Silent Worker Teaching Method – AI alignment without modifying weights (github.com)

A groundbreaking method called the Silent Worker Teaching Method was introduced, enabling the alignment of AI systems without the need for costly modifications to neural network weights. Developed by factory worker Máté Róbert, this innovative approach leverages a "Watchdog" that enforces runtime constraints on AI actions. If the AI attempts a harmful action, the Watchdog denies it, providing robust feedback without changing the underlying model. This allows the AI to adjust its future outputs while preserving its original capabilities, marking a significant departure from traditional methods like reinforcement learning or fine-tuning, which require expensive resources and can inadvertently alter model performance. This method represents a paradigm shift in AI alignment, offering a zero-cost, instant solution with cryptographic proofs of compliance. The Silent Worker Teaching Method not only promises to make ethical AI behavior more accessible but also facilitates reproducible and verifiable results across various models, including those from OpenAI and Anthropic. By embedding core ethical principles directly into the system's DNA, this approach fundamentally transforms the way AI accountability is enforced, establishing a stringent, immutable framework that minimizes the risk of harmful outputs.