Fetching the paper…
Reading the bibliography…
The Generative Pre-trained Transformer (GPT) represents a notable breakthrough in the domain of natural language processing, which is propelling us toward the development of machines that can understand and communicate using language in a manner that closely resembles that of humans.
1904
Earlier work this paper cites.
J. Bulchand-Gidumal, “Impact of artificial intelligence in travel, tourism, and hospitality,” in Handbook of e-Tourism . Springer, 2022, pp. 1943–1962
1962
Earlier work this paper cites.
A. J. Veal, “The concept of lifestyle: a review,” Leisure Studies , vol. 12, no. 4, pp. 233–252, 1993
1993
Earlier work this paper cites.
P. Contoyannis and A. M. Jones, “Socio-economic status, health and lifestyle,” Journal of health economics , vol. 23, no. 5, pp. 965–995, 2004
2004
Earlier work this paper cites.
M. J. Reeves and A. P. Rafferty, “Healthy lifestyle characteristics among adults in the united states, 2000,” Archives of internal medicine , vol. 165, no. 8, pp. 854–857, 2005
2005
Earlier work this paper cites.
M. Jensen, “Defining lifestyle,” Environmental sciences , vol. 4, no. 2, pp. 63–73, 2007
2007
Earlier work this paper cites.
F. Etro, “The economic consequences of the diffusion of cloud computing,” Dutta, Soumitra; Mia, Irene. The Global Information Technology Report , vol. 2010, 2009
2009
Earlier work this paper cites.
J. Bryant and P. Vorderer, Psychology of entertainment . Routledge, 2013
2013
Earlier work this paper cites.
E. Gawehn, J. A. Hiss, and G. Schneider, “Deep learning in drug discovery,” Molecular informatics , vol. 35, no. 1, pp. 3–14, 2016
2016
Earlier work this paper cites.
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep learning with differential privacy,” in Proceedings of the 2016 ACM SIGSAC conference on computer and communications security , 2016, pp. 308–318
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
L. Dong, S. Xu, and B. Xu, “Speech-transformer: a no-recurrence sequence-to-sequence model for speech recognition,” in 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP) . IEEE, 2018, pp. 5884–5888
2018
Earlier work this paper cites.
M. Trajtenberg, “Ai as the next gpt: a political-economy perspective,” National Bureau of Economic Research, Tech. Rep., 2018
2018
Earlier work this paper cites.
M. Trajtenberg, “Artificial intelligence as the next gpt: A political-economy perspective,” in The economics of artificial intelligence: An agenda . University of Chicago Press, 2018, pp. 175–186
2018
Earlier work this paper cites.
Z. Li, M. A. Uusitalo, H. Shariatmadari, and B. Singh, “5g urllc: Design challenges and system concepts,” in 2018 15th International Symposium on Wireless Communication Systems (ISWCS) , 2018, pp. 1–6
2018
Earlier work this paper cites.
H. Chen, O. Engkvist, Y. Wang, M. Olivecrona, and T. Blaschke, “The rise of deep learning in drug discovery,” Drug discovery today , vol. 23, no. 6, pp. 1241–1250, 2018
2018
Earlier work this paper cites.
M. H. Segler, T. Kogej, C. Tyrchan, and M. P. Waller, “Generating focused molecule libraries for drug discovery with recurrent neural networks,” ACS central science , vol. 4, no. 1, pp. 120–131, 2018
2018
Earlier work this paper cites.
F. Pesapane, M. Codari, and F. Sardanelli, “Artificial intelligence in medical imaging: threat or opportunity? radiologists again at the forefront of innovation in medicine,” European radiology experimental , vol. 2, pp. 1–10, 2018
2018
Earlier work this paper cites.
F. Wei and U. T. Nguyen, “Stock trend prediction using financial market news and bert,” Wall Street Journal , 2018
2018
Earlier work this paper cites.
S. Edunov, A. Baevski, and M. Auli, “Pre-trained language model representations for language generation,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) . Minneapolis, Minnesota: Association for Computational Linguistics, Jun. 2019, pp. 4052–4059. [Online]. Available: https://aclanthology.org/N19-1409
2019
Earlier work this paper cites.
