Many years ago, I too worked in AI. Compared with today’s Large Language Models (LLMs) — such as ChatGPT, Gemini, and others — our work was embarrassingly primitive. We were building Expert Systems to help with the diagnosis of software issues. They were called expert systems because we hoped to tease out of human experts their problem-solving rules that we could then encode and apply. The challenge expert systems faced, in fact the challenge all AI systems face, was how to capture, encode, and apply knowledge. We failed. Miserably. Or spectacularly, depending on how you look at it. Where chatbots (LLMs) have succeeded This compound challenge — capturing, encoding, and applying knowledge — is the specific problem LLMs (chatbots) have Read More ›