critical economic driver. At the forefront of this transformation is Generative AI, which is catalyzing a paradigm shift across industries. Deep generative models, an integration of generative and deep learning techniques, excel in creating new data beyond analyzing …

Licence: OPEN CC-BY-4.0
Authors: Subramanyam Sahoo, Kamlesh Dutta
Published: 2024-10-19 · arXiv
Language: en
Length: 14263 words
Type: narrative text

Cites 34 works

Open original ↗

Extracted text · References Words 11,408–13,547 of 13,983

← Previous Next →

References

Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, and Balaji Lakshminarayanan. Do deep generative models know what they don’t know? In International Conference on Learning Representations, 2019. URL https://openreview.net/forum?id=H1xwNhCcYm.
Ruslan Salakhutdinov. Learning deep generative models. Annual Review of Statistics and Its Application, 2:361–385, 2015.
Dinesh Kalla, Nathan Smith, Fnu Samaah, and Sivaraju Kuraku. Study and analysis of chat gpt and its impact on different fields of study. International Journal of Innovative Science and Research Technology, 8(3), 2023.
Yanqing Wang. Generative ai in operational risk management: Harnessing the future of finance. Operational Risk Management: Harnessing the Future of Finance (May 17, 2023), 2023.
Xianzhi Li, Samuel Chan, Xiaodan Zhu, Yulong Pei, Zhiqiang Ma, Xiaomo Liu, and Sameena Shah. Are chatgpt and gpt-4 general-purpose solvers for financial text analytics? a study on several typical tasks. In Empirical Methods in Natural Language Processing (EMNLP 2023), 2023a.
Xiang Ye and Pengpeng Yue. Financial literacy and household energy efficiency: An analysis of credit market and supply chain. Finance Research Letters, 52:103563, 2023.
Longbing Cao. Ai in finance: A review. Available at SSRN 3647625, 2020.
Timo Teräsvirta. Forecasting economic variables with nonlinear models. Handbook of economic forecasting, 1:413–457, 2006.
James Brand, Ayelet Israeli, and Donald Ngwe. Using gpt for market research. Available at SSRN 4395751, 2023.
Philippe Aghion, Benjamin F Jones, and Charles I Jones. Artificial intelligence and economic growth. Technical report, National Bureau of Economic Research, 2017.
Henri Arslanian and Fabrice Fischer. The future of finance: The impact of FinTech, AI, and crypto on financial services. Springer, 2019.
Nizan Geslevich Packin. Consumer finance and ai: The death of second opinions? NYUJ Legis. & Pub. Pol’y, 22:319, 2019.
Stephanie Houde, Vera Liao, Jacquelyn Martino, Michael Muller, David Piorkowski, John Richards, Justin Weisz, and Yunfeng Zhang. Business (mis) use cases of generative ai. arXiv preprint arXiv:2003.07679, 2020.
Anna Strasser. On pitfalls (and advantages) of sophisticated large language models. arXiv preprint arXiv:2303.17511, 2023.
Diederik P Kingma, Max Welling, et al. An introduction to variational autoencoders. Foundations and Trends® in Machine Learning, 12(4):307–392, 2019.
Yuheng Bu, Shaofeng Zou, Yingbin Liang, and Venugopal V Veeravalli. Estimation of kl divergence: Optimal minimax rate. IEEE Transactions on Information Theory, 64(4):2648–2674, 2018.
Feiyu Xu, Hans Uszkoreit, Yangzhou Du, Wei Fan, Dongyan Zhao, and Jun Zhu. Explainable ai: A brief survey on history, research areas, approaches and challenges. In Natural Language Processing and Chinese Computing: 8th CCF International Conference, NLPCC 2019, Dunhuang, China, October 9–14, 2019, Proceedings, Part II 8, pages 563–574. Springer, 2019.
Ian Goodfellow. Nips 2016 tutorial: Generative adversarial networks. arXiv preprint arXiv:1701.00160, 2016.
Kwanda Sydwell Ngwenduna and Rendani Mbuvha. Alleviating class imbalance in actuarial applications using generative adversarial networks. Risks, 9(3):49, 2021.
