Volume- 12
Issue- 4
Year- 2024
DOI: 10.55524/ijircst.2024.12.4.2 | DOI URL: https://doi.org/10.55524/ijircst.2024.12.4.2 Crossref
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)
Article Tools: Print the Abstract | Indexing metadata | How to cite item | Email this article | Post a Comment
Manali Shukla , Ishika Goyal, Bhavya Gupta, Jhanvi Sharma
Generative AI is making buzz all over the globe and has mostly drawn attention due to it's ability to generate variety of content that mimics human behaviour and intelligence along with the ease of access. It comprises of the ability to generate text, images, video, and even audio that are almost unrecognizable from human-created content. Thus there is a huge scope of research in this field due to its vast applicability and motivates this research work. This research work presents comparatively analysis of the three Generative Artificial Intelligence (AI) tool, namely ChatGPT, Gemini, Perplexity AI, based on the content generation, ownership and developing technology, context understanding, transparency, and information retrieval.
1) E. Kasneci et al., "ChatGPT for good? On opportunities and challenges of large language models for education," Learning and Individual Differences, vol. 103, p. 102274, 2023. Available from: https://doi.org/10.1016/j.lindif.2023.102274
2) C. K. Lo, "What is the impact of ChatGPT on education? A rapid review of the literature," Education Sciences, vol. 13, no. 4, p. 410, 2023. Available from: https://doi.org/10.3390/educsci13040410
3) E. Opara, A. M.-E. Theresa, and T. C. Aduke, "ChatGPT for teaching, learning and research: Prospects and challenges," Glob Acad J Humanit Soc Sci, vol. 5, 2023. Available from: https://doi.org/10.36348/gajhss.2023.v05i02.001
4) S. S. Gill and R. Kaur, "ChatGPT: Vision and challenges," Internet Things Cyber-Physical Syst., vol. 3, pp. 262–271, 2023, Available from: https://doi.org/10.1016/j.iotcps.2023.05.004
5) A. Vaswani et al., "Attention is All you Need," in Advances in Neural Information Processing Systems, vol. 30, 2017. Available from: https://dl.acm.org/doi/10.5555/3295222.3295349
6) V. Taecharungroj, "‘What Can ChatGPT Do?’ Analyzing Early Reactions to the Innovative AI Chatbot on Twitter," Big Data Cogn. Comput., vol. 7, no. 1, p. 35, Feb. 2023,. Available from: https://doi.org/10.3390/bdcc7010035
7) S. A. Thorat and V. Jadhav, "A Review on Implementation Issues of Rule-based Chatbot Systems," SSRN Electron. J., 2020, Available from: https://doi.org/10.2139/ssrn.3567047
8) J. E. Casal and M. Kessler, "Can linguists distinguish between ChatGPT/AI and human writing?: a study of research ethics and academic publishing," Res. Methods Appl. Linguist., vol. 2, no. 3, 2023. Available from: https://doi.org/10.1016/j.rmal.2023.100068
9) J. Zhao, X. Han, M. Ouyang, and A. F. Burke, "Specialized deep neural networks for battery health prognostics: Opportunities and challenges," J. Energy Chem., vol. 87, pp. 416–438, Dec. 2023, Available from: https://doi.org/10.1016/j.jechem.2023.08.047
10) A. Vaswani et al., "Attention is All you Need," in Advances in Neural Information Processing Systems, vol. 30, 2017. Available from: https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
11) C. Fu et al., "A challenger to gpt-4v? early explorations of gemini in visual expertise," arXiv preprint arXiv:2312.12436, 2023. Available from: https://doi.org/10.48550/arXiv.2312.12436
12) Y. Wang and Y. Zhao, "Gemini in reasoning: Unveiling commonsense in multimodal large language models," arXiv preprint arXiv:2312.17661, 2023. Available from: https://doi.org/10.48550/arXiv.2312.17661
13) A. C. Kavak, "ChatGPT, Google Bard, Microsoft Bing, Claude, and Perplexity: Which is the Right AI Tool?," Zeo.org, 2023. Available from: https://zeo.org/resources/blog/chatgpt-google-bard-microsoft-bing-claude-and-perplexity-which-is-the-right-ai-to
14) F. Somoye, "What is Perplexity AI and what are its uses?," PC Guide, 2023. Available from: https://zapier.com/blog/perplexity-ai/
15) M. Spencer, "What is Perplexity AI?," Machine Economy Press, 2022. Available from: https://zapier.com/blog/perplexity-ai/
16) A. Creswell, T. White, V. Dumoulin, K. Arulkumaran, B. Sengupta, and A. A. Bharath, "Generative adversarial networks: An overview," IEEE Signal Processing Magazine, vol. 35, no. 1, pp. 53-65, 2018. Available from: https://doi.org/10.48550/arXiv.1710.07035
17) I. J. Goodfellow et al., "Generative adversarial nets," in Neural Information Processing Systems, 2014, pp. 2672–2680. Available from: https://doi.org/10.48550/arXiv.1406.2661
18) W. Cao, X. Wang, Z. Ming, and J. Gao, "A Review on Neural Networks with Random Weights," Neurocomputing, vol. 275, pp. 278-287, 2017. Available from: https://doi.org/10.1016/j.neucom.2017.08.040
19) L. Zhang and P. N. Suganthan, "A Survey of Randomized Algorithms for Training Neural Networks," Information Sciences, vol. 364, pp. 146-155, 2016. Available from: https://doi.org/10.1016/j.ins.2016.01.039
