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machine learning explained

Machine Learning Explained

Artificial intelligence is already all around us and machine learning itself has already improved our lives in many ways. It is influencing pretty much every sector, industry and field, and its influence is only going to increase.

Here we explain what machine learning is, how it is being applied in the real-world, and what to expect in the future.

The Basics of Machine Learning

Machine learning is a subfield of artificial intelligence (AI), though the two terms are often used interchangeably. This is because it is one of the most popular forms of AI, especially for use in business, industry, healthcare and even entertainment.

The very basics of this concept involve a computer that can learn without being directly programmed with the new information. The computer will learn from gathered data and previous actions to make predictions on future activity. Often the computer will also provide recommendations on an ideal action to take according to the data and analysis.

Real-World Examples

Machine learning is responsible for quite a lot of technological advancements that we already take for granted. We all either love or loathe the predictive text feature on our mobile phones, and this example lets you see it working right before your eyes. Type in a new word that isn’t in the built-in word inventory, and you will have to type out the whole thing, but only the first time (or first few times in some cases). Next time you start typing the same word, the predictive function will have learned it is a word you type and will thus suggest it in the predictive options.

Social media feeds also use basic machine learning to show you content it knows you like or – quite often – mistakenly thinks you like because you clicked on that one thing that one time. Streaming services like Netflix use a similar kind to suggest new shows to you based on the ones you’ve watched before.

Image recognition is another real-world form that is commonplace, with some x-ray machines able to discern if a patient has cancer or not.

Face recognition technology uses machine learning as well, especially in law enforcement. Police departments have a database of faces from the mug shots taken of those arrested, and the system uses machine learning to identify commonalities and match them to a face.

The Future of Machine Learning

Many of the benefits are already available for businesses and organizations to use, so the near future will mostly consist of these entities integrating the technology more and more. This includes using it to analyze a market microstructure for algorithmic trading, including analyzing large data sets and identifying real-time arbitrage opportunities.

Predictive analytics is another now and near future opportunity for machine learning, as is the extraction of valuable structured information from huge masses of unstructured data.

Further ahead, we can expect it to improve its capabilities, with there already being a lot of talk about ‘liquid learning’. This development will see the learning machines able to quickly adapt to changes in the data stream and environment all by themselves.

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