Exploring the potential of large language model–based chatbots in challenges of ribosome profiling data analysis: a review DOI Creative Commons
Zheyu Ding, Rong Wei,

Jianing Xia

et al.

Briefings in Bioinformatics, Journal Year: 2024, Volume and Issue: 26(1)

Published: Nov. 22, 2024

Abstract Ribosome profiling (Ribo-seq) provides transcriptome-wide insights into protein synthesis dynamics, yet its analysis poses challenges, particularly for nonbioinformatics researchers. Large language model–based chatbots offer promising solutions by leveraging natural processing. This review explores their convergence, highlighting opportunities synergy. We discuss challenges in Ribo-seq and how mitigate them, facilitating scientific discovery. Through case studies, we illustrate chatbots’ potential contributions, including data result interpretation. Despite the absence of applied examples, existing software underscores value large model. anticipate pivotal role future analysis, overcoming limitations. Challenges such as model bias privacy require attention, but emerging trends promise. The integration models holds immense advancing translational regulation gene expression understanding.

Language: Английский

Gemini versus ChatGPT: applications, performance, architecture, capabilities, and implementation DOI Creative Commons

Nitin Rane,

Saurabh Choudhary, Jayesh Rane

et al.

Journal of Applied Artificial Intelligence, Journal Year: 2024, Volume and Issue: 5(1), P. 69 - 93

Published: March 20, 2024

This research paper presents an in-depth comparative examination of Gemini and ChatGPT, two prominent conversational AI models, exploring their respective applications, performance metrics, architectural variances, overall capabilities. As becomes increasingly prevalent across industries, comprehending the nuances these models pivotal for effective deployment. The initiates by outlining wide array applications both spanning industries such as customer service, construction, finance, education, healthcare, entertainment. It analyzes how each model addresses specific use cases, emphasizing flexibility potential impact different sectors. Following this, study assesses ChatGPT through empirical benchmarks real-world deployment scenarios. Key including response coherence, accuracy, latency, scalability, are scrutinized to gauge models' ability generate contextually appropriate coherent responses in contexts. Moreover, elucidates distinctions between covering variances training methodologies, architectures, underlying technologies. Understanding provides deeper insights into computational mechanisms underpinning model's performance. Lastly, explores capabilities handling complex linguistic phenomena, deciphering user intents, sustaining engaging dialogues over prolonged interactions. discussion encompasses language generation, sentiment analysis, context retention, ethical considerations, shedding light on facilitate meaningful human-computer Through this thorough contributes ongoing conversation surrounding systems. offers valuable strengths limitations empowering stakeholders make informed decisions regarding optimal utilization diverse applications.

Language: Английский

Citations

19
Gemini Versus ChatGPT: Applications, Performance, Architecture, Capabilities, and Implementation DOI

Nitin Rane,

Saurabh Choudhary, Jayesh Rane

et al.

SSRN Electronic Journal, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

Language: Английский

Citations

12
Artificial intelligence, machine learning, deep learning, and blockchain in financial and banking services: a comprehensive review DOI

Mallikarjuna Paramesha,

Nitin Rane,

Jayesh Rane

et al.

SSRN Electronic Journal, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

Language: Английский

Citations

11
Artificial intelligence for low income countries DOI Creative Commons
Muhammad Salar Khan, Hamza Umer,

Farhana Faruqe

et al.

Humanities and Social Sciences Communications, Journal Year: 2024, Volume and Issue: 11(1)

Published: Oct. 25, 2024

Language: Английский

Citations

11
Digital twin-based cyber-physical manufacturing systems, extended reality metaverse enterprise and production management algorithms, and Internet of Things financial and labor market technologies in generative artificial intelligence economics DOI Creative Commons
George Lăzăroiu, Tom Gedeon, Elżbieta Rogalska

et al.

Oeconomia Copernicana, Journal Year: 2024, Volume and Issue: 15(3), P. 837 - 870

Published: Sept. 30, 2024

Research background: Generative artificial intelligence (AI) and machine learning algorithms support industrial Internet of Things (IoT)-based big data enterprise asset management in multiphysics simulation environments by processing, modeling, monitoring, enabling business organizational managerial practices. Machine learning-based decision edge generative AI sensing systems can reduce persistent labor shortages job vacancies power productivity growth market dynamics, shaping career pathways facilitating occupational transitions skill gap identification labor-intensive manufacturing automation path planning spatial cognition algorithms, furthering theoretical implications for sciences. fintech, behavioral analytics assist multi-layered payment transaction processing screening with regard to authorized push payment, account takeover, synthetic identity frauds, flagging suspicious activities combating economic crimes rigorous verification processes. Purpose the article: We show that device functionalities cloud IoT virtual robotic technologies configure plant production route processes across cyber-physical multi-cloud immersive 3D environments, leading tangible outcomes reinforcement convolutional neural networks. Labor-augmenting impact employment participation, increase wage wealth inequality, lead potential displacement massive disruptions. The deep capabilities fintech terms adaptive credit scoring mechanisms enhance financial behaviors algorithmic trading returns, identify fraudulent transactions swiftly, improve forecasts, customized investment recommendations well-informed decisions. Methods: study selection process text mining systematic review software tools leveraged include Abstrackr, CADIMA, Colandr, DistillerSR, EPPI-Reviewer, JBI SUMARI, METAGEAR package R, SluRp, SWIFT-Active Screener. Such reference are harnessed methodologically evidence synthesis, characteristic extraction, predictive document classification, citation record screening, bias assessment, article retrieval automation, classification prioritization. Findings & value added: Industrial augmented reality create streamlining product remote extended reality-based navigation autonomous smart factory articulating level theory implications. operational modeling execute complete complex cognitive task-oriented knowledge economy jobs, producing first-rate quality outputs swiftly while unemployment spells, disruptions, losses, reduced earnings clustering algorithms. decentralized finance, interoperable blockchain networks, cash flow tools, tokenization mitigate fraud risks, enable digital fund crypto investing servicing, automate treasury operations integrating real-time capabilities, routing configurable workflows, lending technologies.

