Purkinje is an AI feedback system I built, inspired by my studies at UCLA on the Purkinje neuron. This neuron plays a critical role in motor learning by processing errors and refining movements, making it the perfect model for an AI system designed to learn from its own mistakes.

Traditional AI models generate outputs based on pre-trained patterns but struggle with deeply nested, unstructured data. By integrating continuous human feedback, Purkinje improves with every iteration. It analyzes errors, makes adjustments, and refines its accuracy over time. This isn’t just another AI system. It learns like the human brain.

The Creation of Purkinje

Processing deeply nested, unstructured data was the challenge. Most AI systems fail when dealing with ambiguous inputs or complex data structures. My background in neuroscience helped shape the vision: a system that doesn’t just react to inputs but improves through experience, just like Purkinje neurons help the brain fine-tune motor control.

Purkinje ingests user manual corrections and compares them against its automatic processing. Whenever a discrepancy is detected, it logs where the automation went wrong. Just like Purkinje neurons, which prune connections based on error signals, this system triggers an adjustment to the model. The change is then tested against a benchmark. If the modification improves performance, it is promoted as the new version. If it degrades accuracy, the system reverts to the previous model.

This iterative approach mimics the same circuit found in mammalian muscle memory, but applied to an LLM-based system. Each adjustment is a micro-experiment, reinforcing pathways that work and discarding those that don't. Over time, this creates a self-correcting AI capable of refining its understanding in real-world applications.

Building this system came with both technical and philosophical challenges. How do you balance adaptability with reliability? How do you ensure that learning from human corrections doesn’t introduce biases? These questions shaped the system's architecture, making it iterative without losing stability.

Why Failure Is Essential

Purkinje turns failures into improvements. Every incorrect AI-generated result is a data point for learning. With each iteration, the system refines its accuracy, much like how human experience improves decision-making over time.

Traditional AI models struggle to adjust beyond their initial training data. Purkinje, by contrast, thrives on failure. The more mistakes it encounters, the smarter it becomes. Because each adjustment is tested against a benchmark, improvements are objectively measured, preventing the system from drifting into ineffective or biased behavior.

Broader Implications

The ability to learn from failure is fundamental not just to AI but to all intelligent systems. Purkinje shows what’s possible when failure is embraced as a design principle rather than avoided as a flaw.

This concept applies beyond AI. I’ve used this approach in startups, product design, and decision-making. The key is to create a system that refines itself through structured failure, whether it’s an AI model adjusting its predictions or an organization learning from past mistakes. Learning from failure isn’t a setback. It’s the fastest way to improve. Purkinje isn’t just an AI model. It’s a framework for how all systems, human and artificial, should be built to adapt and grow.