Artificial intelligence foundation and pre-trained models: Fundamentals, applications, opportunities, and social impacts

Kolides, Adam; Nawaz, Alyna; Rathor, Anshu; Beeman, Denzel; Hashmi, Muzammil; Fatima, Sana; Berdik, David; Al‐Ayyoub, Mahmoud; Jararweh, Yaser

doi:10.1016/j.simpat.2023.102754

Cited by 30 publications

(8 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ethical and legal challenges related to AI-generated content are also a recurrent topic ( Lee, 2023b ; Salvagno, Taccone & Gerli, 2023 ; Ray, 2023 ; Karaali, 2023 ; Dwivedi et al, 2023 ), especially in the context of academic research and education, where concerns about authenticity and academic integrity arise. Moreover, many studies explore ChatGPT’s potential benefits and capabilities ( Victor et al, 2023 ; Farrokhnia et al, 2023 ; Halaweh, 2023 ; Kooli, 2023 ; Cox & Tzoc, 2023 ; Carvalho & Ivanov, 2023 ; Jungwirth & Haluza, 2023 ; Cascella et al, 2023 ; Kolides et al, 2023 ; Dwivedi et al, 2023 ), and potential to address societal megatrends ( Haluza & Jungwirth, 2023 ), with a focus on its role in writing, research, and pedagogy, suggesting a shift in educational paradigms.…”

Section: Study Background and Related Workmentioning

confidence: 99%

“…Furthermore, the majority of the studies encompass exploratory approaches ( Haluza & Jungwirth, 2023 ; Yeadon et al, 2023 ; Victor et al, 2023 ; Dwivedi et al, 2023 ; Rozado, 2023 ; Cascella et al, 2023 ; Ariyaratne et al, 2023 ; Yan, 2023 ; Grünebaum et al, 2023 ; Kooli, 2023 ; Jungwirth & Haluza, 2023 ; Geerling et al, 2023 ; Gilson et al, 2023 ; Cooper, 2023 ; Short & Short, 2023 ). Similarly, several studies take the form of reviews, such as Ray (2023) , Lee (2023b) , Thurzo et al (2023) , Carvalho & Ivanov (2023) and Kolides et al (2023) . Moreover, text analysis is employed in studies conducted by Taecharungroj (2023) and Tlili et al (2023) .…”

Section: Study Background and Related Workmentioning

confidence: 99%

See 1 more Smart Citation

An integrative decision-making framework to guide policies on regulating ChatGPT usage

Bukar,

Sayeed,

Abdul Razak

et al. 2024

PeerJ Computer Science

View full text Add to dashboard Cite

Generative artificial intelligence has created a moment in history where human beings have begin to closely interact with artificial intelligence (AI) tools, putting policymakers in a position to restrict or legislate such tools. One particular example of such a tool is ChatGPT which is the first and world's most popular multipurpose generative AI tool. This study aims to put forward a policy-making framework of generative artificial intelligence based on the risk, reward, and resilience framework. A systematic search was conducted, by using carefully chosen keywords, excluding non-English content, conference articles, book chapters, and editorials. Published research were filtered based on their relevance to ChatGPT ethics, yielding a total of 41 articles. Key elements surrounding ChatGPT concerns and motivations were systematically deduced and classified under the risk, reward, and resilience categories to serve as ingredients for the proposed decision-making framework. The decision-making process and rules were developed as a primer to help policymakers navigate decision-making conundrums. Then, the framework was practically tailored towards some of the concerns surrounding ChatGPT in the context of higher education. In the case of the interconnection between risk and reward, the findings show that providing students with access to ChatGPT presents an opportunity for increased efficiency in tasks such as text summarization and workload reduction. However, this exposes them to risks such as plagiarism and cheating. Similarly, pursuing certain opportunities such as accessing vast amounts of information, can lead to rewards, but it also introduces risks like misinformation and copyright issues. Likewise, focusing on specific capabilities of ChatGPT, such as developing tools to detect plagiarism and misinformation, may enhance resilience in some areas (e.g., academic integrity). However, it may also create vulnerabilities in other domains, such as the digital divide, educational equity, and job losses. Furthermore, the finding indicates second-order effects of legislation regarding ChatGPT which have implications both positively and negatively. One potential effect is a decrease in rewards due to the limitations imposed by the legislation, which may hinder individuals from fully capitalizing on the opportunities provided by ChatGPT. Hence, the risk, reward, and resilience framework provides a comprehensive and flexible decision-making model that allows policymakers and in this use case, higher education institutions to navigate the complexities and trade-offs associated with ChatGPT, which have theoretical and practical implications for the future.

show abstract

Section: Study Background and Related Workmentioning

confidence: 99%

Section: Study Background and Related Workmentioning

confidence: 99%

An integrative decision-making framework to guide policies on regulating ChatGPT usage

