Machine learning (ML) is a branch of artificial intelligence that uses algorithms that are data driven, rather than algorithms programmed with instructions or procedures to perform specific tasks or functions. ML aims to analyse and interpret data by creating a model that makes automated data driven predictions or decisions within specific output ranges; based on given input data. In doing so, humans can understand that output data and use it. With experience, ML technology can use historical data to continually learn from and becoming progressively better at their requested tasks or function (Chapman, 2018; Tagliaferri, 2017).

Two major types of learning algorithms are utilized in ML. Supervised algorithms learn from labelled training data; creating models from known input and output data. Further predictions and decisions are based on learning from historical data. Unsupervised algorithms have unlabelled input data only and organise this into meaningful output data, by finding patterns or clusters (Chapman, 2018; Tagliaferri, 2017).

Deep learning as a subcategory of ML can use unsupervised and supervised algorithms to interpret sophisticated and large data sets, using artificial neural networks (ANN’s); inspired by the neuron structure of vertebrate brains and how they process information (Chapman, 2018; Tagliaferri, 2017).

Practical applications for ML are seen in many industries including crime prevention, such as predicting individuals with high re-offending risk and monitoring individuals on watch lists.  However, while ML has advantages, the technology is still developing and the disadvantages it offers also need considering (Chapman, 2018; Tagliaferri, 2017).

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References:

Chapman, D (2018) ‘Surveillance and Artificial Intelligence‘ Communication and information technologies, Protecting and Prying Oxford, Oxford University Press/Milton Keynes, The Open University, p.p. 120-132, 140.

Pikkart (n/d) Computer Vision and Deep Learning [Figure]. Available at: http://www.pikkart.com/contenuto/contenuti–ecm/1computer-vision–deep-learning.ashx (Accessed 24 May 2019).

Tagliaferri, L. (2017) ‘An Introduction to Machine Learning’, DigitalOcean Community Tutorials, 28 September [Online]. Available at https://www.digitalocean.com/community/ tutorials/ an-introduction-to-machine-learning (Accessed 23 May 2019).