Artificial Intelligence (AI)

A Brief History 

The concept of intelligent machines has captivated humanity’s imagination for decades. However, AI only recently emerged as a practical science and technology. Key milestones include:

  • 1943 – Mathematician Warren McCulloch and neuroscientist Walter Pitt introduced the first computational models of neural networks.
  • 1950 – Alan Turing proposes the Turing Test to determine if a machine can exhibit intelligent behaviour equivalent to a human. 
  • 1956 – The term “Artificial Intelligence” was coined at an academic conference at Dartmouth College.
  • 1997 – IBM’s Deep Blue defeats world chess champion Garry Kasparov.
  • 2011 – IBM’s Watson triumphs at Jeopardy against human champions.
  • 2014 – Google acquired DeepMind, leading to mastering the games Go, chess, Atari games and more.
  • Today – AI continues advancing via machine learning, accomplishing increasingly complex tasks across industries.

Defining Artificial Intelligence

Artificial intelligence (AI) refers to computer systems or machines that can perform tasks normally requiring human intelligence. Key capabilities include:

  • Learning – Algorithms can analyze data to improve tasks over time.
  • Reasoning – Systems can use logic and inference to solve problems.  
  • Self-correction – AI can identify and fix errors without human input.
  • Perception – AI can recognize speech, objects, faces, and more.

Two Types of AI

There are two main types of AI:

Artificial Narrow Intelligence (ANI)

Also called weak AI, ANI excels at a single specialized task like:

  • Playing strategic games
  • Language Translation 
  • Recognizing faces
  • Driving vehicles

Artificial General Intelligence (AGI)

Also known as strong AI, AGI aims to create fully thinking machines with general cognitive abilities. For example:

  • Learning/applying knowledge to new domains
  • Reasoning across multiple areas
  • Self-awareness and consciousness

True AGI does not exist yet. Replicating the complexity of human intelligence remains an ongoing challenge.

Real-World AI Applications

Today’s ANI systems have delivered revolutionary capabilities and benefits across industries, including:

Healthcare – AI can analyze medical data to diagnose health conditions and plan treatments. Business– AI informs decisions, predicts outcomes, automates processes, and optimizes operations.  

Transportation – AI-powered self-driving vehicles promise increased safety and efficiency.

Defence – AI can analyze threats, deploy resources, and guide weapons.

Finance – AI algorithms execute trades, detect fraud, and manage accounts.

Entertainment – From media recommendations to interactive gaming, AI delivers personalized experiences.

The Future of AI

Experts believe continued advances in machine learning will drive AI’s evolution, including:

  • New neural network architectures. 
  • Increased availability of training data.
  • More powerful computer processing.
  • Algorithmic innovations.

While AI will likely become even more capable and ubiquitous, replicating the breadth and complexity of human cognition remains challenging. Ongoing research seeks to make AI systems safer, more trustworthy and beneficial for humanity.


Is AI dangerous or likely to become self-aware?

Today’s AI systems are inherently narrow and unable to exhibit general intelligence or consciousness like humans. However, researchers are working to make AI probably beneficial and safe as capabilities improve.

Can AI be creative or emotional?

While AI cannot innately be creative or experience emotions, some systems fake these abilities by analyzing data and mimicking human creative output. Future advancements in AGI could theoretically lead to AI with imagination and emotional intelligence.

Will AI take people’s jobs?

AI will transform many jobs and potentially reduce employment for certain roles. However, experts believe it will create new job categories and boost productivity. The goal is to manage AI’s impact on employment through policy and education.