The future of deep learning
Deep learning can use both the common supervised learning technique and the more complex and cutting edge alternative of unsupervised learning.
In supervised learning, both the input and output variables are provided and classified. An algorithm only needs to follow an established process to generate new results when further input data is added. This is used in numerous current applications, such as making Amazon recommendations.
In unsupervised learning, the output data is unknown so there are no examples upon which a system can base its conclusions. It can only use the input data to solve the problem. It does this by extracting information from the data to uncover correlations and understand the underlying structure in order to draw its own conclusions. It is the autodidactic alternative to the teacher in a classroom model used in supervised learning.
An example of unsupervised learning would be a system independently classifying animals in a picture without being told what they are. It would do this through a process of description that involves dividing data into categories based on the differences and similarities. It would therefore label dogs and cats as different based on the distinctive features and correlations it finds in their pixels.
Deep learning can be used to transform smartphone photos into paintings that mimic the style and brushstrokes of the great masters, a technique that made Russian mobile application Prisma the leading app in its home country.
The effects of such powerful technology could have ominous consequences. It could be used to generate fake videos that look extremely convincing, for example.
Its potential for media manipulation and disinformation was demonstrated last year when researchers from Stanford University and the University of Erlangen-Nuremberg in Germany unveiled a project called Face2Face.
The programme uses deep learning algorithms and a commercial webcam to reanimate the facial expressions of people talking YouTube videos in real-time. Putting words into the mouths of politicians has never been easier.
Deep learning in the enterprise
For enterprises, any fears over deep learning will be allayed by the potential business benefits. Many of them are already exploiting it in wide-ranging applications, such as online travel agency Expedia.
When customers of the booking website review its hotel listings, their attentions are first drawn to images of the accommodation. Displaying the most attractive photos first would improve the chances of a hotel being chosen, but the company has a total of more than 10 million images from 295,000 hotels. Going through them all of them manually would be an interminable undertaking.
Instead, the data science team is using deep learning to automatically rank the images. A crowd-sourcing product developed by Amazon called Human Turk was used to provide ratings from 1-10 on 100,000 hotel images. Each image was rated twice and classified by traveller types.
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