We’re all familiar with the idea of robots – physical and virtual – designed to make our lives easier. They’re typically pre-programmed to follow set scripts to carry out specific tasks. But what happens when computer systems start to not only execute pre-set programs, but actually absorb information and become smarter through machine learning?

A tool for evaluating data

Machine learning is nothing new; it’s been used in technology for many years. The term itself was invented as early as 1959, by researchers at IBM. The concept was born out of the field of pattern recognition, where machine learning would involve algorithms that could evaluate data and make predictions for the future based on historic information.

Is machine learning really learning?

Although we use the term “learning” for this type of computing, it’s not about machines becoming smarter in a cognitive sense. The learning is based on the processing of data and identifying patterns that may predict future scenarios – but these predictions will only ever be as good as the data that is being analysed. It doesn’t necessarily mean the computer will help people make better decisions.

Is machine learning the same as Artificial Intelligence?

When machine learning first was designed, it was considered to be a stepping stone on the journey towards Artificial Intelligence (AI).  However, as AI developed, it became gradually clear that they were two distinct strands of technology. Traditional machine learning was dependent on having enough (and accurate) data available to draw statistical conclusions from it, whereas AI was about emulating the logical decision-making of a human being. At this point, the field of machine learning changed to focus on problem-solving using statistics and probability theory.

Neural networks

With the arrival of the internet and the huge accumulation of big data available, machine learning obviously became a different animal. The access to so much more detailed and specific data became the perfect backdrop for the development of neural networks. This is a type of machine learning that processes information and classifies it in a similar way to how a human brain would. It can learn to recognise and classify things like images, music and speech – and learn to suggest suitable options based on previous choices. It is, however, still only operating on a probability basis.

Some examples of machine learning

These are a few examples of how we’re using machine learning on a daily basis:

  • Searching and finding
    Search engines use machine learning to understand how to display and rank content the user may be interested in. In a similar way, platforms like Amazon and Netflix use machine learning to offer recommendations.
  • Financial predictions
    The finance industry relies heavily on machine learning to carry out credit scoring, spotting credit card fraud and analysing stock market trends.
  • Astronomy
    Machine learning plays a big part in the analysis of astronomical data and the identification of shifts and patterns, while also helping us understand what the information means.
  • Social networks
    The amount of data shared on social networks is a treasure trove for marketers and social media providers, as the use of machine learning helps to predict what content the user might be interested in.

With the developments in machine learning, we can expect to see a lot more of intelligent systems in our everyday lives. Strange as it sounds, the machine learning technology may even play a part in predicting its own future!