Cutting Through the Confusion and Hype: Understanding the True Potential of Generative AI
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866917812484702208 |
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| author | Prodan, Ante Occhipinti, Jo-An Ahlip, Rehez Ujdur, Goran Eyre, Harris A. Goosen, Kyle Penza, Luke Heffernan, Mark |
| author_facet | Prodan, Ante Occhipinti, Jo-An Ahlip, Rehez Ujdur, Goran Eyre, Harris A. Goosen, Kyle Penza, Luke Heffernan, Mark |
| contents | This paper explores the nuanced landscape of generative AI (genAI), particularly focusing on neural network-based models like Large Language Models (LLMs). While genAI garners both optimistic enthusiasm and sceptical criticism, this work seeks to provide a balanced examination of its capabilities, limitations, and the profound impact it may have on societal functions and personal interactions. The first section demystifies language-based genAI through detailed discussions on how LLMs learn, their computational needs, distinguishing features from supporting technologies, and the inherent limitations in their accuracy and reliability. Real-world examples illustrate the practical applications and implications of these technologies. The latter part of the paper adopts a systems perspective, evaluating how the integration of LLMs with existing technologies can enhance productivity and address emerging concerns. It highlights the need for significant investment to understand the implications of recent advancements, advocating for a well-informed dialogue to ethically and responsibly integrate genAI into diverse sectors. The paper concludes with prospective developments and recommendations, emphasizing a forward-looking approach to harnessing genAI`s potential while mitigating its risks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_16629 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Cutting Through the Confusion and Hype: Understanding the True Potential of Generative AI Prodan, Ante Occhipinti, Jo-An Ahlip, Rehez Ujdur, Goran Eyre, Harris A. Goosen, Kyle Penza, Luke Heffernan, Mark Computers and Society Information Retrieval Multiagent Systems This paper explores the nuanced landscape of generative AI (genAI), particularly focusing on neural network-based models like Large Language Models (LLMs). While genAI garners both optimistic enthusiasm and sceptical criticism, this work seeks to provide a balanced examination of its capabilities, limitations, and the profound impact it may have on societal functions and personal interactions. The first section demystifies language-based genAI through detailed discussions on how LLMs learn, their computational needs, distinguishing features from supporting technologies, and the inherent limitations in their accuracy and reliability. Real-world examples illustrate the practical applications and implications of these technologies. The latter part of the paper adopts a systems perspective, evaluating how the integration of LLMs with existing technologies can enhance productivity and address emerging concerns. It highlights the need for significant investment to understand the implications of recent advancements, advocating for a well-informed dialogue to ethically and responsibly integrate genAI into diverse sectors. The paper concludes with prospective developments and recommendations, emphasizing a forward-looking approach to harnessing genAI`s potential while mitigating its risks. |
| title | Cutting Through the Confusion and Hype: Understanding the True Potential of Generative AI |
| topic | Computers and Society Information Retrieval Multiagent Systems |
| url | https://arxiv.org/abs/2410.16629 |