Proactive AI Systems: Engineering Intelligent Platforms that Sense, Predict, and Act
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V5I3P112Keywords:
Proactive Artificial Intelligence, Predictive Analytics, Autonomous Systems, Intelligent Platforms, Anticipatory Computing, Decision Intelligence, Self-Adaptive SystemsAbstract
Proactive Artificial Intelligence (AI) systems are paradigm shifts in the old models of computations based on traditional reactive and rule-driven computational frameworks to intelligent platforms that can sense continuously, reason predictively and act autonomously. The traditional AI applications will normally react on explicit user inputs or preset events and this limits the effectiveness of such applications in very dynamic, unpredictable and complex environments. Conversely, proactive AI systems are designed to infer the future, detect new risks and opportunities and apply context specific interventions without having to be prompted by humans. This is becoming more important in areas like smart cities, health care, cybersecurity, industrial automation, financial systems and intelligent governance where the timeliness of response may have serious economic, social or safety impacts. In this paper, I will provide an in-depth engineering analysis and analytical examination of proactive AI systems with the perspective of understanding the architectural basis, algorithmic underpinning, and system-level design principles to facilitate machine sensing of environment signals, forecast the future, and make autonomous decisions. The paper integrates multi-artificial knowledge in machine learning, data engineering, control theory, cognitive computing, and distributed systems to establish a cohesive approach to proactive intelligence. This paper suggests a multi-layer system architecture which combines perception layers that obtain real-time data, predictive intelligence layers that predict and evaluate risks, and action layers that make autonomous decisions and adaptive changes. An extensive literature review shows how proactive intelligence has developed over the years since the first rudimentary expert systems and models of control to the current state of deep learning-powered anticipative systems. The methodology section makes the proactive AI lifecycle official, presents the mathematical models of the predictive decision-making, and describes the mechanisms of learning that facilitate self-improvement and optimization on the basis of feedback. The experimental outcomes and discussions reveal proactive AI systems to have the benefits of fast latency of responding, good predictive power, functional resilience, and scalability as compared to reactive AI systems. Lastly, the paper discusses the ethical and governance and deployment issues and recommends future research directions in the area of trustworthy, explainable, and human-compatible proactive AI systems
Downloads
References
[1] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Hoboken, NJ, USA: Pearson, 2021.
[2] E. A. Feigenbaum, “Expert systems in the 1980s,” IEEE Expert, vol. 1, no. 1, pp. 9–20, 1986.
[3] T. Mitchell, Machine Learning. New York, NY, USA: McGraw-Hill, 1997.
[4] K. P. Murphy, Machine Learning: A Probabilistic Perspective. Cambridge, MA, USA: MIT Press, 2012.
[5] D. Blei, A. Ng, and M. Jordan, “Latent Dirichlet allocation,” J. Mach. Learn. Res., vol. 3, pp. 993–1022, 2003.
[6] D. K. McFarland and R. W. Picard, “Anticipatory computing: A survey,” IEEE Pervasive Comput., vol. 14, no. 1, pp. 21–29, 2015.
[7] J. Kleinberg, H. Lakkaraju, J. Leskovec, J. Ludwig, and S. Mullainathan, “Human decisions and machine predictions,” Q. J. Econ., vol. 133, no. 1, pp. 237–293, 2018.
[8] J. D. Long and J. A. F. Stokes, “Decision intelligence: Foundations and future,” IEEE Intell. Syst., vol. 36, no. 3, pp. 12–20, 2021.
[9] C. Chatfield, The Analysis of Time Series: An Introduction, 6th ed. Boca Raton, FL, USA: CRC Press, 2016.
[10] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.
[11] R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, 2nd ed. Cambridge, MA, USA: MIT Press, 2018.
[12] M. Wooldridge, An Introduction to MultiAgent Systems, 2nd ed. Hoboken, NJ, USA: Wiley, 2009.
[13] J. O. Kephart and D. M. Chess, “The vision of autonomic computing,” Computer, vol. 36, no. 1, pp. 41–50, 2003.
[14] C. Rudin, “Stop explaining black box machine learning models for high stakes decisions,” Nature Mach. Intell., vol. 1, no. 5, pp. 206–215, 2019.
[15] L. Floridi et al., “AI4People An ethical framework for a good AI society,” Minds Mach., vol. 28, no. 4, pp. 689–707, 2018.
