Authors :
Sirajo Abdullahi Bakura; Abubakar Bello Bada; Musa Tanimu Karatu; Mubashir Haruna; Abdulsalam Ibrahim Magawata; Ede Ifesinachi Chizzy; Shuaibu Yau
Volume/Issue :
Volume 10 - 2025, Issue 9 - September
Google Scholar :
https://tinyurl.com/57m8kv2v
Scribd :
https://tinyurl.com/389zxfff
DOI :
https://doi.org/10.38124/ijisrt/25sep1292
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Note : Google Scholar may take 30 to 40 days to display the article.
Abstract :
Artificial General Intelligence (AGI) is an area of artificial intelligence research that aspires to create
machines that can perform any intellectual task that a human can do. Unlike Artificial Narrow Intelligence (ANI),
which is domain-specific, AGI seeks to replicate human-like adaptability, reasoning, and creativity. This paper
provides a critical overview of the historical development of AGI, evaluates ongoing projects and initiatives, examines
its potential applications across multiple sectors, and discusses associated ethical and governance challenges. The study
identifies technological and societal gaps, highlights risks, and proposes a phased roadmap toward the development
of AGI. The analysis emphasizes the importance of integrating ethical frameworks, interdisciplinary collaboration,
and responsible innovation in shaping the AGI path.
Keywords :
AI, Artificial, Intelligence.
References :
- O. Arshi and A. Chaudhary, “Overview of artificial general intelligence (AGI),” in Advanced Technologies and Societal Change. Singapore: Springer Nature Singapore, 2025, pp. 1–26.
- J. McCarthy, M. L. Minsky, N. Rochester, and C. E. Shannon, “A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955,” AI Magazine, vol. 27, no. 4, p. 12, Dec. 1955. [Online]. Available: https://ojs.aaai.org/aimagazine/index.php/aimagazine/article/view/1904.
- M. Tan and Q. V. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” arXiv [cs.LG], 2019.
- I. D. Mienye, T. G. Swart, and G. Obaido, “Recurrent neural networks: A comprehensive review of architectures, variants, and applications,” Information (Basel), vol. 15, no. 9, p. 517, Aug. 2024.
- M. M. Islam, “Artificial general intelligence: Conceptual framework, recent progress, and future outlook,” Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, vol. 6, no. 1, pp. 1–25, Aug. 2024.
- F. McKenna, B. Aaron, and D. B. Seth, “Survey of artificial general intelligence projects for ethics, risk, and policy.” Global Catastrophic Risk Institute, Technical Report 20-1, 2020.
- S. S. Adams, I. Arel, J. Bach, R. Coop, R. Furlan, B. Goertzel, J. S. Hall, A. Samsonovich, M. Scheutz, M. Schlesinger, S. C. Shapiro, and J. F. Sowa, “Mapping the landscape of human-level artificial general intelligence,” AI Mag., vol. 33, no. 1, pp. 25–41, Mar. 2012.
- R. Kurzweil, The Singularity is Near. London: Palgrave Macmillan UK, 2014, pp. 393–406. [Online]. Available: https://doi.org/10.1057/978113734908826.
- F. L. G. Faroldi, “Risk and artificial general intelligence,” AI Soc., Jul. 2024.
- J. H. Kim, R. Um, J. Liu, J. Patel, E. Curry, S. Mahapatra, A. Ainechi, Y. Tsehay, J. Ehresman, B. Hwang, B. Tyler, F. Aghabaglou, R. Iyer, N. Theodore, and A. Manbachi, “The development of smart hospital assistant: integrating artificial intelligence and a voice-user interface for improved surgical outcomes,” in Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications, B. J. Park and T. M. Deserno, Eds. SPIE, Feb. 2021.
- B. M. Elbagoury, L. Vladareanu, V. Vlad˘ areanu, A. B. Salem, A.-M. ˘ Travediu, and M. I. Roushdy, “A hybrid stacked CNN and residual feedback GMDH-LSTM deep learning model for stroke prediction applied on mobile AI smart hospital platform,” Sensors (Basel), vol. 23, no. 7, p. 3500, Mar. 2023.
- K. A. Shastry and H. A. Sanjay, “Cancer diagnosis using artificial intelligence: a review,” Artif. Intell. Rev., vol. 55, no. 4, pp. 2641–2673, Apr. 2022.