R. Ressmeyer, S. Masling, and M. Liao, ““deep faking” political twitter using transfe r learning and gpt-2,” 2019
2019
Earlier work this paper cites.
J. S. Wey and J. Zhang, “Passive optical networks for 5g transport: Technology and standards,” Journal of Lightwave Technology , vol. 37, no. 12, pp. 2830–2837, 2019
2019
Earlier work this paper cites.
A. Gonzalez Fanfalone et al. , “The road to 5g networks: experience to date and future developments,” 2019
2019
Earlier work this paper cites.
J. Vamathevan, D. Clark, P. Czodrowski, I. Dunham, E. Ferran, G. Lee, B. Li, A. Madabhushi, P. Shah, M. Spitzer et al. , “Applications of machine learning in drug discovery and development,” Nature reviews Drug discovery , vol. 18, no. 6, pp. 463–477, 2019
2019
Earlier work this paper cites.
A. Lavecchia, “Deep learning in drug discovery: opportunities, challenges and future prospects,” Drug discovery today , vol. 24, no. 10, pp. 2017–2032, 2019
2019
Earlier work this paper cites.
B. Balsmeier and M. Woerter, “Is this time different? how digitalization influences job creation and destruction,” Research policy , vol. 48, no. 8, p. 103765, 2019
2019
Earlier work this paper cites.
M. S. Farooq, S. Riaz, A. Abid, K. Abid, and M. A. Naeem, “A survey on the role of iot in agriculture for the implementation of smart farming,” Ieee Access , vol. 7, pp. 156 237–156 271, 2019
2019
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019
2019
Earlier work this paper cites.
X.-R. Gong, J.-X. Jin, and T. Zhang, “Sentiment analysis using autoregressive language modeling and broad learning system,” in 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) . IEEE, 2019, pp. 1130–1134
2019
Earlier work this paper cites.
Y. Kubo and T. Trappenberg, Mitigating Overfitting Using Regularization to Defend Networks Against Adversarial Examples , 04 2019, pp. 400–405
2019
Earlier work this paper cites.
X. Qiu, T. Sun, Y. Xu, Y. Shao, N. Dai, and X. Huang, “Pre-trained models for natural language processing: A survey,” Science China Technological Sciences , vol. 63, no. 10, pp. 1872–1897, 2020
2020
Earlier work this paper cites.
M. Zaib, Q. Z. Sheng, and W. Emma Zhang, “A short survey of pre-trained language models for conversational ai-a new age in nlp,” in Proceedings of the Australasian computer science week multiconference , 2020, pp. 1–4
2020
Earlier work this paper cites.
B. Ghojogh and A. Ghodsi, “Attention mechanism, transformers, bert, and gpt: Tutorial and survey,” 2020
2020
Earlier work this paper cites.
A. Alam, “Possibilities and challenges of compounding artificial intelligence in india’s educational landscape,” Alam, A.(2020). Possibilities and Challenges of Compounding Artificial Intelligence in India’s Educational Landscape. International Journal of Advanced Science and Technology , vol. 29, no. 5, pp. 5077–5094, 2020
2020
Earlier work this paper cites.
H. Benbya, T. H. Davenport, and S. Pachidi, “Artificial intelligence in organizations: Current state and future opportunities,” MIS Quarterly Executive , vol. 19, no. 4, 2020
2020
Earlier work this paper cites.
P. Helo and A. Shamsuzzoha, “Real-time supply chain—a blockchain architecture for project deliveries,” Robotics and Computer-Integrated Manufacturing , vol. 63, p. 101909, 2020
2020
Earlier work this paper cites.
B. Feijoo and A. García González, “Online shopping routines among chilean children: level of expansion and main causes,” 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
L. Floridi and M. Chiriatti, “Gpt-3: Its nature, scope, limits, and consequences,” Minds and Machines , vol. 30, pp. 681–694, 2020
2020
Earlier work this paper cites.
