(as explained by.... AI)
AI generates horrifying distortions
primarily because it lacks a human-like understanding of reality
. Instead of comprehending objects, proportions, and context, AI models identify mathematical patterns in their training data. When this process fails, the results can fall into the "uncanny valley," where an image is almost—but not quite—realistic, making it unsettling to human observers. The underlying causes of horrifying distortions
Flaws in training data
- Incomplete or biased datasets: AI models are trained on massive datasets of internet images, but these sets are rarely perfect. If the data lacks diverse examples of human anatomy, the AI will fail to generate realistic images. For example, censoring images with nudity can prevent an AI from accurately learning human anatomy, leading to distortions.
- Low-quality or filtered data: If the training data includes low-resolution, filtered, or altered images, the AI can produce results that are over-processed and unnatural-looking.
Misinterpretation of concepts
- Pattern, not meaning: An AI sees an image as a collection of pixels and mathematical patterns, not a cohesive scene with a narrative. It doesn't know that a human has five fingers or that eyes must be symmetrical. It simply predicts the next pixel based on what it has seen before, leading to misshapen hands, mismatched eyes, and other unsettling features.
- Balancing realism and abstraction: Human artists can choose when to abstract or simplify features, but AI models may struggle with this balance. When generating a human form, an AI might focus excessively on one detail, like skin texture, while distorting the overall structure because it lacks a clear sense of priorities.
Technical limitations
- Challenges with complexity: The human body is one of the most complex things for an AI to render correctly. The intricate anatomical structures, subtle movements, and nuanced details of hands and faces are areas where generative models often fail.
- Aspect ratio problems: Many image-generation models are trained on square images. When asked to create an image with a different aspect ratio, like widescreen, the AI may warp or duplicate elements to fill the extra space, resulting in surreal and distorted effects.
- Overfitting: If a model is overtrained on a limited dataset, it can become too focused on specific patterns. When generating human images, this can lead to exaggerated or distorted features that are not physically possible.
Why this creates the "uncanny valley" effect
Our brains are exceptionally good at recognizing human forms, and we are instinctively repulsed by anything that is "almost" human but has minor, unsettling flaws. AI distortions often hit this uncanny valley directly by creating imperfections in the features our brains are most attuned to, such as:
- Eyes: Subtle flaws in reflections, shape, or symmetry can make eyes look "off" or dead.
- Hands and teeth: The complex anatomy of hands and the symmetry of teeth are areas where AI frequently makes mistakes, adding too many fingers or misplacing teeth.
- Proportions: Unnatural body proportions, extra limbs, or features in the wrong place are common AI errors that trigger an instinctive sense of wrongness.
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