In this episode, we explore the early history of artificial intelligence — beginning with Alan Turing’s groundbreaking ideas about machine intelligence and moving through the rise of symbolic reasoning, early AI programs, and the era of expert systems.

We trace how researchers in the 1950s through the 1980s imagined intelligence as something that could be represented with rules, logic, and carefully structured knowledge. Along the way, we look at the optimism that defined early AI research, the breakthroughs that shaped the field, and the limitations that eventually became clear.

This episode covers:

• Alan Turing’s foundational role in defining machine intelligence
• The Turing Test and early philosophical questions about AI
• Early symbolic AI programs like the Logic Theorist and the General Problem Solver
• The significance of the 1956 Dartmouth Conference
• The growth of symbolic AI during the 1960s and 1970s
• The rise of expert systems and how they worked
• Real-world applications where expert systems thrived
• Why expert systems eventually declined
• How this entire era shaped modern AI research

Sources and Further Reading

Rather than listing individual books or papers here, you can find all referenced materials, recommended readings, foundational papers, and extended resources directly on our website:

👉 https://adapticx.co.uk

We continuously update our reading lists, research summaries, and episode-related references, so check back frequently for new material.