AI HALLUCINATIONS 

Liviu Poenaru, Jan. 26, 2025

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AI hallucinations are a complex phenomenon that reveal the intricacies and limitations inherent in machine intelligence, manifesting as false or nonsensical outputs that deviate from reality. This issue arises from the probabilistic and pattern-recognition mechanisms of AI models, such as GPT, which synthesize responses based on statistical likelihoods rather than grounded understanding. While these hallucinations often manifest as confident but inaccurate outputs—such as fabricated scientific references or misinterpreted environmental sounds—they underscore deeper systemic flaws and philosophical questions about the nature of intelligence and knowledge.

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The technical underpinnings of AI hallucinations reveal critical limitations. Ambiguous or noisy inputs often cause AI to overgeneralize or misinterpret data, leading to hallucinated linguistic content from random patterns resembling speech. Large language models tend to generate coherent responses that prioritize linguistic fluency over factual accuracy. This design predisposition results in confident fabrication, particularly in specialized domains where the model lacks comprehensive contextual data. The training datasets themselves often include contradictions, biases, or incomplete information, leading to the synthesis of false outputs that appear plausible.

 

AI often struggles to replicate information exactly, as it compresses or simplifies inputs like scientific references, resulting in omissions or errors. It might also attempt to adapt references to a different style or format, introducing inconsistencies. Without persistent memory in most implementations, AI does not store previously provided information for later use. Instead, it regenerates responses from scratch, often deviating from the original input. AI systems also struggle to distinguish between "creative synthesis" and the faithful reproduction of exact data. When tasked with replicating references, the system generates text that aligns with learned patterns but fails to recognize the real-world significance of citations. This limitation stems from a design that prioritizes generating coherent and natural language over precision and accuracy.

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From a philosophical perspective, AI hallucinations challenge our understanding of intelligence, perception, and the boundaries of human-like cognition. The tendency of AI to "fill in the blanks" parallels human cognitive processes, such as imagination or dreaming, where maintaining coherence with the self and its experiences takes precedence over accuracy when information is incomplete. Notably, as articulated in the thesis "The hallucinatory process of displeasure and its foundations" (Poenaru, 2015), the hallucinatory process is not merely a malfunction but a normal phenomenon that accompanies perceptual processes. This perspective reframes hallucination as an intrinsic component of sense-making, rather than an anomaly. It suggests that both humans and AI navigate sensory and informational inputs by constructing coherent interpretations, even when data is incomplete or ambiguous.

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The insights from "The hallucinatory process of displeasure and its foundations" emphasize that hallucination, far from being a mere error, arises from the interplay of memory, perception, and unresolved or subjective experiences. In humans, this process highlights how memory traces tied to displeasure can dominate perceptual experiences, leading to repetitions of unresolved emotional conflicts. Similarly, in AI, hallucinations emerge as artifacts of its probabilistic reliance on training data—reflecting biases, gaps, and overgeneralizations within the data corpus. This parallel underscores a shared mechanism between humans and machines: both attempt to impose “coherence” and meaning (as decided by whom?) on incomplete inputs, often at the cost of accuracy.

 

The determination of coherence involves a complex interplay of individual perspectives and collective norms, shaped by personal experiences, cultural frameworks, and subjective realities informed by memory and emotional states. What feels coherent to one individual may reflect their internal psychological needs, learned frameworks, and lived experiences. In AI, coherence is determined algorithmically, shaped by the biases, priorities, and limitations embedded in its training data and programming. The developers of AI systems, intentionally or not, make implicit decisions about coherence by curating datasets, defining model architectures, and calibrating performance metrics. Thus, coherence—whether in humans or machines—is never entirely objective; it reflects the interplay of subjective perspectives, systemic norms, and the boundaries of available knowledge.

This raises the question of whether hallucinations represent failures or emergent behaviors of creative synthesis. In creative contexts, such as brainstorming or storytelling, these "hallucinations" may even inspire novel ideas or unconventional solutions. However, in high-stakes contexts like medicine, law, or academia, hallucinations become critical failures that compromise trust, safety, and the integrity of knowledge.

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Moderating AI hallucinations requires systemic improvements in both AI design and user engagement. On the technical side, integrating real-time validation mechanisms to cross-check outputs against reliable databases could reduce the prevalence of hallucinations in factual domains. Similarly, incorporating contextual memory systems and specialized training for domain-specific tasks would improve accuracy and reliability. Enhancing transparency is equally important; AI systems should be designed to signal uncertainty or explicitly indicate when outputs are based on incomplete or ambiguous data. On the user side, fostering critical engagement is essential. Users must approach AI outputs with skepticism, cross-referencing information, and providing iterative feedback to guide the system toward accuracy.

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AI hallucinations also invite broader philosophical and ethical reflections. They reveal the fragility of human trust in technological systems and highlight the limits of anthropomorphizing AI. While we often project intelligence and intentionality onto these systems, their outputs are the result of statistical processes rather than cognitive understanding. Hallucinations expose the biases and gaps in AI training data, serving as a mirror to the imperfections of human knowledge systems. This raises fundamental questions about whether AI should be optimized purely for accuracy or whether its capacity for creative synthesis—even when flawed—should be embraced as a unique form of intelligence.

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AI hallucinations straddle the line between failure and emergent behavior. They highlight systemic flaws in how AI systems are trained, designed, and deployed, yet they also underscore the distinct capabilities of generative models to synthesize and innovate. Overcoming hallucinations necessitates a multidisciplinary approach that integrates technical innovations, user training, and philosophical inquiry. As we continue to navigate the evolving relationship between humans and AI, these hallucinations serve as a reminder of the potential and limitations of machine intelligence, urging us to engage with it critically and cautiously.

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GO FURTHER

Clark, A. (2013). Surfing uncertainty: Prediction, action, and the embodied mind. Oxford University Press.

Dehaene, S. (2014). Consciousness and the brain: Deciphering how the brain codes our thoughts. Viking.

Minsky, M. (1986). The society of mind. Simon and Schuster.

Poenaru, L. (2015). L'hallucinatoire de déplaisir et ses fondements. Une approche neuropsychanalytique. Sarrebruck: Éditions universitaires européennes.

Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386-408.

Varela, F. J., Thompson, E., & Rosch, E. (1991). The embodied mind: Cognitive science and human experience. MIT Press.

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