Language: Английский

Citations

6
Ethical dimensions of generative AI: a cross-domain analysis using machine learning structural topic modeling DOI
Hassnian Ali, Ahmet Faruk Aysan

International Journal of Ethics and Systems, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 3, 2024

Purpose The purpose of this study is to comprehensively examine the ethical implications surrounding generative artificial intelligence (AI). Design/methodology/approach Leveraging a novel methodological approach, curates corpus 364 documents from Scopus spanning 2022 2024. Using term frequency-inverse document frequency (TF-IDF) and structural topic modeling (STM), it quantitatively dissects thematic essence discourse in AI across diverse domains, including education, healthcare, businesses scientific research. Findings results reveal range concerns various sectors impacted by AI. In academia, primary focus on issues authenticity intellectual property, highlighting challenges AI-generated content maintaining academic integrity. healthcare sector, emphasis shifts medical decision-making patient privacy, reflecting about reliability security advice. also uncovers significant discussions educational financial settings, demonstrating broad impact societal professional practices. Research limitations/implications This provides foundation for crafting targeted guidelines regulations AI, informed systematic analysis using STM. It highlights need dynamic governance continual monitoring AI’s evolving landscape, offering model future research policymaking fields. Originality/value introduces unique combination TF-IDF STM analyze large corpus, new insights into multiple domains.

Language: Английский

Citations

5
Framework for Integrating Generative AI in Developing Competencies for Accounting and Audit Professionals DOI Open Access
Ionuț Anica-Popa, Marinela Vrîncianu, Liana-Elena Anica-Popa

et al.

Electronics, Journal Year: 2024, Volume and Issue: 13(13), P. 2621 - 2621

Published: July 4, 2024

The study aims to identify the knowledge, skills and competencies required by accounting auditing (AA) professionals in context of integrating disruptive Generative Artificial Intelligence (GenAI) technologies develop a framework for GenAI capabilities into organisational systems, harnessing its potential revolutionise lifelong learning development assist day-to-day operations decision-making. Through systematic literature review, 103 papers were analysed, outline, current business ecosystem, competencies’ demand generated AI adoption and, particular, associated risks, thus contributing body knowledge underexplored research areas. Positioned at confluence accounting, GenAI, paper introduces meaningful overview areas effective data analysis, interpretation findings, risk awareness management. It emphasizes reshapes role discovering true adopting it accordingly. new LLM-based system model that can enhance through collaboration with similar systems provides an explanatory scenario illustrate applicability audit area.

Language: Английский

Citations

4
Leveraging ChatGPT for enhanced stock selection and portfolio optimization DOI

Zhendai Huang,

Bolin Liao, Cheng Hua

et al.

Neural Computing and Applications, Journal Year: 2025, Volume and Issue: 37(8), P. 6163 - 6179

Published: Jan. 9, 2025

Language: Английский

Citations

0
Asking an AI for salary negotiation advice is a matter of concern: Controlled experimental perturbation of ChatGPT for protected and non-protected group discrimination on a contextual task with no clear ground truth answers DOI Creative Commons
R. Stuart Geiger, Fiona O’Sullivan,

Elsie Wang

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(2), P. e0318500 - e0318500

Published: Feb. 7, 2025

We conducted controlled experimental bias audits for four versions of ChatGPT, which we asked to recommend an opening offer in salary negotiations a new hire. submitted 98,800 prompts each version, systematically varying the employee's gender, university, and major, tested voice side negotiation: employee versus their employer. Empirically, find many reasons why ChatGPT as multi-model platform is not robust consistent enough be trusted such task. observed statistically significant offers when gender all models, although with smaller gaps than other attributes tested. The most substantial were different model between employee- vs employer-voiced prompts. also university but biases across versions. fictional fraudulent universities found wildly inconsistent results cases make broader contributions AI/ML fairness trustworthiness literature. Our negotiation advice scenario our design differ from mainstream auditing efforts key ways. Bias typically test discrimination protected classes like contrast testing non-protected major. Asking includes how aggressive one ought relative known empirical distributions scales, deeply contextual personalized task that has no objective ground truth validate. These raise concerns only specific tested, around consistency robustness web continuous development. epistemology does permit us definitively certify these models either generally biased or unbiased on test, study raises matters concern stakeholders further investigate.

Language: Английский

Citations

0
Leveraging Prompt Engineering to Enhance Financial Market Integrity and Risk Management DOI
Sumit Joshi

SSRN Electronic Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

Citations

0