When to Use Traditional AI vs. Machine Learning
A deterministic evaluation framework for software architects. Compare Symbolic GOFAI, Classical ML (Gradient Boosters), Deep Neural Networks, and Neuro-Symbolic Hybrids against liability, explainability, data availability, and latency bounds.
Given strict determinism and zero tolerance for statistical error, Good Old-Fashioned AI (GOFAI) provides mathematically guaranteed outcomes without risk of hallucination or non-deterministic drift.
Automated Behavioral Simulation: "Sample Query Execution"
Input: Formal state verification testThe 3 Governing Principles of Architecture Selection
Machine learning and deep learning are statistical engines that guess the most probable outcome. Here is how to know when statistical approximation is a critical engineering liability.
⚖️ 1. Explainability & Legal Liability
In 2022, Air Canada tried to argue its hallucinating chatbot was a "separate legal entity." The tribunal held the airline liable. When compliance requires a traceable causal path (e.g., Fair Lending, medical diagnostics, tax calculation), rule-based expert systems allow human auditors to verify exactly which variable triggered a decision.
Case: Regulatory Compliance & Air Canada Lawsuit🧠2. Strict Mathematical Rules
If an environment has known, fixed governing laws—such as aircraft separation geometry, GPS pathfinding, or structural tolerances—statistical probability is a defect. A* search and constraint logic programming calculate mathematically guaranteed solutions every time without inventing physical impossibilities.
Case: Avionics, GPS, A* Search⚡ 3. Zero-Data & Microcontroller Limits
Training neural networks requires vast labeled corpuses and cloud GPUs. Traditional AI requires zero training data. If an engineer knows the temperature and pressure envelope of an industrial turbine, those rules execute in 64 kilobytes of memory on an edge microchip with instant deterministic latency.
Case: Edge IoT, Turbines, Automotive ECU