Welcome to this new series, “AI, But what is it really?” - exploring the core concepts behind artificial intelligence, one idea at a time.
Artificial Intelligence is everywhere right now - in headlines, tools, startups, fears, and dreams. But the term gets thrown around so loosely that it’s easy to lose sight of what it actually means. Is AI just chatbots and neural networks? Is it machine learning? Is it robots? Is it Python?
This series aims to slow things down and build up a real understanding, piece by piece. In this series, we’ll explore the fundamental concepts in AI. We begin with the most basic (and most misunderstood) question: what even is AI?
A Brief History of the Idea of Intelligence
The quest to understand and replicate intelligence didn’t start with computers. It goes back over 2,000 years to philosophers, mathematicians, and logicians trying to formalise how we think, reason, and learn.
Aristotle laid the early foundations of formal logic c. 350 BC.
Euclid’s algorithms, c. 300 BC, anticipated procedural problem-solving.
Francis Bacon introduced inductive reasoning in the 16th century - an early precursor to learning from data.
Thomas Bayes (17th century) and Blaise Pascal (18th century) laid the foundations of probabilistic reasoning.
Alan Turing, in 1950, proposed the imitation game and imagined a child machine - a mind that could learn and grow through experience.
By the time the term “Artificial Intelligence” was coined at the Dartmouth Conference in 1956, many of the core ingredients were already in place: logic, search, probability, learning, and formal systems.
“I believe that in about fifty years time [2000] it will be possible to programme computers to make them play the imitation game so well that an average interrogator will not have more than 70 per cent chance of making the right identification after five minutes of questioning.” - Alan Turing
So, What Is AI?
At its core, intelligence is the ability to perceive, reason, and act adaptively in response to a changing environment. Artificial Intelligence, then, is the field that aims to build systems that can do exactly that - not just compute, but behave intelligently. Russell & Norvig’s classic textbook frames it around four perspectives:
“Systems that think humanly, act humanly, think rationally, or act rationally.”
That’s a broad space - and on purpose. AI isn’t one technique. It’s a long-term attempt to make machines that behave intelligently, however that’s defined.
AI Is Not ML Is Not DL
Let’s clear up a common confusion:
Artificial Intelligence (AI) is the goal: intelligent behaviour.
Machine Learning (ML) is one approach to achieving that goal, focused on learning from data.
Deep Learning (DL) is a type of ML model.
Python (or any language) is just a tool, not part of the definition.
We can build AI without learning, and we can use learning without achieving intelligence.
Thinking in Terms of Agents
One of the most useful ways to understand AI systems is to think in terms of agents - entities that perceive their environment and act upon it to achieve specific goals. This agent-based view helps us break down complex behaviours into manageable, modular components.
Agents come in many forms, each with increasing levels of sophistication:
Reactive agents respond directly to their current sensory input. They don’t store memory or learn - they simply react based on predefined rules. Think of a basic obstacle-avoiding robot.
Model-based agents go a step further by maintaining an internal representation (or model) of the world. This allows them to handle partial information and make decisions based on past perceptions, not just current input.
Goal-based agents evaluate possible actions against desired outcomes. Instead of blindly following rules, they consider what they’re trying to achieve and plan accordingly.
Utility-based agents assign value (or “utility”) to outcomes and choose actions that maximise expected benefit, often weighing trade-offs under uncertainty.
Learning agents improve over time. They adapt their behaviour based on experience, feedback, or exploration, allowing them to function effectively even in changing or unknown environments.
This layered framework allows us to build intelligent behaviour incrementally, starting with simple reflexes and evolving toward agents that can plan, evaluate, and adapt in complex environments.
Why Knowledge Matters
But sensing and acting aren’t enough. To behave truly intelligently, an agent must have some understanding of how the world works. That’s where knowledge representation and reasoning (KRR) come in. KRR is a fundamental area of AI that focuses on how knowledge about the world can be formally represented and used by a machine to solve complex tasks. This discipline intertwines the cognitive aspects of how humans understand and process information with the computational methods necessary for enabling machines to exhibit intelligent behaviour.
AI systems need a way to structure information about the world - facts, rules, relationships, categories - so that they can reason, infer, and make informed decisions. This is more than storing data; it’s about organising information in a way that supports meaningful conclusions.
Common approaches include:
Symbolic logic, such as propositional and first-order logic, which allows systems to make precise, rule-based inferences.
Ontologies and taxonomies, which define structured hierarchies of categories and concepts, like “a car is a kind of vehicle.”
Rule-based systems that encode expert knowledge as a series of “if-then” statements.
Semantic networks, where objects, attributes, and relationships are connected in graph-like structures for efficient reasoning.
These methods allow AI agents to go beyond reactive behaviour. For example, if a system knows: all fruit is edible, and apples are fruit, it can logically infer that apples are edible. That’s reasoning - not hardcoded knowledge, but structured inference drawn from more general principles. In short, knowledge representation enables AI to move from responding to understanding - from executing actions to explaining why those actions make sense.
Looking Ahead
Artificial Intelligence is not just deep learning, chatbots, or hype. It’s the structured study of intelligent behaviour, grounded in perception, logic, learning, and reasoning. In this article, we unpacked its roots, core definitions, and the foundational idea of intelligent agents.
In the next article, we’ll explore one of the most fundamental tools in any intelligent system: search. What is search, and why is it needed? How does human search compare to how machines search? And how do we translate real-world problems into ones a machine can solve efficiently.
See you then!
