A practical history of AI

Early computing made it possible to ask whether machines could reason. The Turing Test and symbolic AI framed intelligence as rules, search, and problem solving.

Researchers built theorem provers, planning systems, and conversational programs. These systems were narrow, but they introduced ideas still visible in modern agents: goals, tools, and state.

Rule-based systems captured specialist knowledge for business and engineering tasks. They showed the value of structured knowledge, but they were brittle and expensive to maintain.

More data and compute shifted attention toward probabilistic methods, ranking, speech recognition, and machine learning systems that learned patterns instead of relying only on hand-written rules.

Search engines, recommendation systems, and large datasets made machine learning commercially central. AI became less about isolated demos and more about production systems.

Neural networks improved vision, speech, translation, and language tasks. Transformers made it practical to train large models that could reuse context across long sequences.

Large language models made natural-language interfaces and tool-using agent loops practical for software work. The hard part shifted toward context, trust, evaluation, and workflow design.