The unfolding dialectic: Human and artificial intelligence, broad challenges, and prospects for tomorrow
Abstract
The relentless progress of Artificial Intelligence (AI) has sparked a profound and enduring debate: which form of intelligence, human or artificial, is superior? This paper navigates this complex question, not by seeking a definitive victor, but by undertaking a comparative analysis of the distinct characteristics of human and AI. It explores the foundational cognitive architectures that underpin both, delves into the enigmatic nature of consciousness, and examines the formidable open challenges confronting the pursuit of Artificial General Intelligence (AGI). Ultimately, this paper argues that the future lies not in a contest of supremacy, but in the synergistic potential of human-AI collaboration, a prospect that promises to redefine the boundaries of knowledge and innovation.
Keywords:
Human, Artificial intelligence, Artificial general intelligence, ConsciousnessReferences
- [1] Cohen, Y., Engel, T. A., Langdon, C., Lindsay, G. W., Ott, T., Peters, M. A. K., …& Ramaswamy, S. (2022). Recent advances at the interface of neuroscience and artificial neural networks. Journal of neuroscience, 42(45), 8514–8523. https://doi.org/10.1523/JNEUROSCI.1503-22.2022
- [2] Schrittwieser, J., Antonoglou, I., Hubert, T., Simonyan, K., Sifre, L., Schmitt, S., … & Silver, D. (2020). Mastering atari, go, chess and shogi by planning with a learned model. Nature, 588(7839), 604–609. https://doi.org/10.1038/s41586-020-03051-4
- [3] Tian, S., Li, W., Ning, X., Ran, H., Qin, H., & Tiwari, P. (2023). Continuous transfer of neural network representational similarity for incremental learning. Neurocomputing, 545, 126300. https://doi.org/10.1016/j.neucom.2023.126300
- [4] A Mageed, I., & Nazir, A. R. (2025). AI-generated abstract expressionism inspiring creativity through Ismail A Mageed’s internal monologues in poetic form. https://doi.org/10.20944/preprints202501.0425.v1
- [5] A Mageed, I. (2024). Entropic artificial intelligence and knowledge transfer. Advances in machine learning & artificial intelligence, 5(2), 1–8. https://B2n.ir/td6327
- [6] A Mageed, I. (2024). Ismail’s threshold theory to master perplexity AI. Management analytics and social insights, 1(2), 223–234. https://doi.org/10.22105/kfyyze86
- [7] A Mageed, I. (2024). On the Rényi entropy functional, Tsallis distributions and Lévy stable distributions with entropic applications to machine learning. Soft computing fusion with applications, 1(2), 90–101. https://doi.org/10.22105/scfa.v1i2.33
- [8] A Mageed, I. (2024). Information data length theory of human emotions, how, what and why. https://doi.org/10.20944/preprints202403.0557.v1
- [9] A Mageed, I. (2025). Surpassing beyond boundaries: Open mathematical challenges in AI-driven robot control. https://doi.org/10.20944/preprints202505.2456.v1
- [10] A Mageed, I. (2025). The hidden mathematics to treat cancer, innovative mathematics to unlock life mysteries. Computational algorithms and numerical dimensions, 4(2), 106–144. https://doi.org/10.22105/cand.2025.512116.1195
- [11] A Mageed, I., Bhat, A. H., & Alja’am, J. (2024). Shallow learning vs. deep learning in social applications. In Shallow learning vs. deep learning: A practical guide for machine learning solutions (pp. 93–114). Springer. https://doi.org/10.1007/978-3-031-69499-8_4
- [12] A Mageed, I., Bhat, A. H., & Edalatpanah, S. A. (2024). Shallow learning vs. deep learning in finance, marketing, and e-commerce. In Shallow learning vs. deep learning: A practical guide for machine learning solutions (pp. 77–91). Springer. https://doi.org/10.1007/978-3-031-69499-8_3
- [13] A Mageed, I., Bhat, A. H., & Rehman, H. U. (2024). Shallow learning vs. deep learning in anomaly detection applications. In Shallow learning vs. deep learning: A practical guide for machine learning solutions (pp. 157–177). Springer. https://doi.org/10.1007/978-3-031-69499-8_7
- [14] Zhang, A., Lipton, Z. C., Li, M., & Smola, A. J. (2023). Dive into deep learning. Cambridge University Press. https://B2n.ir/dh2218
- [15] Thompson, N. C., Greenewald, K., Lee, K., Manso, G. F. (2020). The computational limits of deep learning. https://doi.org/10.21428/bf6fb269.1f033948
- [16] Del Pin, S. H., Skóra, Z., Sandberg, K., Overgaard, M., & Wierzchoń, M. (2021). Comparing theories of consciousness: Why it matters and how to do it. Neuroscience of consciousness, 2021(2), 1–8. https://doi.org/10.1093/nc/niab019
- [17] Seth, A. K. (2024). Conscious artificial intelligence and biological naturalism. https://doi.org/10.1017/S0140525X25000032
- [18] Chang, A. Y. C., Biehl, M., Yu, Y., & Kanai, R. (2020). Information closure theory of consciousness. Frontiers in psychology, 11, 1504. https://doi.org/10.3389/fpsyg.2020.01504
- [19] Feinberg, T. E., & Mallatt, J. M. (2025). Consciousness demystified. MIT Press. https://B2n.ir/zb2656
- [20] Summerfield, C. (2023). Natural general intelligence: How understanding the brain can help us build AI. Oxford university press. https://B2n.ir/wu6761
- [21] Mastrogiorgio, A., & Palumbo, R. (2025). Superintelligence, heuristics and embodied threats. Mind & society, 24, 1–15. https://doi.org/10.1007/s11299-025-00317-0
- [22] Wang, J., Chen, W., Xiao, X., Xu, Y., Li, C., Jia, X., & Meng, M. Q. H. (2021). A survey of the development of biomimetic intelligence and robotics. Biomimetic intelligence and robotics, 1, 100001. https://doi.org/10.1016/j.birob.2021.100001
- [23] Kim, J. Z., & Bassett, D. S. (2023). A neural machine code and programming framework for the reservoir computer. Nature machine intelligence, 5(6), 622–630. https://doi.org/10.1038/s42256-023-00668-8