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.

The 1956 Dartmouth Conference proposal and early connectionists assumed AI would be created by first precisely describing human intelligence and then simulating it. In reality, deep learning evolved to reverse-engineer cognitive functions without a pre-existing human understanding, a 180-degree turn from original expectations.

The cortex has a uniform six-layer structure and algorithm throughout. Whether it becomes visual or auditory cortex depends entirely on the sensory information plugged into it, demonstrating its remarkable flexibility and general-purpose nature, much like a universal computer chip.

AI models are not explicitly programmed with knowledge like word meanings. Instead, their training is a form of evolution that reverse-engineers cognitive functions that natural selection created over millennia, leading to convergent solutions like edge-detector neurons.

AI is moving beyond simply identifying patterns in existing research papers. It is now able to extrapolate fundamental biological principles, enabling it to understand complex systems from the ground up, like the relationship between atoms, molecules, and proteins.

Neural networks, like brains, emerge from countless small nudges during training rather than a premeditated architectural design. The field of interpretability, therefore, functions like neuroscience, attempting to reverse-engineer what this 'evolutionary' process has learned.

While biology (birds) provides initial inspiration for flight, progress eventually requires engineering machine-specific solutions (jet engines). Similarly, AI learned foundational principles from human cognition, but its recent breakthroughs come from non-biological methods like massive scaling. The focus should be on universal "laws of thought," not just mimicking biological hardware.

The computer industry originally chose a "hyper-literal mathematical machine" path over a "human brain model" based on neural networks, a theory that existed since the 1940s. The current AI wave represents the long-delayed success of that alternate, abandoned path.

Liquid AI's origins lie in MIT research modeling the nervous system of the C. elegans worm. This led to differential equation-based networks where a small number of complex "liquid neurons" could perform complex robotics tasks like autonomous driving, showcasing extreme efficiency.