The fourth industrial revolution: a primer on Artificial Intelligence (AI)

Some of the most effective machine learning algorithms beyond deep learning include:

• ‘random forests’ that create multitudes of decision trees to optimise a prediction;
• ‘Bayesian networks’ that use a probabilistic approach to analyse variables and the relationships between them; and
• support vector machines that are fed categorised examples and create models to assign new inputs to one of the categories. 

Deep learning involves using an artificial ‘neural network’ — a collection of ‘neurons’ connected together.

An artificial neuron has one or more inputs. It performs a mathematical calculation based on these to deliver an output. The input-output function can vary. A neuron may be:

• a linear unit (the output is proportional to the total weighted input;
• a threshold unit (the output is set to one of two levels, depending on whether the total input is above a specified value); or a
• sigmoid unit (the output varies continuously, but not linearly as the input changes).

Example applications of AI include the following; there are many more.

1. Reasoning: Legal assessment; financial application processing; games; autonomous weapons systems.
2. Knowledge: Medical diagnosis; media recommendation; purchase prediction; financial market trading; fraud prevention.
3. Planning: Logistics; scheduling; navigation; physical and digital network optimization.
4. Communication: Voice control; assistants, and customer support; real-time translation of written and spoken languages; real-time transcription.
5. Perception: Autonomous vehicles; medical diagnosis; surveillance.