20) S. Wang, T. Z. Huang, J. Liu, and X. G. Lv, "An alternating iterative algorithm for image deblurring and denoising problems," Communications in Nonlinear Science and Numerical Simulation, vol. 19, no. 3, pp. 617-626, 2014. Available from: https://doi.org/10.1016/j.cnsns.2013.07.004
21) I. J. Goodfellow et al., "Generative Adversarial Nets," in Proc. of International Conference on Neural Information Processing Systems, 2014. Available from: https://doi.org/10.48550/arXiv.1406.2661
22) D. E. Rumelhart, "Learning Representations by Back-Propagating Errors," Nature, vol. 323, pp. 533-536, 1986. Available from: https://www.iro.umontreal.ca/~vincentp/ift3395/lectures/backprop_old.pdf
23) D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114, 2013. Available from: https://doi.org/10.48550/arXiv.1312.6114
24) H. Li and S. Misra, "Prediction of Subsurface NMR T2 Distributions in a Shale Petroleum System Using Variational Autoencoder-Based Neural Networks," IEEE Geoscience & Remote Sensing Letters, vol. 14, no. 12, pp. 23995-2397, 2017. Available from: https://doi.org/10.1109/LGRS.2017.2766130
25) M. Lopez-Martin, B. Carro, A. Sanchez-Esguevillas, and J. Lioret, "Conditional variational autoencoder for prediction and feature recovery applied to intrusion detection in IoT," Sensors, vol. 17, no. 9, 2017. Available from: https://doi.org/10.3390/s17091967
26) D. M. Blei, A. Kucukelbir, and J. D. McAuliffe, "Variational inference: A review for statisticians," Journal of the American Statistical Association, vol. 112, no. 518, pp. 859-877, 2017. Available from: https://doi.org/10.48550/arXiv.1601.00670
27) K. Zhu, "Bootstrapping the portmanteau tests in weak auto-regressive moving average models," Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 78, no. 2, pp. 463-485, 2016. Available from: https://www.jstor.org/stable/24775347
28) H. Akaike, "Fitting autoregressive models for prediction," Annals of the Institute of Statistical Mathematics, vol. 21, no. 1, pp. 243-247, 1969. Available from: https://www.ism.ac.jp/editsec/aism/pdf/021_2_0243.pdf
29) A. Oord, N. Kalchbrenner, and K. Kavukcuoglu, "Pixel recurrent neural networks," in Proc. of the 33rd International Conference on International Conference on Machine Learning, vol. 48, pp. 1747-1756, 2016. Available from: https://doi.org/10.48550/arXiv.1601.06759
30) L. Mou, P. Ghamisi, and X. Zhu, "Deep Recurrent Neural Networks for Hyperspectral Image Classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 7, pp. 3639-3655, July 2017. Available from: https://elib.dlr.de/114208/1/07914752.pdf
31) K. Gregor et al., "DRAW: A Recurrent Neural Network for Image Generation," Computer Science, vol. 37, pp. 1462-1471, 2015. Available from: https://doi.org/10.48550/arXiv.1502.04623
32) A. Oord et al., "Conditional image generation with pixelcnn decoders," Advances in neural information processing systems, pp. 4797-4805, 2016. Available from: https://doi.org/10.48550/arXiv.1606.05328
33) T. Salimans, A. Karpathy, X. Chen, and D. P. Kingma, "Pixelcnn++: Improving the pixelcnn with discretized logistic mixture likelihood and other modifications," arXiv preprint arXiv: 1701.05517, 2017. Available from: https://openreview.net/pdf?id=BJrFC6ceg
34) C. Nash et al., "PolyGen: An autoregressive generative model of 3D meshes," arXiv preprint arXiv:2002.10880, Feb. 2020. Available from: https://proceedings.mlr.press/v119/nash20a/nash20a.pdf
35) Z. Bahroun et al., "Transforming education: A comprehensive review of generative artificial intelligence in educational settings through bibliometric and content analysis," Sustainability, vol. 15, no. 17, p. 12983, 2023. Available from: https://doi.org/10.3390/su151712983
36) V. Ratten and P. Jones, "Generative artificial intelligence (ChatGPT): Implications for management educators," The International Journal of Management Education, vol. 21, no. 3, p. 100857, 2023. Available from: https://doi.org/10.1016/j.ijme.2023.100857
37) T. Sakirin and S. Kusuma, "A Survey of Generative Artificial Intelligence Techniques," Babylonian Journal of Artificial Intelligence, vol. 2023, pp. 10–14, 2023. Available from: https://doi.org/10.48550/arXiv.2306.02781
38) R. T. Hughes, L. Zhu, and T. Bednarz, "Generative adversarial networks–enabled human–artificial intelligence collaborative applications for creative and design industries: A systematic review of current approaches and trends," Frontiers in Artificial Intelligence, vol. 4, p. 604234, 2021. Available from: https://doi.org/10.3389/frai.2021.604234
B.Tech Scholar, Computer Science & Engineering, ITM University, Gwalior, India
No. of Downloads: 70 | No. of Views: 1347
Dipti Prajapati, Samishtarani Sabat, Sanika Bhilare, Rashmi Vishe, Prof. Suman Bhujbal.
March 2024 - Vol 12, Issue 2
Anu Sharma, Vivek Kumar.
May 2023 - Vol 11, Issue 3
Venkateswaran Radhakrishnan.
May 2023 - Vol 11, Issue 3