Page 20 - AI & Machine Learning for Beginners: A Guided Workbook

Researchers confidently predicted that within a few decades,
machines would match human intellect. However, the journey
toward true AI would prove far more challenging than expected.

The First "AI Winter" (1970s)

By the 1970s, the once-high optimism surrounding AI began to fade
as researchers encountered significant limitations in scaling up
early AI systems. The ambitious promises of fully intelligent
machines failed to materialize, leading to disillusionment and a
decline in funding.

Key Challenges That Led to AI’s Decline

Computational Limitations: Early AI programs lacked the

computing power needed to handle complex tasks efficiently.
Lack of Practical Applications: While AI showed promise in

controlled settings, real-world applications remained limited.

Unmet Expectations: Researchers overestimated AI’s

progress, leading governments and investors to pull back funding.
The Lighthill Report (UK, 1973)

A critical turning point came when the UK government
commissioned the Lighthill Report, which criticized AI research
for failing to deliver meaningful progress. The report argued that AI
advancements were too narrow and impractical, leading to
reduced government support.

As a result, AI funding dried up, and research efforts stalled for
nearly a decade. This period became known as the "AI Winter"—a
time of slowed progress and skepticism about the field’s future.

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