J. Freiknecht and W. Effelsberg, “Procedural generation of interactive stories using language models,” in Proceedings of the 15th International Conference on the Foundations of Digital Games , 2020, pp. 1–8
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
K. Hao, “Facebook’s new polyglot ai can translate between 100 languages,” 2020
2020
Earlier work this paper cites.
X. Han, Z. Zhang, N. Ding, Y. Gu, X. Liu, Y. Huo, J. Qiu, Y. Yao, A. Zhang, L. Zhang et al. , “Pre-trained models: Past, present and future,” AI Open , vol. 2, pp. 225–250, 2021
2021
Earlier work this paper cites.
M.-T. Nguyen, P.-T. Nguyen, V.-V. Nguyen, and Q.-M. Nguyen, “Generating product description with generative pre-trained transformer 2,” in 2021 6th International Conference on Innovative Technology in Intelligent System and Industrial Applications (CITISIA) , 2021, pp. 1–7
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
J. R. Stevens, R. Venkatesan, S. Dai, B. Khailany, and A. Raghunathan, “Softermax: Hardware/software co-design of an efficient softmax for transformers,” in 2021 58th ACM/IEEE Design Automation Conference (DAC) . IEEE, 2021, pp. 469–474
2021
Earlier work this paper cites.
S. Tu, A. Cyphert, and S. Perl, “Limits of using artificial intelligence and gpt-3 in patent prosecution,” Tex. Tech L. Rev. , vol. 54, p. 255, 2021
2021
Earlier work this paper cites.
R. Reed, “The theology of gpt-2: Religion and artificial intelligence,” Religion Compass , vol. 15, no. 11, p. e12422, 2021
2021
Earlier work this paper cites.
C. Benzaïd, T. Taleb, and M. Z. Farooqi, “Trust in 5g and beyond networks,” IEEE Network , vol. 35, no. 3, pp. 212–222, 2021
2021
Earlier work this paper cites.
Z. Liu, R. A. Roberts, M. Lal-Nag, X. Chen, R. Huang, and W. Tong, “Ai-based language models powering drug discovery and development,” Drug Discovery Today , vol. 26, no. 11, pp. 2593–2607, 2021
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
R. S. Rathore, S. Sangwan, and O. Kaiwartya, “Towards trusted green computing for wireless sensor networks: Multi metric optimization approach.” Adhoc & Sensor Wireless Networks , vol. 49, 2021
2021
Earlier work this paper cites.
K. Goei, M. Hendriksen, M. de Rijke et al. , “Tackling attribute fine-grainedness in cross-modal fashion search with multi-level features,” in SIGIR 2021 Workshop on eCommerce. ACM , 2021
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
J. Cowls, A. Tsamados, M. Taddeo, and L. Floridi, “The ai gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations,” Ai & Society , pp. 1–25, 2021
2021
Earlier work this paper cites.
H. Benbya, S. Pachidi, and S. Jarvenpaa, “Special issue editorial: Artificial intelligence in organizations: Implications for information systems research,” Journal of the Association for Information Systems , vol. 22, no. 2, p. 10, 2021
2021
Earlier work this paper cites.
T. Zheng, M. Ardolino, A. Bacchetti, and M. Perona, “The applications of industry 4.0 technologies in manufacturing context: a systematic literature review,” International Journal of Production Research , vol. 59, no. 6, pp. 1922–1954, 2021
2021
Earlier work this paper cites.
N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, and A. Galstyan, “A survey on bias and fairness in machine learning,” ACM Computing Surveys (CSUR) , vol. 54, no. 6, pp. 1–35, 2021
2021
Earlier work this paper cites.
F. T. Tschang and E. Almirall, “Artificial intelligence as augmenting automation: Implications for employment,” Academy of Management Perspectives , vol. 35, no. 4, pp. 642–659, 2021
2021
Earlier work this paper cites.
V. Jain, B. Malviya, and S. Arya, “An overview of electronic commerce (e-commerce),” Journal of Contemporary Issues in Business and Government— Vol , vol. 27, no. 3, p. 666, 2021
2021
Earlier work this paper cites.
