Certainty and Risk in Hybrid AI

These sources examine the distinctions and integrations between deterministic algorithms, expert systems, and machine learning within modern computing. Traditional software relies on explicit, rule-based logic to produce predictable outcomes, whereas machine learning utilizes data-driven patterns to handle complex, probabilistic tasks. The texts highlight the evolution of artificial intelligence, tracing its history from the early success of rule-based expert systems to contemporary neural networks and autonomous military applications. Organizations like the Department of Defense and NIST are currently establishing frameworks to address the unique challenges of AI trustworthiness, data dependency, and system reliability. Ultimately, the literature suggests that the future of technology lies in hybrid architectures that combine the foundational control of traditional programming with the adaptive power of intelligent models.