By Caroline Hermon

Machine learning is very much the ‘topic of the moment’. It is being discussed everywhere, whether as machine learning, or as artificial intelligence. Machine learning techniques and tools have been around for a while, however, so why now? Is it just an idea whose time has come, or have there genuinely been new developments that have made a difference?

Two important developments

I think the answer is probably a bit of both. Machine learning was a bit of a niche market until recently. What has changed is two things. First is the increase in the volume of data—which makes it hard, if not impossible, to process by hand, or using simpler computational techniques. It also, however, makes it possible to train machine learning algorithms, because enough data is now available.

The second change is the potential computing power available. When you think back to the rooms that used to house computer servers, and the fact that we now have as much, if not more, computing power available in an individual smartphone, the exponential growth in power becomes clear. This power is also cheap. This makes a huge difference.

These two developments are important. But you also need a recognition that machine learning can help. More and more organisations are recognising the value of data-driven insights into behaviour, trends, and forecasts, and how they can improve the bottom line. Machine learning and artificial intelligence algorithms offer more powerful ways to get good insights. In that sense, therefore, it is an idea whose time has come.

Developing use cases

As a result of the rapid uptake of machine learning techniques, the number of use cases is also rapidly developing. This, in turn, spreads the information that machine learning may be able to help, creating a virtuous cycle of learning. Rapidly growing areas of developing include fraud detection and resolution, and techniques like image recognition used in motor insurance claims processing.

There are, however, far more examples of use cases than many people would even imagine. Machine learning techniques feature in automatic translation software (remember the ‘rate this translation’ button in Facebook? It feeds back to the algorithm, helping it learn).

They are part of search engine technology, stock management, planned maintenance, claims handling, and even simple customer care processes via chatbots. These systems are all using AI and machine learning technology to improve efficiency, and provide a better service to customers.

The sheer ubiquity of these systems and techniques suggests that most organisations are already likely to be using at least one. Which brings us back to the question of why machine learning is now such a hot topic. I think that the biggest issues are scale and visibility.