Zilong Zhao, Aditya Kunar, Robert Birke, and Lydia Y Chen. Ctab-gan: Effective table data synthesizing. In Asian Conference on Machine Learning, pages 97–112. PMLR, 2021.
Kai Lei, Yuexiang Xie, Shangru Zhong, Jingchao Dai, Min Yang, and Ying Shen. Generative adversarial fusion network for class imbalance credit scoring. Neural Computing and Applications, 32:8451–8462, 2020.
Sam Bond-Taylor, Adam Leach, Yang Long, and Chris G Willcocks. Deep generative modelling: A comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models. arxiv 2021. arXiv preprint arXiv:2103.04922.
Jana Doering, Renatas Kizys, Angel A Juan, Angels Fito, and Onur Polat. Metaheuristics for rich portfolio optimisation and risk management: Current state and future trends. Operations Research Perspectives, 6:100121, 2019.
Debasis Mohapatra, Sayoni Das, Lopamudra Pattnaik, Swati Meher, Rakshanda Khan, and Subramanyam Sahoo. Evaluation of standard classifiers for protein subcellular localization. In 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA), pages 1–4. IEEE, 2020.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017.
Samuel López-Ruiz, Carlos Ignacio Hernández-Castellanos, and Katya Rodríguez-Vázquez. Multi-objective framework for quantile forecasting in financial time series using transformers. In Proceedings of the Genetic and Evolutionary Computation Conference, pages 395–403, 2022.
Xuetong Niu, Li Wang, and Xulei Yang. A comparison study of credit card fraud detection: Supervised versus unsupervised. arXiv preprint arXiv:1904.10604, 2019.
Jian Chen, Yao Shen, and Riaz Ali. Credit card fraud detection using sparse autoencoder and generative adversarial network. In 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pages 1054–1059. IEEE, 2018.
Yu-Jun Zheng, Xiao-Han Zhou, Wei-Guo Sheng, Yu Xue, and Sheng-Yong Chen. Generative adversarial network based telecom fraud detection at the receiving bank. Neural Networks, 102:78–86, 2018.
Adrian Micu, Alexandru Capatina, Dragos Sebastian Cristea, Dan Munteanu, Angela-Eliza Micu, and Daniela Ancuta Sarpe. Assessing an on-site customer profiling and hyper-personalization system prototype based on a deep learning approach. Technological Forecasting and Social Change, 174:121289, 2022.
Dmitri Goldenberg, Kostia Kofman, Javier Albert, Sarai Mizrachi, Adam Horowitz, and Irene Teinemaa. Personalization in practice: Methods and applications. In Proceedings of the 14th ACM international conference on web search and data mining, pages 1123–1126, 2021.
Salvatore Parise, Patricia J Guinan, and Ron Kafka. Solving the crisis of immediacy: How digital technology can transform the customer experience. Business Horizons, 59(4):411–420, 2016.
Irene Solaiman. The gradient of generative ai release: Methods and considerations. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, pages 111–122, 2023.
Justin D Weisz, Michael Muller, Jessica He, and Stephanie Houde. Toward general design principles for generative ai applications. arXiv preprint arXiv:2301.05578, 2023.
Seongil Han. Explainable credit scoring through generative adversarial networks. PhD thesis, Birkbeck, University of London, 2021.
Yanzhe Kang, Liao Chen, Ning Jia, Wei Wei, Jiang Deng, and Haizhang Qian. A cwgan-gp-based multi-task learning model for consumer credit scoring. Expert Systems with Applications, 206:117650, 2022.
Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, and Ilya Sutskever. Robust speech recognition via large-scale weak supervision. In International Conference on Machine Learning, pages 28492–28518. PMLR, 2023.
David N Sousa, Miguel A Brito, and Carlos Argainha. Virtual customer service: building your chatbot. In Proceedings of the 3rd International Conference on Business and Information Management, pages 174–179, 2019.