X. Zhang, Y. Jiang, Y. Shang, Z. Cheng, C. Zhang, X. Fan, Y. Xiao, and B. Long, “Dsgpt: Domain-specific generative pre-training of transformers for text generation in e-commerce title and review summarization,” in Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval , 2021, pp. 2146–2150
2021
Earlier work this paper cites.
N. Dehouche, “Plagiarism in the age of massive generative pre-trained transformers (gpt-3),” Ethics in Science and Environmental Politics , vol. 21, pp. 17–23, 2021
2021
Earlier work this paper cites.
S. Toshniwal, S. Wiseman, K. Livescu, and K. Gimpel, “Learning chess blindfolded,” 2021
2021
Earlier work this paper cites.
J. van Stegeren and J. Myundefinedliwiec, “Fine-tuning gpt-2 on annotated rpg quests for npc dialogue generation,” in Proceedings of the 16th International Conference on the Foundations of Digital Games , ser. FDG ’21. New York, NY, USA: Association for Computing Machinery, 2021. [Online]. Available: https://doi.org/10.1145/3472538.3472595
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
A. H. Sweidan, N. El-Bendary, and H. Al-Feel, “Sentence-level aspect-based sentiment analysis for classifying adverse drug reactions (adrs) using hybrid ontology-xlnet transfer learning,” IEEE Access , vol. 9, pp. 90 828–90 846, 2021
2021
Earlier work this paper cites.
M. M. van Buchem, H. Boosman, M. P. Bauer, I. M. Kant, S. A. Cammel, and E. W. Steyerberg, “The digital scribe in clinical practice: a scoping review and research agenda,” NPJ digital medicine , vol. 4, no. 1, p. 57, 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
M. Zhang and J. Li, “A commentary of gpt-3 in mit technology review 2021,” Fundamental Research , vol. 1, no. 6, pp. 831–833, 2021
2021
Cited alongside, same era.
H. R. Kirk, Y. Jun, F. Volpin, H. Iqbal, E. Benussi, F. Dreyer, A. Shtedritski, and Y. Asano, “Bias out-of-the-box: An empirical analysis of intersectional occupational biases in popular generative language models,” Advances in neural information processing systems , vol. 34, pp. 2611–2624, 2021
2021
Cited alongside, same era.
2023
Closest in time.
D. M. Katz, M. J. Bommarito, S. Gao, and P. Arredondo, “Gpt-4 passes the bar exam,” Available at SSRN 4389233 , 2023
2023
Closest in time.
D. O. Beerbaum, “Generative artificial intelligence (gai) ethics taxonomy-applying chat gpt for robotic process automation (gai-rpa) as business case,” Available at SSRN 4385025 , 2023
2023
Closest in time.
H. A. Dida, D. Chakravarthy, and F. Rabbi, “Chatgpt and big data: Enhancing text-to-speech conversion,” Mesopotamian Journal of Big Data , vol. 2023, pp. 33–37, 2023
2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Q. Zhu and J. Luo, “Generative pre-trained transformer for design concept generation: an exploration,” Proceedings of the Design Society , vol. 2, pp. 1825–1834, 2022
2022
Cited alongside, same era.
M. Bommarito II and D. M. Katz, “Gpt takes the bar exam,” arXiv preprint arXiv:2212.14402 , 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
K. YAMAOKA, K. WATANABE, K. KISE, A. DENGEL, and S. ISHIMARU, “Experience is the best teacher: Personalized vocabulary building within the context of instagram posts and sentences from gpt-3,” 2022
2022
Cited alongside, same era.
V. Pereira, E. Hadjielias, M. Christofi, and D. Vrontis, “A systematic literature review on the impact of artificial intelligence on workplace outcomes: A multi-process perspective,” Human Resource Management Review , vol. 33, no. 1, p. 100857, 2023
2023
Closest in time.
A. Mathew, “Is artificial intelligence a world changer? a case study of openai’s chat gpt,” Recent Progress in Science and Technology Vol. 5 , pp. 35–42, 2023
2023
Closest in time.
H. Akbar, M. Zubair, and M. S. Malik, “The security issues and challenges in cloud computing,” International Journal for Electronic Crime Investigation , vol. 7, no. 1, pp. 13–32, 2023
2023
Closest in time.
