IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v15y2023i8p260-d1207623.html
   My bibliography  Save this article

The Power of Generative AI: A Review of Requirements, Models, Input–Output Formats, Evaluation Metrics, and Challenges

Author

Listed:
  • Ajay Bandi

    (School of Computer Science and Information Systems, Northwest Missouri State University, Maryville, MO 64468, USA)

  • Pydi Venkata Satya Ramesh Adapa

    (School of Computer Science and Information Systems, Northwest Missouri State University, Maryville, MO 64468, USA)

  • Yudu Eswar Vinay Pratap Kumar Kuchi

    (School of Computer Science and Information Systems, Northwest Missouri State University, Maryville, MO 64468, USA)

Abstract

Generative artificial intelligence (AI) has emerged as a powerful technology with numerous applications in various domains. There is a need to identify the requirements and evaluation metrics for generative AI models designed for specific tasks. The purpose of the research aims to investigate the fundamental aspects of generative AI systems, including their requirements, models, input–output formats, and evaluation metrics. The study addresses key research questions and presents comprehensive insights to guide researchers, developers, and practitioners in the field. Firstly, the requirements necessary for implementing generative AI systems are examined and categorized into three distinct categories: hardware, software, and user experience. Furthermore, the study explores the different types of generative AI models described in the literature by presenting a taxonomy based on architectural characteristics, such as variational autoencoders (VAEs), generative adversarial networks (GANs), diffusion models, transformers, language models, normalizing flow models, and hybrid models. A comprehensive classification of input and output formats used in generative AI systems is also provided. Moreover, the research proposes a classification system based on output types and discusses commonly used evaluation metrics in generative AI. The findings contribute to advancements in the field, enabling researchers, developers, and practitioners to effectively implement and evaluate generative AI models for various applications. The significance of the research lies in understanding that generative AI system requirements are crucial for effective planning, design, and optimal performance. A taxonomy of models aids in selecting suitable options and driving advancements. Classifying input–output formats enables leveraging diverse formats for customized systems, while evaluation metrics establish standardized methods to assess model quality and performance.

