AI encompasses knowledge representation, perception, optimisation, self organising systems, and complex algorithms based on genetics, evolution, and survival of the fittest. It draws inspiration from other natural systems like human organ function, and forms of intelligence such as animal, herd, and swarm intelligence to uniquely solve problems.

Weak AI vs strong AI

Weak AI

More commonly known as narrow AI, weak AI solves a single narrow task. It focuses on specific areas such as playing strategic games, language translation, self-driving vehicles, and image recognition.

Strong AI

Sometimes called artificial general intelligence, strong AI refers to a future system that shows intelligent behaviour as advanced as a person across the full range of cognitive tasks. This definition doesn’t include consciousness, which it may inherit or evolve into.

What we want AI to do

Think like a human

We're inspired by the massive distributed connectedness of the human brain and its neuron-firing mechanism. This is the basis for neural networks, cognitive architectures, bayesian inference and massive parallel processing.

Act like a human

Understands real world entities, and how they relate to each other through video capture and computer vision techniques. Communicates in human language, and understand people’s intentions and emotions through natural language processing techniques. Stores knowledge, can reason and continuously learn.

Think rationally

Solve problems through inductive or deductive logical reasoning. Provides good solutions to a constrained problem with multiple circumstances and outcomes. Optimises a decision to get maximum benefit. For example, the best next move in a  chess game.

Act rationally

Embodies a rational, intelligent system into an agent or robot. This can then be used by it’s own sensor data to achieve goals through perception, planning, reasoning, learning, communicating, decision-making, and acting.

Major areas of AI research

Expert Systems

Emulating human expert decision-making through reasoning about knowledge stored in a large set of conditional rules.

Natural Language Processing

Understanding natural human language.

Computer Vision

Making sense of the word through visual data.

Evolutionary Computation

Global optimisation methods inspired by Darwinian evolution.

Machine Learning

Learning without being explicitly programmed.


Machines that can deduce things about the world from visual sensory, sounds and other sensory inputs.

Five AI main mindsets

Symbolic AI 

Logical reasoning based on abstract symbols. Commonly referred to as ‘old AI’ and popular between 1950 and 1980, with its greatest contribution being expert systems.

Connective AI 

Builds structures inspired by the human brain. Includes neural networks and all their different architectures.

Evolutionary AI 

Methods inspired by Darwinian evolution, genetic algorithms, genetic programming, and particle swarm optimisation.

Bayesian AI 

Uses probabilistic inference, which includes gaussian processes, hidden Markov models and bayesian belief nets.

Analogistic AI 

Learns from similar cases experienced previously, resulting in simple algorithms that are easy to understand. Examples include k-nearest neighbour, naive bayes, and k-means clustering.

These are just a few ways for viewing AI, how we think about it and explain AI to our clients. They may not all resonate with you, but demonstrate our comprehensive, considered and expert approach to this topic. 

To find out how AI can help your business, get in touch for a friendly chat with a human expert on the subject.