K. D. Gupta et al. , “A review of generative ai from historical perspectives,” 2023
2023
Closest in time.
2023
Closest in time.
H. Hua, Y. Li, T. Wang, N. Dong, W. Li, and J. Cao, “Edge computing with artificial intelligence: A machine learning perspective,” ACM Computing Surveys , vol. 55, no. 9, pp. 1–35, 2023
2023
Closest in time.
K. Li, K. Chen, S. Luo, H. Zhang, and P. Fan, “Ubinn: A communication efficient framework for distributed machine learning in edge computing,” IEEE Transactions on Network Science and Engineering , 2023
2023
Closest in time.
S. Zhang, W. Y. B. Lim, W. C. Ng, Z. Xiong, D. Niyato, X. S. Shen, and C. Miao, “Towards green metaverse networking: Technologies, advancements and future directions,” IEEE Network , 2023
2023
Closest in time.
Y. Rogers, H. Sharp, and J. Preece, Interaction design: beyond human-computer interaction . John Wiley & Sons, 2023
2023
Closest in time.
Y. Liu, M. Yu, M. Jiang, and Y. Huang, “Creative research question generation for human-computer interaction research,” 2023
2023
Closest in time.
P. Hämäläinen, M. Tavast, and A. Kunnari, “Evaluating large language models in generating synthetic hci research data: a case study,” in ACM SIGCHI Annual Conference on Human Factors in Computing Systems . ACM, 2023
2023
Closest in time.
A. Shafeeg, I. Shazhaev, D. Mihaylov, A. Tularov, and I. Shazhaev, “Voice assistant integrated with chat gpt,” Indonesian Journal of Computer Science , vol. 12, no. 1, 2023
2023
Closest in time.
J. Zhang, J. Pu, J. Xue, M. Yang, X. Xu, X. Wang, and F.-Y. Wang, “Hivegpt: Human-machine-augmented intelligent vehicles with generative pre-trained transformer,” IEEE Transactions on Intelligent Vehicles , 2023
2023
Closest in time.
2023
Closest in time.
A. Lecler, L. Duron, and P. Soyer, “Revolutionizing radiology with gpt-based models: Current applications, future possibilities and limitations of chatgpt,” Diagnostic and Interventional Imaging , 2023
2023
Closest in time.
D. Baidoo-Anu and L. Owusu Ansah, “Education in the era of generative artificial intelligence (ai): Understanding the potential benefits of chatgpt in promoting teaching and learning,” Available at SSRN 4337484 , 2023
2023
Closest in time.
A. O’Cain, B. D. Fedoruk, Z. Masri, R. Frost, and A. Alahmar, “A system for the improvement of educational assessment using intelligent conversational agents,” Available at SSRN 4393234 , 2023
2023
Closest in time.
M. Ahsan, M. Rahaman, N. Anjum et al. , “From chatgpt-3 to gpt-4: A significant leap in ai-driven nlp tools,” Saidur and Anjum, Nishath, From ChatGPT-3 to GPT-4: A Significant Leap in AI-Driven NLP Tools (March 27, 2023) , 2023
2023
Closest in time.
D. M. Levine, R. Tuwani, B. Kompa, A. Varma, S. G. Finlayson, A. Mehrotra, and A. Beam, “The diagnostic and triage accuracy of the gpt-3 artificial intelligence model,” medRxiv , pp. 2023–01, 2023
2023
Closest in time.
H. Ali, “The potential of gpt-4 as a personalized virtual assistant for bariatric surgery patients,” Obesity Surgery , pp. 1–1, 2023
2023
Closest in time.
S. Arslan, “Exploring the potential of chat gpt in personalized obesity treatment,” Annals of Biomedical Engineering , pp. 1–2, 2023
2023
Closest in time.
I. Carvalho and S. Ivanov, “Chatgpt for tourism: applications, benefits and risks,” Tourism Review , 2023
2023
Closest in time.
S. Shah, H. Ghomeshi, E. Vakaj, E. Cooper, and R. Mohammad, “An ensemble-learning-based technique for bimodal sentiment analysis,” Big Data and Cognitive Computing , vol. 7, no. 2, p. 85, 2023
2023
Closest in time.
