Reconceptualizing the Epistemic Foundations of Artificial Intelligence: A Critical Analysis of Machine Learning Paradigms

Abstract

The rapid evolution of artificial intelligence (AI) has led to critical paradigms in machine learning that necessitate a re-evaluation of foundational concepts. This paper investigates the epistemic underpinnings of contemporary AI models, exploring the implications of their design and implementation on cognitive processes. Utilizing a comparative methodology, we analyze the interplay between algorithmic strategies and cognitive mimicry, revealing inherent biases and limitations. Our findings indicate that traditional paradigms may not adequately address the complexities of human-like reasoning, necessitating a paradigm shift towards more holistic models. This work advocates for a reconceptualization of AI frameworks that embrace interdisciplinary insights, paving the way for innovations that align more closely with ethical and cognitive standards in technology.