Nandini Prasad KS, S Sudhanva, TN Tarun, Yuvraaj Yuvraaj, and DA Vishal. Conversational chatbot builder–smarter virtual assistance with domain specific ai. In 2023 4th International Conference for Emerging Technology (INCET), pages 1–4. IEEE, 2023.
Tae Hyun Baek and Minseong Kim. Is chatgpt scary good? how user motivations affect creepiness and trust in generative artificial intelligence. Telematics and Informatics, 83:102030, 2023.
Yang Li, Yangyang Yu, Haohang Li, Zhi Chen, and Khaldoun Khashanah. Tradinggpt: Multi-agent system with layered memory and distinct characters for enhanced financial trading performance. arXiv preprint arXiv:2309.03736, 2023b.
Haohan Zhang, Fengrui Hua, Chengjin Xu, Jian Guo, Hao Kong, and Ruiting Zuo. Unveiling the potential of sentiment: Can large language models predict chinese stock price movements? arXiv preprint arXiv:2306.14222, 2023.
Udit Gupta. Gpt-investar: Enhancing stock investment strategies through annual report analysis with large language models. arXiv preprint arXiv:2309.03079, 2023.
Thomas Yue and Chi Chung Au. Gptquant’s conversational ai: Simplifying investment research for all. Available at SSRN 4380516, 2023.
Kris McGuffie and Alex Newhouse. The radicalization risks of gpt-3 and advanced neural language models. arXiv preprint arXiv:2009.06807, 2020.
Victor Jüttner, Martin Grimmer, and Erik Buchmann. Chatids: Explainable cybersecurity using generative ai. arXiv preprint arXiv:2306.14504, 2023.
Laura Weidinger, John Mellor, Maribeth Rauh, Conor Griffin, Jonathan Uesato, Po-Sen Huang, Myra Cheng, Mia Glaese, Borja Balle, Atoosa Kasirzadeh, et al. Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359, 2021.
Luciano Floridi and Massimo Chiriatti. Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines, 30:681–694, 2020.
Maria Rigaki, Ondřej Lukáš, Carlos A Catania, and Sebastian Garcia. Out of the cage: How stochastic parrots win in cyber security environments. arXiv preprint arXiv:2308.12086, 2023.
Maanak Gupta, CharanKumar Akiri, Kshitiz Aryal, Eli Parker, and Lopamudra Praharaj. From chatgpt to threatgpt: Impact of generative ai in cybersecurity and privacy. IEEE Access, 2023.
Scott Shackelford, Lawrence J Trautman, and W Gregory Voss. How we learned to stop worrying and love ai: Analyzing the rapid evolution of generative pre-trained transformer (gpt) and its impacts on law, business, and society. Business, and Society (July 20, 2023), 2023.
Abi Aryan, Aakash Kumar Nain, Andy McMahon, Lucas Augusto Meyer, and Harpreet Singh Sahota. The costly dilemma: Are large language models the pay-day loans of machine learning? 2023.
Breana Patel. The future of mortgages: Evaluating the potential of blockchain and generative ai for reducing costs and streamlining processes. 2023.
Lars Hillebrand, Armin Berger, Tobias Deußer, Tim Dilmaghani, Mohamed Khaled, Bernd Kliem, Rüdiger Loitz, Maren Pielka, David Leonhard, Christian Bauckhage, et al. Improving zero-shot text matching for financial auditing with large language models. arXiv preprint arXiv:2308.06111, 2023.
Agam Shah and Sudheer Chava. Zero is not hero yet: Benchmarking zero-shot performance of llms for financial tasks. arXiv preprint arXiv:2305.16633, 2023.
Xinli Yu, Zheng Chen, Yuan Ling, Shujing Dong, Zongyi Liu, and Yanbin Lu. Temporal data meets llm–explainable financial time series forecasting. arXiv preprint arXiv:2306.11025, 2023.
Hongyang Yang, Xiao-Yang Liu, and Christina Dan Wang. Fingpt: Open-source financial large language models. arXiv preprint arXiv:2306.06031, 2023.
Jacques Bughin. To chatgpt or not to chatgpt? Available at SSRN 4411051, 2023.
Qianqian Xie, Weiguang Han, Xiao Zhang, Yanzhao Lai, Min Peng, Alejandro Lopez-Lira, and Jimin Huang. Pixiu: A large language model, instruction data and evaluation benchmark for finance. arXiv preprint arXiv:2306.05443, 2023.