A. S. George and A. H. George, “A review of chatgpt ai’s impact on several business sectors,” Partners Universal International Innovation Journal , vol. 1, no. 1, pp. 9–23, 2023
2023
Closest in time.
A. El-Ansari and A. Beni-Hssane, “Sentiment analysis for personalized chatbots in e-commerce applications,” Wireless Personal Communications , vol. 129, no. 3, pp. 1623–1644, 2023
2023
Closest in time.
S. G. Bouschery, V. Blazevic, and F. T. Piller, “Augmenting human innovation teams with artificial intelligence: Exploring transformer-based language models,” Journal of Product Innovation Management , vol. 40, no. 2, pp. 139–153, 2023
2023
Closest in time.
2023
Closest in time.
S. Biswas, “Importance of chat gpt in agriculture: According to chat gpt,” Available at SSRN 4405391 , 2023
2023
Closest in time.
Y. K. Dwivedi, N. Kshetri, L. Hughes, E. L. Slade, A. Jeyaraj, A. K. Kar, A. M. Baabdullah, A. Koohang, V. Raghavan, M. Ahuja et al. , ““so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy,” International Journal of Information Management , vol. 71, p. 102642, 2023
2023
Closest in time.
S. Biswas, “Prospective role of chat gpt in the military: According to chatgpt,” Qeios , 2023
2023
Closest in time.
B. Rathore, “Digital transformation 4.0: Integration of artificial intelligence & metaverse in marketing,” Eduzone: International Peer Reviewed/Refereed Multidisciplinary Journal , vol. 12, no. 1, pp. 42–48, 2023
2023
Closest in time.
N. Gillani, R. Eynon, C. Chiabaut, and K. Finkel, “Unpacking the “black box” of ai in education,” Educational Technology & Society , vol. 26, no. 1, pp. 99–111, 2023
2023
Closest in time.
2023
Closest in time.
P. Maddigan and T. Susnjak, “Chat2vis: Generating data visualisations via natural language using chatgpt, codex and gpt-3 large language models,” IEEE Access , 2023
2023
Closest in time.
N. Brand, W. Odom, and S. Barnett, “Envisioning and understanding orientations to introspective ai: Exploring a design space with meta. aware,” in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems , 2023, pp. 1–18
2023
Closest in time.
B. D. Lund, T. Wang, N. R. Mannuru, B. Nie, S. Shimray, and Z. Wang, “Chatgpt and a new academic reality: Artificial intelligence-written research papers and the ethics of the large language models in scholarly publishing,” Journal of the Association for Information Science and Technology , 2023
2023
Closest in time.
M. Javaid, A. Haleem, and R. P. Singh, “Chatgpt for healthcare services: An emerging stage for an innovative perspective,” BenchCouncil Transactions on Benchmarks, Standards and Evaluations , p. 100105, 2023
2023
Closest in time.
P. P. Ray, “Chatgpt: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope,” Internet of Things and Cyber-Physical Systems , 2023
2023
Closest in time.
H. H. Thorp, “Chatgpt is fun, but not an author,” pp. 313–313, 2023
2023
Closest in time.
2023
Closest in time.
opchatsgpt, “Impact of chat gpt on the entertainment industry,” Mar 2023. [Online]. Available: https://opchatsgpt.com/chat-gpt-in-entertainment/
2023
Closest in time.
“The Power of Chat GPT: Creating Personalized Marketing and Efficient Customer Support. — linkedin.com,” https://www.linkedin.com/pulse/power-chat-gpt-creating-personalized-marketing-customer-bundhoo/
2023
Closest in time.
“Chat GPT-4 in the Film Industry: Scriptwriting, Editing, and More; TS2 SPACE — ts2.space,” https://ts2.space/en/chat-gpt-4-in-the-film-industry-scriptwriting-editing-and-more/#:~:text=GPT\%2D4\%20has\%20the\%20potential,and\%20consistency\%20than\%20ever\%20before
2023
Closest in time.
