Machine learning is an application of AI and a field of study dedicated to building methods that ‘learn’ and subsequently can make predictions or decisions without being specifically programmed to do so. Historical data or mathematical models of data are used to help a computer learn without direct instructions and predict new output values.
The term was first introduced by Arthur Samuel, a computer scientist at IBM, in 1952, when he designed a computer programme to play checkers. Since then, machine learning has been used in a great mixture of areas. One of the most obvious and popular uses is Facebook with its recommendation engine that powers the newsfeed. If a member frequently reads a particular group’s news, the engine will start to show more of that group’s activity earlier in the feed. Should this pattern change, the feed will adjust accordingly.
Another one is product recommendations, which works in a similar way. By using AI and ML, websites track one’s behaviour based on previous purchases, search patterns and cart history and then make recommendations.
Machine learning has been extensively used in image and speech recognition. For example, Amazon Alexa uses speech recognition to record live voice, which is then sent over to Alexa Voice Services where it’s parsed into commands that it understands. It then sends the relevant output back to the device, which Alexa reproduces. And every time Alexa makes a mistake in interpreting requests, that data is used to make the system smarter the next time.
In image recognition the use of ML ranges from assigning a name to a photographed face (‘tagging’ on social media) to labelling an X-Ray as cancerous or not to the facial recognition system that can identify commonalities and match them to faces, which is used in law enforcement.
Machine learning is also used to automate the process of generating, storing, retrieving, and analysing data. It can extract meaningful insights from the data at high speeds through structured as well as unstructured data volumes. A practical application of this is the ability of Machine Learning to predict potential heart failure. An algorithm can scan a doctor’s e-notes, identify pattens in a patient’s cardiovascular history and make an analysis based on the available information.
After this brief look at practical applications of Machine Learning it’s clear that it has achieved a lot and there’s no way stopping the progress. The demand for it grows and ML, in turn, develops pace.
At Tencoins we specialize in ML and AI projects and are very excited to discover what lies ahead for us. And if you have a cool project in mind, drop us an email, let’s bring that future closer.
