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

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


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

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Coined in 1956 by Dartmouth Assistant Professor John McCarthy, ‘Artificial Intelligence (AI) is a general term that refers to hardware or software that exhibits behavior that appears intelligent.

Basic ‘AI’ has existed for decades, via rules-based programs that deliver rudimentary displays of ‘intelligence’ in specific contexts. Progress, however, has been limited — because algorithms to tackle many real-world problems are too complex for people to program by hand.


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Machine Learning: offloading optimisation

Machine learning (ML) is a subset of AI. All machine learning is AI, but not all AI is machine learning. Interest in ‘AI’ today reflects enthusiasm for machine learning, where advances are rapid and significant.

The goal of most machine learning is to develop a prediction engine for a particular use case. Machine learning algorithms learn through training.


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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. 


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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).


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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.


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