The Path
to Modern AI

From the Dartmouth Conference to Deep Learning: How artificial intelligence evolved through winters and springs

1956

Dartmouth Conference

John McCarthy coins term "Artificial Intelligence" and launches AI as a field

1966

First AI Winter

ALPAC report highlights limitations of machine translation systems

2000s

Machine Learning Era

Data explosion and computing power enable ML breakthroughs

AI Evolution Timeline

1950s: The Birth of AI

John McCarthy, Marvin Minsky, and others propose that "every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it."

1960s: Early Optimism

Government funding pours into AI research, particularly machine translation during the Cold War. Early successes with simple problem-solving programs.

1966-1974: First AI Winter

ALPAC report concludes machine translation is more difficult and expensive than human translation. Funding dries up as AI fails to deliver on early promises.

1980s: Expert Systems

AI experiences resurgence with expert systems that encode human knowledge in if-then rules. Private companies like IBM and Xerox invest heavily.

1987-1993: Second AI Winter

Expert systems prove brittle and difficult to maintain. Commercial failures lead to reduced funding and interest in AI.

1990s-2000s: Quiet Progress

Machine learning approaches gain traction behind the scenes. Focus shifts from rule-based systems to learning from data.

2010s: Deep Learning Revolution

Breakthroughs in neural networks, combined with big data and GPU computing, lead to unprecedented advances in image recognition, NLP, and more.

Two Types of AI

General AI

  • Also called Strong AI or Artificial General Intelligence (AGI)
  • Capable of performing any intellectual task a human can
  • Self-aware, adaptable to new situations
  • Remains theoretical - no existing examples
  • Similar to human-level intelligence

Narrow AI

  • Also called Weak AI or Applied AI
  • Designed to perform a single specific task
  • Excels within limited parameters
  • All current AI applications are narrow AI
  • Examples: Image recognition, spam filters, recommendation systems

The Machine Learning Revolution

Why Now?

Three key enablers of the modern AI revolution:

Data Explosion

Digitalization created massive training datasets

Computing Power

GPUs and cloud computing enable complex model training

Algorithmic Advances

Breakthroughs in neural networks and deep learning

Machine Learning Definition

"Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed."

- Arthur Samuel, 1959

Traditional Programming

Humans define explicit rules for computers to follow. This approach works well for structured problems but fails for complex, ambiguous tasks.

Machine Learning

Computers learn patterns from data. Instead of being explicitly programmed, ML systems improve with experience.

Defining Artificial Intelligence

The AI Effect: "Once we get used to a technology in our daily life, we remove the AI badge of honor and start calling it just computer software."

Our Working Definition:

"Software that solves a problem without explicit human instruction"

Data Science vs. AI

Data Science focuses on extracting insights from data through analysis. AI focuses on creating systems that perform tasks autonomously.

AI in Practice

99% of successful AI applications today are powered by machine learning techniques applied to narrow, specific problems.

The Journey Continues...