Part 8/9:
The subjective nature of perception and mathematical interpretation emerges in our interactions with neural networks and artificial intelligence. Training these systems poses challenges because they often rely on human input, which is inherently biased and limited by personal experiences. This raises essential questions about the nature of understanding and the potential pitfalls of human cognition within computational frameworks.
Just as individuals perceive optical illusions in differing manners, so too do AI systems face ambiguities when classifying data. This suggests that all knowledge—both human and artificial—is layered with its own limitations and biases.