Originally published December 15, 2016
Here is an excerpt:
Machine learning is the main reason for the renewed interest in artificial intelligence, but deep learning is where the most exciting innovations are happening today. Considered by some to be a subfield of machine learning, this new approach to AI is informed by neurological insights about how the human brain functions and the way that neurons connect with one another.
Deep learning systems are formed of artificial neural networks that exist on multiple layers (hence the word ‘deep’), with each layer given the task of making sense of a different pattern in images, sounds or texts. The first layer may detect rudimentary patterns, for example the outline of an object, whereas the next layer may identify a band of colours. And the process is repeated across all the layers and across all the data until the system can cluster the various patterns to create distinct categories of, say, objects or words.
Deep learning is particularly impressive because, unlike the conventional machine learning approach, it can often proceed without humans ever having defined the categories in advance, whether they be objects, sounds or phrases. The distinction here is between supervised and unsupervised learning, and the latter is showing evermore impressive results. According to a King’s College London study, deep learning techniques more than doubled the accuracy of brain age assessments when using raw data from MRI scans.
The blog post is here.