Clive Spenser pointed to a very interesting Oct-2024 article by Martin Milani that makes the case for coupling deep learning with expert systems.

“In the next generation of AI, advanced expert systems will serve as the core “intelligence,” acting as the primary engine for decision-making. While deep learning systems excel at recognizing patterns, these perceptual learning methods represent a subservient and lower form of intelligence. Just as in humans, where perception informs but does not govern complex reasoning, deep learning systems will be subordinate to expert systems that provide structured, logical, and complex decision-making.” Link

“Expert systems of the 1970s are one of the earliest successful applications of AI, representing a pivotal development in the pursuit of replicating human decision-making processes within intelligent machines…

Initially, expert systems followed pre-set rules, fixed within their knowledge base, meaning their reasoning was limited to the expertise available at the time of programming..

One of the remarkable features of modern expert systems is their ability to learn dynamically and continuously update their knowledge base. This shift has significantly enhanced the relevance and effectiveness of expert systems in dynamic environments where conditions change rapidly..

Similar to how human experts combine experiential knowledge with logical reasoning to solve complex problems, expert systems will complement human intelligence, working symbiotically to continuously improve.

Expert systems operate based on explicit rules and knowledge, much like human intelligence applies reason, logic, and problem-solving. Over time, they also advance by deducing new rules and gaining knowledge through dynamic learning. These systems represent the higher-order thinking in AI—analyzing complex inputs, drawing logical conclusions, and making high-stakes decisions that go beyond simple pattern recognition.

In highly intelligent autonomous systems, for instance, expert systems would handle the “why” and “how” of decision-making, applying reasoned judgment to determine the best course of action. This hierarchy, where perceptual learning feeds into expert reasoning, ensures “explainability”, consistency, and accountability in AI decisions. Such rigor is particularly crucial in fields like defense, healthcare, law, and autonomous systems, where decision-making requires precision, transparency, and deep understanding of cause and effect.“