The Symbolic Era & Expert Systems (1950s-80s)

The field of AI began with a straightforward idea: if intelligence is about manipulating symbols and following logical rules, you should be able to program a computer to do it. Early pioneers like Alan Turing, John McCarthy, and Marvin Minsky believed that encoding human knowledge as explicit rules - if X then Y - would eventually produce genuinely intelligent machines. For a while, it seemed to work. Programs could prove mathematical theorems, play chess at a reasonable level, and solve logic puzzles. By the 1980s, "expert systems" were big business - software that captured specialist knowledge from doctors, engineers, or financial analysts and applied it to new problems. Companies spent millions building these systems. The trouble was that the real world is messy, and writing rules to cover every situation turned out to be impossibly complex. An expert system for diagnosing car faults might handle a thousand scenarios beautifully, then fail completely on scenario one thousand and one. These systems were brittle, expensive to maintain, and couldn't learn from experience. They represent an important lesson: intelligence isn't just about knowing rules - it's about knowing when to break them.