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Artificial Intelligence (AI) could add £654 billion to the UK economy by 2035, but as it marches into the mainstream, the terminology that describes it is causing confusion.
The buzzwords of AI, machine learning and deep learning are often used interchangeably, despite each meaning something different.
Stanford computer scientist John McCarthy is credited with coining the term "artificial intelligence". He defined it in a conference on the subject held in 1956 as: "The science and engineering of making intelligent machines, especially intelligent computer programs."
The ambiguity of the word "intelligent" allows AI to cover a range of applications, but most researchers agree that it broadly refers to something that replicates human thought.
Machine learning is a subset of AI that grants computers a degree of independent thought. This is achieved by giving it large volumes of data that an algorithm can process and then learn from in order to make predictions and decisions for which it hasn't been specifically programmed. The machine is effectively learning to solve new problems from existing examples.
Deep learning, meanwhile, is a type of machine learning inspired by the connections between neurons in the human brain. Researchers developed a man-made imitation of this biological connectivity known as artificial neural networks (commonly known as neural nets).
Deep learning in practice
In human neural networks, billions of interconnected neurons communicate with each other by sending electrical signals that develop into thoughts and actions. In artificial neural networks, nodes take the role of neurons, and collaborate in an organised structure to solve problems through their combined analyses.
For example, deep learning software could be used to understand a complex photograph made up of overlapping items, such as a full laundry basket.
The nodes are arranged in separate layers and each reviews individual elements of the picture and makes computations about that specific element in order to fully understand it. These computations result in a signal being passed on to the other nodes.
All the signals in the layers are then assessed in combination to make a final prediction as to what exactly is in the picture.
The advantage that deep learning has over alternative forms of machine learning is that while the others need to analyse a predefined set of features on which they base their predictions, deep learning can identify the individual features itself.
For example, if a system wanted to identify human faces in a photo it would not need to be first be fed the individual features, such as noses and eyeballs. It could instead be fed an entire image that it can scan to understand the different features in order to make an independent prediction about the content of the images.
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