Toward Human-Level Artificial Intelligence: Technological Foundations and Trajectories
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Artificial General Intelligence,
Cognitive Science,
Human-Level Artificial Intelligence,
Machine Learning
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Ammar Aljawad
Abstract
Background of study: Human-Level Artificial Intelligence (HLAI) represents one of the most formidable dreams in laptop technology, aiming to create structures able to cognition, perception, and reasoning similar to people. Despite splendid development in slender AI programs, the development of Artificial General Intelligence (AGI) remains confined by conceptual, technical, and moral demanding situations.
Aims and scope of paper: This paper pursuits to analyze the theoretical foundations, technological enablers, and ethical dimensions of HLAI. It seeks to distinguish HLAI from slender AI and to make clear the clinical, engineering, and societal problems concerned in constructing structures with human-like intelligence.
Methods: The have a look at adopts a conceptual and literature-based totally method, synthesizing insights from synthetic intelligence, cognitive science, neuroscience, and ethics. Key frameworks and latest research are reviewed to pick out common concepts, technological developments, and unresolved challenges shaping the evolution of HLAI.
Result: The evaluation highlights essential enablers which include device gaining knowledge of, herbal language processing, laptop vision, and robotics as important pathways in the direction of HLAI. Findings screen that at the same time as development in these domain names is good sized, reaching popular intelligence calls for deeper integration of cognitive modeling, neural architectures, and moral alignment mechanisms.
Conclusion: The study shows that collaboration across sectors and designs with principles and safety in mind are important steps to create artificial brains that are robust and reliable. This underlines the need for ethical governance and adaptation of human values to cushion the risk associated with uncontrolled AI development.
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Copyright (c) 2025 Ammar Aljawad
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
Coheur, L. (2020). From Eliza to Siri and beyond. Information Processing and Management of Uncertainty in Knowledge-Based Systems: 18th International Conference, IPMU 2020, Lisbon, Portugal, June 15–19, 2020, Proceedings, Part I 18, 29–41. https://doi.org/10.1007/978-3-030-50146-4_3
Crowley, J. L., Coutaz, J., Grosinger, J., Vazquez-Salceda, J., Angulo, C., Sanfeliu, A., Iocchi, L., & Cohn, A. G. (2023). A Hierarchical Framework for Collaborative Artificial Intelligence. IEEE Pervasive Computing, 22(1), 9–18. https://doi.org/10.1109/MPRV.2022.3208321
Emmanuel, D. R., Raquel, D. H., & Leopoldo, A. R. (2022). Human Gesture Recognition Using Hidden Markov Models and Sensor Fusion. Computer Science, 23(2), 225–243. https://doi.org/10.7494/csci.2022.23.2.3745
Feliu-Talegon, D., & Feliu-Batlle, V. (2022). Control of Very Lightweight 2-DOF Single-Link Flexible Robots Robust to Strain Gauge Sensor Disturbances: A Fractional-Order Approach. IEEE Transactions on Control Systems Technology, 30(1), 14–29. https://doi.org/10.1109/TCST.2021.3053857
Fresca, S., & Manzoni, A. (2022). POD-DL-ROM: Enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition. Computer Methods in Applied Mechanics and Engineering, 388, 114181. https://doi.org/10.1016/j.cma.2021.114181
Gil, Y., & Selman, B. (2019). A 20-year community roadmap for artificial intelligence research in the US. ArXiv Preprint ArXiv:1908.02624. https://doi.org/10.48550/arXiv.1908.02624
Gupta, P., Nguyen, T. N., Gonzalez, C., & Woolley, A. W. (2025). Fostering collective intelligence in human–AI collaboration: laying the groundwork for COHUMAIN. Topics in Cognitive Science, 17(2), 189–216. https://doi.org/10.1111/tops.12679
Harguess, J., & Ward, C. M. (2022). Is the next winter coming for AI? Elements of making secure and robust AI. 2022 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), 1–7. https://doi.org/10.1109/AIPR57179.2022.10092230
