Deep, Convolutional and Recurrent Neural Networks Sample Clauses

Deep, Convolutional and Recurrent Neural Networks. Building on the above (XXX) approach to machine learning is a more recent development, deep learning, which is effectuated through a deep neural network (DNN).196 This has become increasingly viable with a) the exponential rise in computing power, and b) the availability of ever-larger datasets, partly as a result of the proliferation of internet-enabled devices.197 The outcome of this twin phenomena has seen newer ANNs superseding previous ones by a factor of ten or more.198 With so many nodes and connections between them, AI researchers are now able to take another cue from the biological brain: namely, to organise those nodes into distinct, hidden hierarchical layers, with each successive layer identifying higher level abstractions.199 Figure 2.4, below, illustrates this model. After feeding input data (images of a face), the first hidden layer of the network scans individual pixels, their brightness and colours, to identify edges and shadows. The second hidden layer identifies combinations of edges, to recognise specific features like ‘eyes’, ‘nose’ or ‘mouth’. This continues until the output layer, which classifies the object (‘face’).200 In a military context, the same approach may be used to train a network to identify military objects, like tanks. Thus, after feeding input data (images of a tank), the first hidden layer may scan individual pixels, to identify edges and shadows. The second may identify combinations of edges and shadows, to recognise specific features like ‘tank treads’ or ‘gun barrel’. This will continue until the output layer, which should classify the object as a ‘tank’. 196 XxXxx, Xxxxx and Xxxxxx (n 182). 197 Xxxxxxxx (n 176). 198 Xxxxxx Xxx, ‘Biggest Neural Network Ever Pushes AI Deep Learning’, IEEE Spectrum (8 July 2015) <xxxx://xxxxxxxx.xxxx.xxx/tech-talk/computing/software/biggest-neural-network-ever-pushes-ai-deep- learning> accessed 13 May 2018. 199 ‘Artificial Intelligence: Rise of the Machines’, The Economist Briefing (9 May 2015). 200 Xxxx Xxxx Xxxxxx, ‘Tutorial: Deep Learning in Human and Computer Vision’ (30 August 2017) <xxxx://xxxx.xxx.xxx.xx/people/BRomeny/Courses/Taipei2017/index.html> accessed 13 May 2018. Figure 2.4: Deep neural network and its hidden layers. Source: Romeny (n 200). It is the use of these interlinked layers that gives this approach to machine learning its ‘deep’ characteristic. Adding more layers between input data and output makes the network more complex, enabling it to handle more sophistica...
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