What you need to know about Machine Learning

What you need to know about Machine Learning

Machine learning is one of the most common types of AI in development for business purposes today. Machine learning is primarily used to process large amounts of data quickly. These types of AIs are algorithms that appear to “learn” over time, according to Business News Daily. 

If you feed a machine-learning algorithm more data, its modelling should improve. Machine learning helps put vast troves of data – increasingly captured by connected devices and the Internet of Things – into a digestible context for humans.

Machine learning can rapidly analyze the data as it comes in, identifying patterns and anomalies. Suppose a machine in the manufacturing plant is working at a reduced capacity. In that case, a machine-learning algorithm can catch it and notify decision-makers that it’s time to dispatch a preventive maintenance team.

But machine learning is also a relatively broad category. The development of artificial neural networks – an interconnected web of artificial intelligence “nodes” – has given rise to what is known as deep learning.

How machine learning works

Based on Venture Beat, machine learning enables a computer to “think” without being externally programmed. Instead of programming it by hand to accomplish specific tasks, as is the case with traditional computers, machine learning allows you to provide data instead and describe what you want the program to do.

The computer trains itself with that data and then uses algorithms to accomplish your desired task. It also collects more data, getting “smarter” over time. A crucial part of how this all works is the data labelling. If you want a program to sort photos of ice cream and pepperoni pizza, for example, you first need to label some images to give the algorithm an idea of what ice cream and pepperoni pizza each look like.

This labelling is also a critical difference between machine learning and a popular subset within the field, called deep learning. Deep learning doesn’t require any labelling, instead relying on neural networks, which are inspired by the human brain both in structure and name. Using this technique, you must provide a significantly more extensive set of photos to sort the images of ice cream and pepperoni pizza. The computer then puts the pictures through several layers of processing — which make up the neural network — to distinguish the ice cream from the pepperoni pizza one step at a time. Earlier layers look at basic properties like lines or edges between the light and dark parts of the images, while subsequent layers identify more complex features like shapes or even faces.

Machine learning and its subsets are helpful for a wide range of problems, tasks, and applications. Computer vision allows computers to “see” and make sense of images and videos. Additionally, natural language processing (NLP) is a rising part of machine learning, which allows computers to extract the meaning of the unstructured text. There’s also voice and speech recognition, which powers services like Amazon’s Alexa and Apple’s Siri and introduced many consumers to AI for the first time. 

 Across industries, enterprises use machine learning in their products and internally within their organizations. For example, machine learning can simplify, streamline, and enhance supply chain operations. It’s also widely used for business analytics, security, sales, and marketing. Machine learning has even been used to help fight COVID-19. Social media like Facebook relies on machine learning to take down harmful content while Google uses it to improve search. And American Express recently tapped NLP for its customer service chatbots and to run a predictive search capability inside its app.

Do you know any other applications of machine learning?

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