Shijie Wu, Ozan Irsoy, Steven Lu, Vadim Dabravolski, Mark Dredze, Sebastian Gehrmann, Prabhanjan Kambadur, David Rosenberg, and Gideon Mann. Bloomberggpt: A large language model for finance. arXiv preprint arXiv:2303.17564, 2023.
Pawel Korzynski, Grzegorz Mazurek, Andreas Altmann, Joanna Ejdys, Ruta Kazlauskaite, Joanna Paliszkiewicz, Krzysztof Wach, and Ewa Ziemba. Generative artificial intelligence as a new context for management theories: analysis of chatgpt. Central European Management Journal, 2023.
Alex G Kim, Maximilian Muhn, and Valeri V Nikolaev. Bloated disclosures: Can chatgpt help investors process information? Chicago Booth Research Paper, (23-07), 2023.
Leland Bybee. Surveying generative ai’s economic expectations. arXiv preprint arXiv:2305.02823, 2023.
Yiheng Liu, Tianle Han, Siyuan Ma, Jiayue Zhang, Yuanyuan Yang, Jiaming Tian, Hao He, Antong Li, Mengshen He, Zhengliang Liu, et al. Summary of chatgpt/gpt-4 research and perspective towards the future of large language models. arXiv preprint arXiv:2304.01852, 2023.
Edward W Felten, Manav Raj, and Robert Seamans. Occupational heterogeneity in exposure to generative ai. Available at SSRN 4414065, 2023.
Zihan Chen, Lei Nico Zheng, Cheng Lu, Jialu Yuan, and Di Zhu. Chatgpt informed graph neural network for stock movement prediction. arXiv preprint arXiv:2306.03763, 2023.
Jeremy Bertomeu, Yupeng Lin, Yibin Liu, and Zhenghui Ni. Capital market consequences of generative ai: Early evidence from the ban of chatgpt in italy. Available at SSRN, 2023.
Emilio Ferrara. Should chatgpt be biased? challenges and risks of bias in large language models. arXiv preprint arXiv:2304.03738, 2023.
Razvan Azamfirei, Sapna R Kudchadkar, and James Fackler. Large language models and the perils of their hallucinations. Critical Care, 27(1):1–2, 2023.
Nuno M Guerreiro, Duarte Alves, Jonas Waldendorf, Barry Haddow, Alexandra Birch, Pierre Colombo, and André FT Martins. Hallucinations in large multilingual translation models. arXiv preprint arXiv:2303.16104, 2023.
Yifan Li, Yifan Du, Kun Zhou, Jinpeng Wang, Wayne Xin Zhao, and Ji-Rong Wen. Evaluating object hallucination in large vision-language models. arXiv preprint arXiv:2305.10355, 2023c.
Minghao Wu and Alham Fikri Aji. Style over substance: Evaluation biases for large language models. arXiv preprint arXiv:2307.03025, 2023.
Niels Mündler, Jingxuan He, Slobodan Jenko, and Martin Vechev. Self-contradictory hallucinations of large language models: Evaluation, detection and mitigation. arXiv preprint arXiv:2305.15852, 2023.
Angeliki Lazaridou, Adhiguna Kuncoro, Elena Gribovskaya, Devang Agrawal, Adam Liska, Tayfun Terzi, Mai Gimenez, Cyprien de Masson d’Autume, Sebastian Ruder, Dani Yogatama, et al. Pitfalls of static language modelling. arXiv preprint arXiv:2102.01951, 2021.
Jack W Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, et al. Scaling language models: Methods, analysis & insights from training gopher. arXiv preprint arXiv:2112.11446, 2021.
Ping Xiao, Yuanyuan Chen, and Weining Bao. Waiting, banning, and embracing: An empirical analysis of adapting policies for generative ai in higher education. arXiv preprint arXiv:2305.18617, 2023.
Virginia K Felkner, Ho-Chun Herbert Chang, Eugene Jang, and Jonathan May. Winoqueer: A community-in-the-loop benchmark for anti-lgbtq+ bias in large language models. arXiv preprint arXiv:2306.15087, 2023.
Leonard Salewski, Stephan Alaniz, Isabel Rio-Torto, Eric Schulz, and Zeynep Akata. In-context impersonation reveals large language models’ strengths and biases. arXiv preprint arXiv:2305.14930, 2023.
Vishesh Thakur. Unveiling gender bias in terms of profession across llms: Analyzing and addressing sociological implications. arXiv preprint arXiv:2307.09162, 2023.
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding. arXiv preprint arXiv:2009.03300, 2020.

