Article

From Origins to Future: The Evolution and Prospects of Artificial Intelligence in the Reasoning Era

Bingyi Yang^1,*, Jiang Qu²

¹China Academy of Information and Communications Technology, Beijing, China

²China Academy of Information and Communications Technology, Beijing, China

^*Correspondence: Bingyi Yang, E-mail: yangbingyi@caict.ac.cn

Received: 2025-01-09Revised: 2025-02-19Accepted: 2025-02-25Published: 2025-03-25

J. Int. Eco. Glo. Gov., 2025, 2(2), 84-96; https://doi.org/10.12414/jiegg.250453

Abstract:

Abstract: With the release of the OpenAI o1 model, artificial intelligence (AI) technology has ushered in a new era of Reasoning. This article reviews the development history of AI technology, from the early days of symbolic reasoning and logic programming, to the data-driven era of machine learning, and to the current era of [...] Read More.

Keywords: Artificial Intelligence, Era of Reasoning; Global Governance, International Cooperation

Keywords: Artificial Intelligence, Era of Reasoning; Global Governance, International Cooperation

Abstract

Abstract

With the release of the OpenAI o1 model, artificial intelligence (AI) technology has ushered in a new era of Reasoning. This article reviews the development history of AI technology, from the early days of symbolic reasoning and logic programming, to the data-driven era of machine learning, and to the current era of deep learning and large models. Driven by the AI technology, the close connection and impact between economic support and policies have been analyzed, as well as the cyclical fluctuations in the development of AI industry. All sectors of industry, academia and research play different roles in the development of AI, and in the context of industrial profit-seeking and national security issues, the international governance variables of AI will further increase. In this context, this paper analyzes the dilemmas and risks of international cooperation in artificial intelligence, as well as the challenges facing the development of industrial ecosystem, and looks forward to the future development and international governance direction of AI.

Keywords: Artificial Intelligence, Era of Reasoning; Global Governance, International Cooperation

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Funding

Funding

None.

Conflicts of Interest

Conflicts of Interest:

The authors declare that they have no conflicts of interest to report regarding the present study.

References

References

Galanos, V. (2023). Expectations and expertise in artificial intelligence: specialist views and historical perspectives on conceptualisation, promise, and funding.
Kastner, J. K., & Hong, S. J. (1984). A review of expert systems. European journal of operational research, 18(3), 285-292.
Boyd, R., & Holton, R. J. (2018). Technology, innovation, employment and power: Does robotics and artificial intelligence really mean social transformation?. Journal of Sociology, 54(3), 331-345.
Helbing, D. (2019). Societal, economic, ethical and legal challenges of the digital revolution: from big data to deep learning, artificial intelligence, and manipulative technologies (pp. 47-72). Springer International Publishing.
Stuart Russell and Peter Norvig. 2010. Artificial Intelligence: A Modern Approach (3 ed.). Prentice Hall.
Müller, A. C., & Guido, S. (2016). Introduction to machine learning with Python: a guide for data scientists. O'Reilly Media.
Bishop, C. M. (2006). Pattern recognition and machine learning (Information Science and Statistics series). Springer.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Advances in neural information processing systems (Vol. 25, pp. 1097-1105).
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
Zwitter, A. (2014). Big data ethics. Big Data & Society, 1(2), 1-6. https://doi.org/10.1177/2053951714559253
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems (Vol. 30).
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020). Language Models are Few-Shot Learners. NeurIPS 2020, 33, 1877-1901. https://arxiv.org/abs/2005.14165
Bommasani, R., Hudson, D. A., Adolphs, R., Schuessler, H. F., & Weld, D. S. (2021). On the Opportunities and Risks of Foundation Models. Communications of the ACM, 64(12), 62-71. https://doi.org/10.1145/3460218
Vallor, S. (2022). Technology and the Virtues: A Philosophical Guide to a Future Worth Wanting. Oxford University Press.
Shin, D., Lee, H., & Lee, S. (2023). The Governance of Artificial Intelligence: Perspectives, Challenges, and Solutions. AI & Society, 38(4), 845-859. https://doi.org/10.1007/s00146-023-01598-2
Ye, J. (2023). China’s AI “War of a hundred models” heads for a shakeout. Reuters. https://www.reuters.com/technology/chinas-ai-war-hundred-models-heads-shakeout-2023-09-21/
Kaplan, J., Henighan, T., Chess, B., Child, R., Gray, S., Brown, T. B., Ko, A., Dennison, D., & Amodei, D. (2020). Scaling laws for neural language models. arXiv preprint. https://arxiv.org/abs/2001.08361
Smith，J. (2023). The Llama 3 Herd of Models. arXiv, 2407.21783.
Hestness, J., et al. (2023). Large Language Monkeys: Scaling Inference Compute. arXiv preprint arXiv:2305.12148.
OpenAI. (2024). Learning to reason with LLMs. OpenAI Blog. https://openai.com/blog/learning-to-reason-with-llms
Villalobos, P., Ho, A., Sevilla, J., Besiroglu, T., Heim, L., & Hobbhahn, M. (2024, June 4). Will we run out of data? limits of LLM scaling based on human-generated data. arXiv.org. https://arxiv.org/abs/2211.04325
Anwer, S., Hosen, M. S., Khan, D. S., Oluwabusayo, E., Folorunso, M. M. H. M., & Khan, H. (2024). Revolutionizing the global market: An inclusion of AI the game changer in international dynamics. Migration Letters, 21(13), 54-73.
Garcia, E. V. (2024). The technological leap of AI and the Global South: deepening asymmetries and the future of international security. In Research Handbook on Warfare and Artificial Intelligence (pp. 370-387). Edward Elgar Publishing.
Mügge, D. (2024). Eu AI sovereignty: for whom, to what end, and to whose benefit?. Journal of European Public Policy, 1-26.
Collingridge, D. (1982). The social control of technology.
Wallach, W., & Marchant, G. E. (2018). An agile ethical/legal model for the international and national governance of AI and robotics. Association for the Advancement of Artificial Intelligence.
International Telecommunication Union. (2017). AI for Good. https://aiforgood.itu.int/about-us/#:~:text=AI%20for%20Good%20was%20established,industry%2C%20civil%20society%20and%20UN.
Cavanagh, C. (2023). U.S. economic restrictions on China: Small yard, high fence?. Georgetown Security Studies Review. https://georgetownsecuritystudiesreview.org/2023/12/26/u-s-economic-restrictions-on-china-small-yard-high-fence/
United Nations. (2024). Enhancing international cooperation on capacity-building of artificial intelligence : resolution / adopted by the General Assembly. https://digitallibrary.un.org/record/4054005?ln=en&v=pdf