A Comparative Study of ChatGPT, Gemini, and Perplexity

Manali Shukla; Ishika Goyal; Bhavya Gupta; Jhanvi Sharma

doi:10.55524/ijircst.2024.12.4.2

Abstract

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.

Keywords

Generative Artificial Intelligence, ChatGPT, Gemini, Perplexity AI.

References

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

Cites this article as

M. Shukla, I. Goyal, B. Gupta, J. Sharma, "A Comparative Study of ChatGPT, Gemini, and Perplexity", International Journal of Innovative Research in Computer Science and Technology (IJIRCST), Vol-12, no.4, pp.10-15, 2024. Available from: https://doi.org/10.55524/ijircst.2024.12.4.2

Corresponding Author

Ishika Goyal

B.Tech Scholar, Computer Science & Engineering, ITM University, Gwalior, India

Download Full Paper

Download PDF

No. of Downloads: 10 | No. of Views: 92

Helmet Detection and Number Plate Recognition Using YOLOv8 and Tensorflow Algorithm in Machine Learning

Dipti Prajapati, Samishtarani Sabat, Sanika Bhilare, Rashmi Vishe, Prof. Suman Bhujbal.

March 2024 - Vol 12, Issue 2
Machine Learning Prospects: Insights for Social Media Data Mining and Analytics

Anu Sharma, Vivek Kumar.

May 2023 - Vol 11, Issue 3
Review Analysis of Cyber Security in Healthcare System: A Systematic Approach of Modern Development

Venkateswaran Radhakrishnan.

May 2023 - Vol 11, Issue 3

IJIRCST

A Comparative Study of ChatGPT, Gemini, and Perplexity

Citations

Download Full Paper PDF

Total View 92

Total Download 10