Metadata record

One description, two standard projections

Built from what the sources declared and what the gates observed. Nothing absent has been filled in here. 1 value read out of the text by the enrichment rules and 34 links to or from other resources — a lab's files, the pages it links, the works it cites stand under their elements, marked inferred, and are kept apart in the exports.

Valid

Profile: aimpro-oer-profile/1 built 2026-10-09
Content language: en declared English

Export Dublin Core LOM Internal record

Show empty elements

DCMI Metadata Terms. Dublin Core has no element that separates the original file from the text extracted out of it, and none for LOM's educational characterisation. Both survive here as provenance statements and in the record itself, not in the projection. the standard ↗

The groups below are this library's, for reading. DCMI Terms itself has no categories; each term keeps its standard name.

Identity 3/3

Identifier dcterms:identifier this engine resource_id source resource/arxiv_id: tag:aim-pro.eu,2026:oer/0c7c3e6bcfa8
https://arxiv.org/abs/2410.15212
arxiv:2410.15212

This engine's id, the source URI, and any persistent identifier the source published.
Title dcterms:title source resource/title: Boardwalk Empire: How Generative AI is Revolutionizing Economic Paradigms

The title of this resource, not of the record or repository it came from.
Type dcterms:type source resource: dcmitype:Text
preprint

A DCMI Type term for the form held, plus the source's own genre label as declared.

Responsibility 1/3

Creator dcterms:creator

source 2 assertions

Subramanyam Sahoo
Kamlesh Dutta

Only agents the source named as creators. A repository owner is not made an author by default.

Contributor dcterms:contributor

source 2 assertions

none found — the source did not declare it

Everyone else named, with the role as declared and the scope they were declared at.

Publisher dcterms:publisher

none found — the source did not declare it

Only when the source names a publisher of the work. The platform it was collected from is recorded as provenance instead.

Content 3/5

Description dcterms:description

source resource/summary

The relentless pursuit of technological advancements has ushered in a new era where artificial intelligence (AI) is not only a powerful tool but also a critical economic driver. At the forefront of this transformation is Generative AI, which is catalyzing a paradigm shift across industries. Deep generative models, an integration of generative and deep learning techniques, excel in creating new data beyond analyzing existing ones, revolutionizing sectors from production and manufacturing to finance. By automating design, optimization, and innovation cycles, Generative AI is reshaping core industrial processes. In the financial sector, it is transforming risk assessment, trading strategies, and forecasting, demonstrating its profound impact. This paper explores the sweeping changes driven by deep learning models like Large Language Models (LLMs), highlighting their potential to foster innovative business models, disruptive technologies, and novel economic landscapes. As we stand at the threshold of an AI-driven economic era, Generative AI is emerging as a pivotal force, driving innovation, disruption, and economic evolution on a global scale.

The abstract, and the depositor's additional notes after it when the source has a field for them.

Subject dcterms:subject

source resource/categories/0

cs.CE (arxiv)
cs.CY (arxiv)
cs.LG (arxiv)
cs.SI (arxiv)

Declared keywords and taxonomy terms, each carrying its scheme.

Language dcterms:language

language gate read the declared field

The language of the content as the language gate read it.

Audience dcterms:audience

mapped learning_resource_type/preprint

none found — the source did not declare it

Imported when the source declares an intended audience. Never inferred.

Education level dcterms:educationLevel

mapped learning_resource_type/preprint

none found — the source did not declare it

Imported when the source declares an educational context.

Rights 3/4

Licence dcterms:license

licence gate arXiv:arXiv/arXiv:license

http://creativecommons.org/licenses/by/4.0/

The licence document, as a URI where the gate resolved one.

