How Does Machine Learning Learn?

You’ve heard the term ‘AI’. You’ve heard the phrase ‘Machine Learning’. But how do intelligent machines actually learn new things — and how do they become intelligent in the first place?

The ability to learn is an indispensable aspect of intelligence. Humans who are willing to keep learning are the ones that will really let their intelligence to advance. When we translate that power to machines, we’re giving computers the power to learn at a rate that requires less human interaction in order to maintain them. The learning aspect fuels intelligent processes for continuous growth.

Let’s outline some key differences between Artificial Intelligence (AI) and Machine Learning (ML) to get an idea of how machines become intelligent.

AI is the concept of machines being able to carry out tasks that we would consider ‘smart’. It’s defined as ‘the capability of a machine to imitate human behavior’. Here, we’re thinking of customer support voice recognition systems and computerized opponents in a game of chess.

ML is an application of AI, centered on the idea that we can give machines access to data and let them learn by themselves. It can understand and recognize patterns, and make predictions without the need for constant human administration. As models are exposed to new data, they are able to independently adapt. Fundamentally, it’s ML that fuels the condition of AI by cultivating data intelligence from the resources they’re given.

Machine Learning Principals

Machine Learning fundamentally works by building algorithms to allow computer technology to learn to perform tasks from data, rather than being explicitly programmed.  It’s built on algorithmic approaches such as inductive logic programming, clustering, and reinforcement learning. The developments in neural networks, designed to classify information just like a human brain does, can allow for breakthroughs.

Machine Learning is essentially made up of three major parts:

Model: the system that makes predictions or identifications.

Parameters: the signals used by a model to make decisions.

Learner: the system that adjusts to parameters, by looking at differences in predictions versus actual outcome.

This is just a broad scope of how the brains of Machine Learning actually operates. Now let’s have a look at the different categories of Machine Learning: supervised and unsupervised learning. Both are key to its development.

Supervised Learning

Supervised learning works using the process of an algorithm to learn from a training dataset, like a teacher supervising the learning process. It uses labeled examples, where the inputs and outputs are known from the start, but the machine is tested to see if it comes up with the same result. The learning algorithm part compares the correct output (from the teacher) with the resulted output (from the machine) to find any errors. Then, it modifies the model accordingly. Methods such as classification, regression, prediction, and gradient boosting will use supervised learning for patterns to predict the values of labels on supplementary unlabeled data.

Unsupervised Machine Learning

When people talk about computers learning to ‘teach themselves’, as opposed to humans having to teach them, they are often alluding to unsupervised learning. