In this episode, we explore the moments in history when enthusiasm for artificial intelligence suddenly cooled — the periods now known as the AI winters. These slowdowns weren’t just funding cuts or short pauses; they were turning points that reshaped the entire direction of AI research.

We look at what went wrong, why expectations collapsed twice, and what the field learned from these setbacks. From early symbolic systems struggling with real-world complexity to the boom and bust of expert systems, this episode unpacks how optimism turned into frustration — and how those challenges ultimately pushed AI forward.

This episode covers:

• What the term AI winter means and where it came from
• The first AI winter in the 1970s and the technical limitations that triggered it
• How government reports and unmet expectations affected funding and research
• The critical role of limited hardware, data, and computational power
• The second AI winter in the late 1980s and the collapse of expert systems
• Why expert systems failed to scale and maintain reliability
• How hype cycles and unrealistic promises shaped both downturns
• The lessons researchers carried forward into the statistical and machine learning eras
• Why the concept of “avoiding another AI winter” is still discussed today

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.