Rights dcterms:rights

licence gate arXiv:arXiv/arXiv:license

CC-BY-4.0 — assessed as OPEN by the harvester's licence gate. Conditions: attribution required. open, permissive

The verdict and the conditions, in words.

Rights holder dcterms:rightsHolder

none found — the source did not declare it

Only when named at source.

Access rights dcterms:accessRights

source resource

open

The access condition the source declared. Not the licence.

Form and dates 4/10

Format dcterms:format

conversion latexml-html

text/markdown

One value per form held — the original and the extracted text.

Extent dcterms:extent

conversion latexml-html

99442 bytes (extracted text)

Sizes in bytes, each saying which form it measures.

Issued dcterms:issued

source resource

2024-10-19

When the resource was published. Not when it was collected.

Modified dcterms:modified

source resource

2024-10-22

When the source's own record was last changed.

Created dcterms:created

source resource

none found — the source did not declare it

Accepted dcterms:dateAccepted

source resource

none found — the source did not declare it

When a publisher accepted the work, where the source records it.

Submitted dcterms:dateSubmitted

source resource

none found — the source did not declare it

When the work was submitted, where the source records it.

Available dcterms:available

source resource

none found — the source did not declare it

Valid dcterms:valid

source resource

none found — the source did not declare it

Bibliographic citation dcterms:bibliographicCitation

none found — the source did not declare it

Where the work was published, in the source's own words: a journal with its volume and pages, a conference, an imprint.

Relations and custody 1/9

Is part of dcterms:isPartOf

none found — the source did not declare it

The repository, book or record this resource was found inside.

Has part dcterms:hasPart

none found — the source did not declare it

What this resource is made of, when the source lists its parts.

Is version of dcterms:isVersionOf

none found — the source did not declare it

Has version dcterms:hasVersion

none found — the source did not declare it

References dcterms:references

inferred read its reference list, line 254 citation

arXiv:2003.07679
arXiv:2303.17511
arXiv:1701.00160
arXiv:2103.04922
arXiv:1904.10604
arXiv:2301.05578
arXiv:2309.03736
arXiv:2306.14222
arXiv:2309.03079
arXiv:2009.06807
arXiv:2306.14504
arXiv:2112.04359
and 22 more, every one of them in the export

Works this one cites, when the source declares them as relations. What its text links to and its reference list cites is inferred, and stands under it apart.

Is referenced by dcterms:isReferencedBy

none found — the source did not declare it

What links to or cites this one, when a source declares it; inferred from the texts held otherwise, and set apart.

Requires dcterms:requires

none found — the source did not declare it

What this resource needs in order to be used, when a source declares it. The files a lab works on are inferred, and stand under it apart.

Is required by dcterms:isRequiredBy

none found — the source did not declare it

What needs this resource, when a source declares it. The lab a component belongs to is inferred, and stands under it apart.

Provenance dcterms:provenance

this engine source
conversion latexml-html

Retrieved from arXiv on 2026-10-09 in response to the search string “(all:"artificial intelligence" OR all:"machine learning" OR all:"generative AI" OR all:"deep learning" OR all:"reinforcement learning" OR all:"large language model") AND (all:"AI concepts" OR all:"types of AI" OR all:"AI fundamentals" OR all:"recognizing AI" OR all:"recognising AI" OR all:"general versus narrow AI" OR all:"narrow AI" OR all:"general AI" OR all:"machine intelligence" OR all:"AI strengths and weaknesses" OR all:"traditional software" OR all:"rule-based systems" OR all:"introduction to AI" OR all:"introduction to artificial intelligence" OR all:"artificial intelligence introduction" OR all:"AI primer" OR all:"foundations of artificial intelligence" OR all:"overview of AI" OR all:"understanding AI" OR all:"history of AI" OR all:"AI essentials" OR all:"AI terminology" OR all:"metaphors for AI" OR all:"AI fundamental concepts" OR all:"AI key concepts" OR all:"philosophy of AI" OR all:"critical AI literacy")”. arXiv served the resource and is not asserted to be its publisher or author.
Text extracted from latexml-html to Markdown by arxiv-html; the original is retained unchanged beside it.

