Predictive technology introduces a fundamental tension. While AI offers unprecedented clarity into future outcomes, its very implementation makes the world more complex and interconnected. This creates a feedback loop where the tool for prediction is also a source of new, unpredictable variables.
The true, lasting impact of AI is not just in automating tasks but in fundamentally changing how humans perceive and interact with the future. By making outcomes more predictable, AI alters our core frameworks for decision-making and risk assessment, a profound societal shift that is currently under-recognized.
The development of neural networks wasn't a linear path. It involved a cycle where computer scientists and psychologists alternately abandoned and revived the concept. When one discipline hit a wall or lost interest, researchers in the other field would pick it up, solve a key problem, and reignite progress.
Today's AI, particularly neural networks, stems from a long tradition in cognitive science where psychologists used mathematical models to understand human thought. Key advances in neural nets were made by researchers trying to replicate how human minds work, not just build intelligent machines.
Despite AI's impressive capabilities, it lags significantly behind humans in learning efficiency. Today's models are trained on amounts of data that would take a person tens of thousands of years to consume, while a human child achieves language fluency in under ten years, indicating a fundamental algorithmic difference.