How is the master algorithm changing the machine learning world?

An idea called the "master algorithm" is having an effect on how we view machine learning and artificial intelligence work.

The idea is popularly attributed to Pedro Domingos, a professor at the University of Washington, who bases the master algorithm on five different types of machine learning and artificial intelligence principles: symbolism, connectionism, evolutionism, Bayesian theory and analogizing.

The idea is that by unifying these disciplines, and creating an algorithm that works across channels, you can fundamentally advance what machine learning is able to do. This is related to the idea of deep learning networks that involve machine learning processes.

One of the aspects of the master algorithm is that it works across channels. Some experts explain this as the idea that algorithms can cross proprietary platforms that tell them more about consumers. For instance, one of the popular aspects of a master algorithm is that it would work with social media platforms like Facebook, and other environments like Google tools, to achieve a more comprehensive form of digital surveillance and a deeper relationship with the user or subject.

The master algorithm has won lots of attention from technology leaders like Bill Gates and world leaders like Xi Jinping – it's become an interesting and popular way to describe concepts like Hebbian learning, supervised and unsupervised machine learning, Bayesian logistics and more.

Some alternate meanings of "master algorithm" refer to other efforts to create comprehensive algorithms that will do more to mimic human and cognitive behavior – for instance, the idea of backpropagation as advanced by Geoff Hinton and others. However, the master algorithm idea set theorized by Pedro Domingos is the most popular example of how the master algorithm is getting used in the technology industry. Whether it's combining disciplines, modalities, platforms or types of cognitive work, the master algorithm promotes the idea that you can mix and blend different types of tools into one stronger and more capable application of machine learning and artificial intelligence.