Where it was collected from, what was converted, and what container it came out of — the custody statements that would otherwise be mistaken for authorship.

IEEE 1484.12.1 Learning Object Metadata. LOM has no element for an SPDX identifier or a licence URI, so both are written into 6.3 Rights.Description. Flattening this record into simple Dublin Core would lose more again, which is why the two projections exist side by side rather than one being generated from the other. the standard ↗

1 General 5/8

Identifier 1.1

this engine resource_id
source resource/arxiv_id

URI: tag:aim-pro.eu,2026:oer/0c7c3e6bcfa8
URI: https://arxiv.org/abs/2410.15212
ARXIV: 2410.15212

This engine's identifier for the resource, the source URI, and any persistent identifier the source published.

Title 1.2

source resource/title

Boardwalk Empire: How Generative AI is Revolutionizing Economic Paradigms

Language 1.3

language gate read the declared field

The language of the content, as the language gate read it off the converted text. The language the source declared is kept separately.

Description 1.4

source resource/summary

The relentless pursuit of technological advancements has ushered in a new era where artificial intelligence (AI) is not only a powerful tool but also a critical economic driver. At the forefront of this transformation is Generative AI, which is catalyzing a paradigm shift across industries. Deep generative models, an integration of generative and deep learning techniques, excel in creating new data beyond analyzing existing ones, revolutionizing sectors from production and manufacturing to finance. By automating design, optimization, and innovation cycles, Generative AI is reshaping core industrial processes. In the financial sector, it is transforming risk assessment, trading strategies, and forecasting, demonstrating its profound impact. This paper explores the sweeping changes driven by deep learning models like Large Language Models (LLMs), highlighting their potential to foster innovative business models, disruptive technologies, and novel economic landscapes. As we stand at the threshold of an AI-driven economic era, Generative AI is emerging as a pivotal force, driving innovation, disruption, and economic evolution on a global scale.

The abstract, and the depositor's additional notes after it as a second LangString when the source has a field for them.

Keyword 1.5

source resource/categories/0

cs.CE
cs.CY
cs.LG
cs.SI

Coverage 1.6

not collected — this library does not fill it

The time, place or culture the resource applies to. No source declares it.

Structure 1.7

not collected — this library does not fill it

Not declared by any of the three sources, and not estimated here.

Aggregation level 1.8

not collected — this library does not fill it

Not declared by any of the three sources, and not estimated here.

2 Life cycle 3/3

Version 2.1 source resource: v2
Status 2.2 source resource: revised

Draft, final, revised or unavailable, and only when the source says so. A first version is not thereby final.
Contribute 2.3 source 2 assertions: Subramanyam Sahoo — author
Kamlesh Dutta — author

Role, entity and date per declared contribution. A role outside LOM's vocabulary is reported in the entry's description instead.

3 Meta-metadata 4/4

Identifier 3.1 this engine resource_id: URI: tag:aim-pro.eu,2026:oer/0c7c3e6bcfa8/record

The identifier of this metadata record — the resource's own, with /record after it, because the record is a description of the resource and not the resource.
Contribute 3.2 this engine source: AIM-PRO WP3 OER harvester (arXiv) — creator

Who generated this record and when — a statement about the record, not about the resource.
Metadata schema 3.3: LOMv1.0
aimpro-oer-profile/1

LOMv1.0, and the profile this was built against.
Language 3.4 language gate read the declared field: en

4 Technical 3/7

Format 4.1

conversion latexml-html

text/markdown

One value per form held: the original as the source published it, and the Markdown this engine extracted.

Size 4.2

conversion latexml-html

99442

Bytes. The original's, because the resource is the file and not our conversion of it.

Location 4.3

this engine resource_id

https://arxiv.org/abs/2410.15212

Where the source serves it.

Requirement 4.4

not collected — this library does not fill it

Software or hardware needed to use it. No source declares it.

Installation remarks 4.5

not collected — this library does not fill it

No source declares it.

Other platform requirements 4.6

not collected — this library does not fill it

No source declares it.

Duration 4.7

not collected — this library does not fill it

Playing time, for audio and video. The corpus holds neither.

