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Artificial Intelligence stands poised to become a transformative force on par with the printing press, steam engine,
electricity, and internet—technologies that fundamentally reshaped human civilization. However, AI today faces
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fundamental challenges that prevent it from reaching this revolutionary potential. While AI excels at generating
creative and plausible outputs, it struggles to reliably provide error-free outputs. These limitations constrain AI
primarily to human-supervised tasks or lower-consequence applications like chatbots, falling far short of AI's potential
to handle high-stakes tasks autonomously and in real time.
The key barrier is AI reliability. AI systems suffer from two primary types of errors: hallucinations and bias, which
together determine a model's overall error rate. Current error rates remain too high for autonomous operation in
consequential scenarios, creating a fundamental gap between AI's theoretical capabilities and practical applications.
As AI models continue to evolve with increased training data and parametrization, these reliability challenges persist
due to the training dilemma. This dilemma mirrors the classical precision-accuracy trade-off: hallucinations represent
precision errors (the consistency of model outputs), while bias manifests as accuracy errors (systematic deviation from
ground truth). When model builders curate training data to increase precision and reduce hallucinations, they
inevitably introduce accuracy errors (bias) through their selection criteria. Conversely, training on diverse, potentially
conflicting data sources to improve accuracy (reduce bias) leads to decreased precision (increased hallucinations) as
the model produces inconsistent outputs across its broader knowledge distribution.
Fine-tuned models have been observed to achieve higher reliability within narrow domains; however, research has
shown that fine-tuned models struggle to reliably incorporate new knowledge, with training examples that introduce
novel information being learned substantially less effectively than those that align with the model's existing knowledge
base. Fine-tuned models also struggle with edge cases and unexpected scenarios outside their training domain, making
them unsuitable for autonomous systems that must handle diverse, real-world situations.
This fundamental constraint establishes an immutable boundary in AI model performance: there exists a minimum
error rate that cannot be overcome by any single model, regardless of scale or architecture. $MIRA #Mira @mira_network