First of all, it’s not as new as most think. It’s actually been used in tech for many years and the term was invented around 1959 by IBM researchers. The concept was born from pattern recognition involving algorithms which evaluate data and make predictions.
It’s not about machines becoming smarter in a cognitive sense. The learning is based on the processing of data and the predictions made can only be as good as the data being analysed.
We can train a computer to use data to perform tasks such as predicting user load on a website, highlighting anomalous activity within a website and predicting how systems might need to scale in the future.
When machine learning was first designed, it was considered to be a stepping stone on the journey towards Artificial Intelligence. However, it soon became clear that they are two distinct strands of tech. Machine learning is dependent on having enough data to draw statistical conclusions from, while AI is about emulating logical decision making of humans.
As a simple example, take search engines. They use machine learning to understand how to display and rank content the user may be interested in. Platforms such as Netflix and Amazon use machine learning to offer recommendations. Also, the finance industry relies on machine learning to carry out credit scoring, identify fraud and analyse trends in the stock market.
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