5 Educational 1/11

Interactivity type 5.1

not collected — this library does not fill it

Active, expositive or mixed. A judgement about how the resource is used; no source declares it.

Learning resource type 5.2

source resource
mapped learning_resource_type/preprint

narrative text

Only from a declaration that maps onto LOM's vocabulary. A file format is not a declaration.

Interactivity level 5.3

not collected — this library does not fill it

No source declares it, and nothing here reads the resource to judge it.

Semantic density 5.4

not collected — this library does not fill it

No source declares it, and nothing here reads the resource to judge it.

Intended end user role 5.5

mapped learning_resource_type/preprint

none found — the source did not declare it

Imported when declared.

Context 5.6

mapped learning_resource_type/preprint

none found — the source did not declare it

Imported when declared.

Typical age range 5.7

not collected — this library does not fill it

No source declares it.

Difficulty 5.8

mapped learning_resource_type/preprint

none found — the source did not declare it

Imported when declared and never estimated.

Typical learning time 5.9

mapped learning_resource_type/preprint

none found — the source did not declare it

Imported when declared. Never computed from a word count.

Description 5.10

mapped learning_resource_type/preprint

none found — the source did not declare it

An explicit statement about educational use, when the source makes one.

Language 5.11

not collected — this library does not fill it

The language of the intended learner. Not assumed to be the content's: a lesson in English may be written for learners of it.

6 Rights 3/3

Cost 6.1 licence gate arXiv:arXiv/arXiv:license: no — free to use

No: the licence gate refuses anything it cannot establish open reuse terms for, before the bytes are requested.
Copyright and other restrictions 6.2 licence gate arXiv:arXiv/arXiv:license: yes — attribution required (CC-BY-4.0)

Yes unless the licence reserves nothing — attribution is a restriction. The conditions after the dash are the licence gate's reading; the export carries LOM's bare term.
Description 6.3 licence gate arXiv:arXiv/arXiv:license: CC-BY-4.0 · http://creativecommons.org/licenses/by/4.0/. Conditions: adaptation permitted, attribution required.

The SPDX id, the licence URI and the conditions. LOM has no element for any of the three, so this is where they survive.

7 Relation 0/2

Kind 7.1: none found — the source did not declare it
Resource 7.2 inferred read its reference list, line 254 citation: references: arXiv:2003.07679
references: arXiv:2303.17511
references: arXiv:1701.00160
references: arXiv:2103.04922
references: arXiv:1904.10604
references: arXiv:2301.05578
references: arXiv:2309.03736
references: arXiv:2306.14222
references: arXiv:2309.03079
references: arXiv:2009.06807
references: arXiv:2306.14504
references: arXiv:2112.04359
and 22 more, every one of them in the export

The container a file was found inside, and the Markdown extracted from the original. What a lab requires, and the lab a component belongs to, are inferred and stand apart.

8 Annotation 0/3

Entity 8.1

not collected — this library does not fill it

Comments on the resource's educational use, by whoever made them. The platform's review grades competencies, which are classification (9), and writes no comment here.

Date 8.2

not collected — this library does not fill it

Description 8.3

not collected — this library does not fill it

9 Classification 0/4

Purpose 9.1

not collected — this library does not fill it

Empty in the record: no source declares a competency. The alignment reads the resource for them and stands beside the record, never in it, and a taxon path derived from the search string that found it would be a claim about the query.

Taxon path 9.2

not collected — this library does not fill it

Where the competency framework goes. Empty in the record for the reason above.

Description 9.3

not collected — this library does not fill it

Keyword 9.4

not collected — this library does not fill it

What could not be established3

Where the source's metadata could not be carried over as it was — missing, contradictory, with no matching term in the standard, restructured, or taken from the repository — and what was done instead. Without these notes, an empty element would look like something the harvester missed.

Status	Field	Why
Not available	`rights_holder`	no rights holder is named at source; the licence is recorded without one rather than attributed to the platform that served it
Not available	`publisher`	the source named no publisher of the work; where it was collected from is recorded as collection provenance instead, which is a different claim
Not available	`educational`	the source declared no educational metadata — no resource type, audience, context, difficulty or learning time. Nothing here estimates them