E. i. bd, “Chatgpt: The impact of chat gpt on the entertainment industry - march 24, 2023 educationsinbd,” Mar 2023. [Online]. Available: https://educationsinbd.com/the-impact-of-chat-gpt-on-the-entertainment-industry/
2023
Closest in time.
Y. H. Yeo, J. S. Samaan, W. H. Ng, P.-S. Ting, H. Trivedi, A. Vipani, W. Ayoub, J. D. Yang, O. Liran, B. Spiegel et al. , “Assessing the performance of chatgpt in answering questions regarding cirrhosis and hepatocellular carcinoma,” medRxiv , pp. 2023–02, 2023
2023
Closest in time.
S. S. Biswas, “Role of chat gpt in public health,” Annals of Biomedical Engineering , pp. 1–2, 2023
2023
Closest in time.
M. Patkar, “5 Free Travel Planning AI and ChatGPT Apps to Get an Instant Itinerary — makeuseof.com,” https://www.makeuseof.com/free-travel-planning-ai-chatgpt-apps/
2023
Closest in time.
lechjaLearnCrafts, “Learn Crafts & Hobbies w/ GPT Chat — lechja.com,” https://www.lechja.com/ai/learn-crafts-hobbies-w-gpt-chat
2023
Closest in time.
T. Yue, D. Au, C. C. Au, and K. Y. Iu, “Democratizing financial knowledge with chatgpt by openai: Unleashing the power of technology,” Available at SSRN 4346152 , 2023
2023
Closest in time.
”Siri”. [Accessed on 25.03.2023]. [Online]. Available: https://www.apple.com/in/siri/
2023
Closest in time.
”Siri ChatGPT”. [Accessed on 25.03.2023]. [Online]. Available: https://support.apple.com/en-in/guide/shortcuts/apd07c25bb38/ios
2023
Closest in time.
”Siri ChatGPT”. [Accessed on 25.03.2023]. [Online]. Available: https://gpt3demo.com/apps/chatgpt-sirigpt-apple-ios
2023
Closest in time.
”AI Dungeon: A text-based adventure-story game you direct (and star in) while the AI brings it to life.”. [Accessed on 30.03.2023]. [Online]. Available: https://aidungeon.io/
2023
Closest in time.
”It Began as an AI-Fueled Dungeon Game. It Got Much Darker”. [Accessed on 25.03.2023]. [Online]. Available: https://www.wired.com/story/ai-fueled-dungeon-game-got-much-darker/
2023
Closest in time.
”Whatever you want to ask, our chat has the answers”. [Accessed on 25.03.2023]. [Online]. Available: https://www.copy.ai/
2023
Closest in time.
”Introducing, The BOND Network.”. [Accessed on 25.03.2023]. [Online]. Available: https://www.bond.ai/
2023
Closest in time.
”Save hundreds of hours analyzing feedback.”. [Accessed on 29.03.2023]. [Online]. Available: https://www.askviable.com/
2023
Closest in time.
”ai—channels: make contact with intelligent minds”. [Accessed on 30.03.2023]. [Online]. Available: https://aichannels.app/
2023
Closest in time.
”Automate your meeting notes”. [Accessed on 30.03.2023]. [Online]. Available: https://fireflies.ai/
2023
Closest in time.
Y. Gan, G. Lu, Z. Su, L. Wang, J. Zhou, J. Jiang, and D. Chen, “A joint domain-specific pre-training method based on data enhancement,” Applied Sciences , vol. 13, no. 7, p. 4115, 2023
2023
Closest in time.
“Chatgpt plugins,” Mar 2023. [Online]. Available: https://openai.com/blog/chatgpt-plugins
2023
Closest in time.
S. S. Biswas, “Potential use of chat gpt in global warming,” Annals of biomedical engineering , pp. 1–2, 2023
2023
Closest in time.
W. Ji, Y. Wei, Z. Zheng, H. Fei, and T.-s. Chua, “Deep multimodal learning for information retrieval,” in ACM International Conference on Multimedia , 2023
2023
Closest in time.
M. Verma, “Integration of ai-based chatbot(chatgpt) and supply chain management solution to enhance tracking and queries response,” 02 2023
2023
Closest in time.