Suggested Citation

  • Ajay Bandi & Pydi Venkata Satya Ramesh Adapa & Yudu Eswar Vinay Pratap Kumar Kuchi, 2023. "The Power of Generative AI: A Review of Requirements, Models, Input–Output Formats, Evaluation Metrics, and Challenges," Future Internet, MDPI, vol. 15(8), pages 1-60, July.
  • Handle: RePEc:gam:jftint:v:15:y:2023:i:8:p:260-:d:1207623
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/15/8/260/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1999-5903/15/8/260/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xingyu Zhou & Zhisong Pan & Guyu Hu & Siqi Tang & Cheng Zhao, 2018. "Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-11, April.
    2. Florian Eckerli & Joerg Osterrieder, 2021. "Generative Adversarial Networks in finance: an overview," Papers 2106.06364, arXiv.org, revised Jul 2021.
    3. Pratyush Muthukumar & Jie Zhong, 2021. "A Stochastic Time Series Model for Predicting Financial Trends using NLP," Papers 2102.01290, arXiv.org.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shaotong Qi & Yubo Cheng & Zhiyuan Li & Jiaxin Wang & Huaiyi Li & Chunwei Zhang, 2024. "Advanced Deep Learning Techniques for Battery Thermal Management in New Energy Vehicles," Energies, MDPI, vol. 17(16), pages 1-38, August.
    2. Hyun Yong Ahn, 2024. "AI-Powered E-Learning for Lifelong Learners: Impact on Performance and Knowledge Application," Sustainability, MDPI, vol. 16(20), pages 1-20, October.
    3. Safa Jameel Al-Kamil & Róbert Szabolcsi, 2024. "Enhancing Mobile Robot Navigation: Optimization of Trajectories through Machine Learning Techniques for Improved Path Planning Efficiency," Mathematics, MDPI, vol. 12(12), pages 1-21, June.
    4. Theodora Sanida & Maria Vasiliki Sanida & Argyrios Sideris & Minas Dasygenis, 2024. "Enhancing Pulmonary Diagnosis in Chest X-rays through Generative AI Techniques," J, MDPI, vol. 7(3), pages 1-17, August.
    5. Nikša Alfirević & Daniela Garbin Praničević & Mirela Mabić, 2024. "Custom-Trained Large Language Models as Open Educational Resources: An Exploratory Research of a Business Management Educational Chatbot in Croatia and Bosnia and Herzegovina," Sustainability, MDPI, vol. 16(12), pages 1-18, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Solveig Flaig & Gero Junike, 2022. "Scenario Generation for Market Risk Models Using Generative Neural Networks," Risks, MDPI, vol. 10(11), pages 1-28, October.
    2. Jos'e-Manuel Pe~na & Fernando Su'arez & Omar Larr'e & Domingo Ram'irez & Arturo Cifuentes, 2023. "A Modified CTGAN-Plus-Features Based Method for Optimal Asset Allocation," Papers 2302.02269, arXiv.org, revised May 2024.
    3. Tomonori Takahashi & Takayuki Mizuno, 2024. "Generation of synthetic financial time series by diffusion models," Papers 2410.18897, arXiv.org.
    4. Nicolas Boursin & Carl Remlinger & Joseph Mikael & Carol Anne Hargreaves, 2022. "Deep Generators on Commodity Markets; application to Deep Hedging," Papers 2205.13942, arXiv.org.
    5. Mourad Mroua & Ahlem Lamine, 2023. "Financial time series prediction under Covid-19 pandemic crisis with Long Short-Term Memory (LSTM) network," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-15, December.
    6. Rudy Morel & Gaspar Rochette & Roberto Leonarduzzi & Jean-Philippe Bouchaud & St'ephane Mallat, 2022. "Scale Dependencies and Self-Similar Models with Wavelet Scattering Spectra," Papers 2204.10177, arXiv.org, revised Jun 2023.
    7. Jun Lu & Danny Ding, 2022. "A Hybrid Approach on Conditional GAN for Portfolio Analysis," Papers 2208.07159, arXiv.org.
    8. Jun Lu & Shao Yi, 2022. "Autoencoding Conditional GAN for Portfolio Allocation Diversification," Papers 2207.05701, arXiv.org.
    9. Ali Asgarov, 2023. "Predicting Financial Market Trends using Time Series Analysis and Natural Language Processing," Papers 2309.00136, arXiv.org.
    10. Emiel Lemahieu & Kris Boudt & Maarten Wyns, 2023. "Generating drawdown-realistic financial price paths using path signatures," Papers 2309.04507, arXiv.org.
    11. Cheng Zhang & Nilam Nur Amir Sjarif & Roslina Ibrahim, 2023. "Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020-2022," Papers 2305.04811, arXiv.org, revised Sep 2023.
    12. Yoojeong Song & Jae Won Lee & Jongwoo Lee, 2022. "Development of Intelligent Stock Trading System Using Pattern Independent Predictor and Turning Point Matrix," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 27-38, January.
    13. Fateme Shahabi Nejad & Mohammad Mehdi Ebadzadeh, 2023. "Stock market forecasting using DRAGAN and feature matching," Papers 2301.05693, arXiv.org.
    14. Solveig Flaig & Gero Junike, 2021. "Scenario generation for market risk models using generative neural networks," Papers 2109.10072, arXiv.org, revised Aug 2023.
    15. Jun Lu & Shao Yi, 2022. "Autoencoding Conditional GAN for Portfolio Allocation Diversification," Applied Economics and Finance, Redfame publishing, vol. 9(3), pages 55-68, August.
    16. Carl Remlinger & Joseph Mikael & Romuald Elie, 2022. "Robust Operator Learning to Solve PDE," Working Papers hal-03599726, HAL.
    17. Florian Eckerli & Joerg Osterrieder, 2021. "Generative Adversarial Networks in finance: an overview," Papers 2106.06364, arXiv.org, revised Jul 2021.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:15:y:2023:i:8:p:260-:d:1207623. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.