Bukar,

Sayeed,

Abdul Razak

et al. 2024

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…Deep neural networks offer considerable potential for identifying brain illnesses and providing prognosis predictions based on neuroimaging data, but significant, labeled training datasets are often needed for excellent predictive accuracy [15,18]. The authors of [19] explored a variety of pretraining and transfer learning (TL) [20] techniques to construct usable MRI representations for downstream tasks that lack significant quantities of training data, such as AD classification [21], in the absence of vast amounts of training data. The scientists studied 4098 3D T1-weighted brain MRI images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort and 600 scans from the Open Access Series of Imaging Studies (OASIS3) [22] cohort to assess the suggested pretraining methodologies for identifying AD.…”

Section: Related Workmentioning

confidence: 99%

Alzheimer’s Disease Prediction Using Deep Feature Extraction and Optimization

Mohammad,

Al Ahmadi

2023

Mathematics

View full text Add to dashboard Cite

Alzheimer’s disease (AD) is a prevalent neurodegenerative disorder that affects a substantial proportion of the population. The accurate and timely prediction of AD carries considerable importance in enhancing the diagnostic process and improved treatment. This study provides a thorough examination of AD prediction using the VGG19 deep learning model. The primary objective of this study is to investigate the effectiveness of feature fusion and optimization techniques in enhancing the accuracy of classification. The generation of a comprehensive feature map is achieved through the fusion of features that have been extracted from the fc7 and fc8 layers of VGG19. Several machine learning algorithms are employed to classify integrated features and recognize AD. The amalgamated feature map demonstrates a significant level of accuracy of 98% in the prognostication of AD, outperforming present cutting-edge methodologies. In this study, a methodology is utilized that makes use of the whale optimization algorithm (WoA), a metaheuristic approach to optimize features through feature selection. Feature optimization aims to eliminate redundant features and enhance the discriminatory power of the selected features. Following the optimization procedure, the F-KNN algorithm attained a precision level of 99%, surpassing the present state-of-the-art (SOTA) results reported in the current literature.

show abstract

“…The general steps performed by ChatGPT are described as follows [24,25]: (a) the system enables the user to create a prompt message, which may contain specific commands, types of questions, etc., (b) the system tokenizes the above message by separating it into words or sequences of words that are going to be analyzed, (c) the tokenized output from the previous step is fed into the transformer-based neural network, and (d) the transformer elaborates on the resulting input and, using specialized inference mechanisms, provides a text-based answer to the user.…”

Section: Chatgpt 211 Capabilities and Functionality Of Chatgptmentioning

confidence: 99%

“…As a result, it appears to possess a more effective performance in various downstream endeavors related to classifying texts, answering questions, sentiment analysis, etc. The model also uses a transformer-based neural network with 48 transformer blocks, 1600 dimensions, and 1.5 billion parameters, while the context window uses 1024 tokens [13,24,25].…”

Section: Gpt Versions and Technologies Involvedmentioning

confidence: 99%

Using ChatGPT and Persuasive Technology for Personalized Recommendation Messages in Hotel Upselling

Remountakis,

Kotis,

Kourtzis

et al. 2023

Information

View full text Add to dashboard Cite

Recommender systems have become indispensable tools in the hotel hospitality industry, enabling personalized and tailored experiences for guests. Recent advancements in large language models (LLMs), such as ChatGPT, and persuasive technologies have opened new avenues for enhancing the effectiveness of those systems. This paper explores the potential of integrating ChatGPT and persuasive technologies for automating and improving hotel hospitality recommender systems. First, we delve into the capabilities of ChatGPT, which can understand and generate human-like text, enabling more accurate and context-aware recommendations. We discuss the integration of ChatGPT into recommender systems, highlighting the ability to analyze user preferences, extract valuable insights from online reviews, and generate personalized recommendations based on guest profiles. Second, we investigate the role of persuasive technology in influencing user behavior and enhancing the persuasive impact of hotel recommendations. By incorporating persuasive techniques, such as social proof, scarcity, and personalization, recommender systems can effectively influence user decision making and encourage desired actions, such as booking a specific hotel or upgrading their room. To investigate the efficacy of ChatGPT and persuasive technologies, we present pilot experiments with a case study involving a hotel recommender system. Our inhouse commercial hotel marketing platform, eXclusivi, was extended with a new software module working with ChatGPT prompts and persuasive ads created for its recommendations. In particular, we developed an intelligent advertisement (ad) copy generation tool for the hotel marketing platform. The proposed approach allows for the hotel team to target all guests in their language, leveraging the integration with the hotel’s reservation system. Overall, this paper contributes to the field of hotel hospitality by exploring the synergistic relationship between ChatGPT and persuasive technology in recommender systems, ultimately influencing guest satisfaction and hotel revenue.

show abstract

Artificial intelligence foundation and pre-trained models: Fundamentals, applications, opportunities, and social impacts

Cited by 30 publications

References 60 publications

An integrative decision-making framework to guide policies on regulating ChatGPT usage

An integrative decision-making framework to guide policies on regulating ChatGPT usage

Alzheimer’s Disease Prediction Using Deep Feature Extraction and Optimization

Using ChatGPT and Persuasive Technology for Personalized Recommendation Messages in Hotel Upselling

Contact Info

Product

Resources

About