- E. Latif, G. Mai, M. Nyaaba, X. Wu, N. Liu, G. Lu, S. Li, T. Liu, and X. Zhai, “AGI: Artificial general intelligence for education,” 2023.
- OpenAI, “Chatgpt-4,” OpenAI, Large Language Model 4, 2024.
- M. Capilot, “Microsoft 365 copilot,” Microsoft, Large Language Model 365, 2024.
- A. FireFly, “Adobe firefly v25.0.0.2257,” Adobe FireFly, Large Language Model 365, 2024.
- S. Amell. (2025) Top 10 ai companies for manufacturing. [Online]. Available: https://medium.com/@iamamellstephen/top-10-ai-companies-formanufacturing-806430e2148c.
- L. Silva. (2024) How artificial general intelligence will drive an inclusive financial sector in latin america. [Online]. Available: https://www.weforum.org/stories/2024/01/ai-is-driving-the-evolution-ofa-more-inclusive-financial-sector-in-latin-america-here-is-how/.
- T. D. Wankhade, S. W. Ingale, P. M. Mohite, and N. J. Bankar, “Artificial intelligence in forensic medicine and toxicology: The future of forensic medicine,” Cureus, vol. 14, no. 8, p. e28376, Aug. 2022.
- L. Schwartz-croft, “Effects of ROSS intelligence and NDAS, highlighting the need for AI regulation,” SSRN Electron. J., 2024.
- A. Kamilaris and F. X. Prenafeta-Boldu, “Deep learning in agriculture: A ´ survey,” Computers and Electronics in Agriculture, vol. 147, p. 70–90, Apr. 2018. [Online]. Available: http://dx.doi.org/10.1016/j.compag.2018.02.016.
- M. M. Islam, “Artificial general intelligence: Conceptual framework, recent progress, and future outlook,” Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, vol. 6, no. 1, p. 1–25, Aug. 2024. [Online]. Available: http://dx.doi.org/10.60087/jaigs.v6i1.212.
- K. Liakos, P. Busato, D. Moshou, S. Pearson, and D. Bochtis, “Machine learning in agriculture: A review,” Sensors, vol. 18, no. 8, p. 2674, Aug. 2018. [Online]. Available: http://dx.doi.org/10.3390/s18082674.
- R. Patel and P. Singh, “Climate-smart agriculture and artificial intelligence: Opportunities and challenges,” Journal of Agricultural Informatics, vol. 1, no. 16, p. 15, 2025.
- S. Neethirajan, “Correction: Neethirajan, s. affective state recognition in livestock—artificial intelligence approaches. animals 2022, 12, 759,” Animals, vol. 12, no. 14, p. 1856, Jul. 2022. [Online]. Available: http://dx.doi.org/10.3390/ani12141856.
- D. Rolnick, P. L. Donti, L. H. Kaack, K. Kochanski, A. Lacoste, K. Sankaran, A. S. Ross, N. Milojevic-Dupont, N. Jaques, A. Waldman-Brown, A. S. Luccioni, T. Maharaj, E. D. Sherwin, S. K. Mukkavilli, K. P. Kording, C. P. Gomes, A. Y. Ng, D. Hassabis, J. C. Platt, F. Creutzig, J. Chayes, and Y. Bengio, “Tackling climate change with machine learning,” ACM Computing Surveys, vol. 55, no. 2, p. 1–96, Feb. 2022. [Online]. Available: http://dx.doi.org/10.1145/348512.
Artificial General Intelligence (AGI) is an area of artificial intelligence research that aspires to create
machines that can perform any intellectual task that a human can do. Unlike Artificial Narrow Intelligence (ANI),
which is domain-specific, AGI seeks to replicate human-like adaptability, reasoning, and creativity. This paper
provides a critical overview of the historical development of AGI, evaluates ongoing projects and initiatives, examines
its potential applications across multiple sectors, and discusses associated ethical and governance challenges. The study
identifies technological and societal gaps, highlights risks, and proposes a phased roadmap toward the development
of AGI. The analysis emphasizes the importance of integrating ethical frameworks, interdisciplinary collaboration,
and responsible innovation in shaping the AGI path.
Keywords :
AI, Artificial, Intelligence.