AI technology: a lawyer's guide
Deep learning
The defining characteristic of deep learning is that it takes an input and produces an output, and then this output is used as the input for the next layer of processing.
It is ‘deep’ because of its many layers, each layer being a separate algorithmic function. On the one hand, this means that there can be ‘deep learning’ systems that don’t appear to be AI at all. On the other hand, this technique has allowed for advances in computing that produce ‘intelligent’ behaviours: behaviours like image classification and text recognition that previously were performed only by humans.
An advantage of deep learning is that it can structure and weight values appropriately. Each successive layer aggregates the outputs of the previous layers and can adjust weightings of each previous layer accordingly so that the desired result is reached. This is useful in conjunction with machine learning, as it can use large datasets to adjust its own weights in a way that is beyond the ability of human operators. Deep learning also mimics human intelligence in that it replicates the way we make decisions: prioritising the most important factors over lesser ones.
As an example, image classification works by the first layer’s algorithm classifying the individual pixels in an image based on their colour. This itself is not particularly meaningful information; it does not allow a computer to determine whether there’s a particular object in that image. If the same object appeared in two pictures, but was placed slightly differently in one in comparison to the other, then the pixel values at each location in each image would be different and so the first layer would not by itself be able to classify the image.
Deep learning applies a second layer, so that a second algorithm recognises the relationships of certain pixels to each other. Together these two layers will be able to recognise certain features of an object – for example the ear of a cat.
Other layers are then used to recognise other features, with further layers used to recognise when these features are correctly positioned in relation to each other (i.e. for a cat you need two ears on the top of its head, rather than two ears anywhere). The final layer aggregates the outputs of all previous layers, to decide whether the combination of those outputs means the object is in the image.
Machine learning is usually used in conjunction with this deep learning process in order to train each layer to recognise when the desired feature is present.
Author
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Giles Pratt Partner
London