Machines Can Learn, But How?

Many experts predict that machine learning (which any companies are currently investing in significantly) will be responsible for the most important breakthroughs in history. That includes being more important than the industrial revolution or the introduction of electricity, the computer, or the internet. Only time will tell whether these predictions prove to be correct, but machine learning is undoubtedly advancing at a significant pace.

What is a Machine That Learns?

A standard machine is programmed to do a particular task while a machine that can learn is programmed to learn how to do it. This learning is achieved through data, so the quality of the machine is dependent on the data.

In an article on Datanami, authors Hui Li and Fiona McNeill explain that machines learn in four main ways:

·         Supervised learning - labelling the data that the machine uses to learn as well as defining the desired output.

·         Semi-supervised learning - this uses some data that is not labelled and some data that is labelled.

·         Unsupervised learning - the data used by machines that learn unsupervised is completely unlabelled. In this form of learning, the machine looks for patterns in the data.

·         Reinforcement learning - this is a trial and error method of learning. Machines that learn in this way will try a scenario, get feedback from its environment, then adapt its approach based on that feedback.

Machine Learning's Various Forms

Machine learning is a term used to describe a number of different types of technology, all of which learn in one or more of the ways listed above:

·         Artificial Intelligence - this is the most widely known form of machine learning, particularly among people not involved in the technology sector. In simple terms, an artificially intelligent machine will be indistinguishable from humans. For example, it will be able to find solutions to complex problems in the same way that we do.

·         Neural Networks - this is also known as deep learning. It has been around for many decades but recent advances in technology, including processing speeds, have increased its pace of development. Examples of the technology include image or speech recognition.

·         Natural Language Processing - machines that can learn usually receive data in a way that they understand. This means you can't simply talk to it like you would talk to a human. This is what natural language processing, along with cognitive computing, is trying to solve. This goes much deeper than speech recognition. Instead, it is about understanding context and the nuances of the we all naturally speak.

Advances are being made in all the areas above primarily because of the computational power that is now available to those in the industry. This makes it possible for machines to process and understand enormous amounts of data, leading to impressive, albeit early, results.

Here are the takeaways:

·         Machines learn in a variety of ways including supervised, semi-supervised, and unsupervised as well as through reinforcement learning

·         Machine learning covers a number of specific fields including natural language processing, deep learning, and AI

·         This is all possible now because of the computational power that is available

Big Data and related technologies – from data warehousing to analytics and business intelligence (BI) – are transforming the business world. Big Data is not simply big: Gartner defines it as "high-volume, high-velocity and high-variety information assets." Managing these assets to generate the fourth "V" – value – is a challenge. Many excellent solutions are on the market, but they must be matched to specific needs. At GRT Corporation our focus is on providing value to the business customer.