Islam, M. M. (2024). Artificial General Intelligence: Conceptual Framework, Recent Progress, and Future Outlook. Journal of Artificial Intelligence General Science (JAIGS) ISSN:3006-4023, 6(1), 1–25. https://doi.org/10.60087/jaigs.v6i1.212
Jadczyk, T. (2018). Audio-visual speech-processing system for Polish applicable to human-computer interaction. Computer Science, 19(1), 41–64. https://doi.org/10.7494/csci.2018.19.1.2398
Kambhampati, S. (2020). Challenges of human-aware AI systems: AAAI presidential address. AI Magazine, 41(3), 3–17. https://doi.org/10.1609/aimag.v41i3.16120
Kuru, K., Clough, S., Ansell, D., McCarthy, J., & McGovern, S. (2023). WILDetect: An intelligent platform to perform airborne wildlife census automatically in the marine ecosystem using an ensemble of learning techniques and computer vision. Expert Systems with Applications, 231, 120574. https://doi.org/https://doi.org/10.1016/j.eswa.2023.120574
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017). Building machines that learn and think like people. Behavioral and Brain Sciences, 40(2017). https://doi.org/10.1017/S0140525X16001837
Nair, A., & Banaei-Kashani, F. (2022). Bridging the gap between artificial intelligence and artificial general intelligence: A ten commandment framework for human-like intelligence. ArXiv Preprint ArXiv:2210.09366, 1. https://doi.org/10.48550/arXiv.2210.09366
O’Hara, I. (2022). Automated epistemology: Bots, computational propaganda & information literacy instruction. The Journal of Academic Librarianship, 48(4), 102540. https://doi.org/https://doi.org/10.1016/j.acalib.2022.102540
Pantsar, M. (2024). Theorem proving in artificial neural networks: new frontiers in mathematical AI. European Journal for Philosophy of Science, 14(1), 4. https://doi.org/https://doi.org/10.1007/s13194-024-00569-6
Qi, L., & Yao, K. (2021). Artificial intelligence enterprise human resource management system based on FPGA high performance computer hardware. Microprocessors and Microsystems, 82(December 2020), 103876. https://doi.org/10.1016/j.micpro.2021.103876
Rajasekar, V., Krishnamoorthi, S., Saračević, M., & A, J. (2022). Ensemble Machine Learning Methods to Predict the Balancing of Ayurvedic Constituents in the Human Body. Computer Science, 23(1), 117–132. https://doi.org/10.7494/csci.2022.23.1.4315
Rawal, A., McCoy, J., Rawat, D. B., Sadler, B. M., & Amant, R. S. (2021). Recent advances in trustworthy explainable artificial intelligence: Status, challenges, and perspectives. IEEE Transactions on Artificial Intelligence, 3(6), 852–866. https://doi.org/10.1109/TAI.2021.3074020
Sasilatha, T., Suprianto, A. A., & Hamdani, H. (2025). AI-Driven Approaches to Power Grid Management: Achieving Efficiency and Reliability. International Journal of Advances in Artificial Intelligence and Machine Learning, 2(1), 27–37. https://doi.org/https://doi.org/10.58723/ijaaiml.v2i1.380
Shafiq, S. I., Sanin, C., & Szczebicki, E. (2021). Integrating Experience-Based Knowledge Representation and Machine Learning for Efficient Virtual Engineering Object Performance. Procedia Computer Science, 192, 3955–3965. https://doi.org/https://doi.org/10.1016/j.procs.2021.09.170
Stolpe, K., & Hallström, J. (2024). Artificial intelligence literacy for technology education. Computers and Education Open, 6(October 2023), 0–7. https://doi.org/10.1016/j.caeo.2024.100159
Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., & Krishna, R. (2023). Explanations can reduce overreliance on ai systems during decision-making. Proceedings of the ACM on Human-Computer Interaction, 7(CSCW1), 1–38. https://doi.org/https://doi.org/10.1145/3585882
Yang, W., Ng, D. T. K., & Gao, H. (2022). Robot programming versus block play in early childhood education: Effects on computational thinking, sequencing ability, and self‐regulation. British Journal of Educational Technology, 53(6), 1817–1841. https://doi.org/https://doi.org/10.1111/bjet.13204
Zakaria, Z., Misinem, M., Sopiah, N., & Efrizoni, L. (2024). Scalability and Efficiency: A Comparative Study of Face Recognition Technologies. International Journal of Advances in Artificial Intelligence and Machine Learning, 1(1), 36–48. https://doi.org/https://doi.org/10.58723/ijaaiml.v1i1.296
Zhang, B., & Dafoe, A. (2019). Artificial intelligence: American attitudes and trends. Available at SSRN 3312874. https://doi.org/https://doi.org/10.2139/ssrn.3312874