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Limitations and Innovations in AI Models
This chapter explores the limitations of current AI models in knowledge integration and pattern recognition, discussing innovative strategies to enhance their capabilities. It highlights the challenges of deploying these models in real-world applications, especially in high-stakes environments like autonomous vehicles, while addressing the trade-offs between accuracy and computational costs. The chapter contrasts human learning with AI, emphasizing the philosophical and practical gaps that exist and the importance of real-world testing for model robustness.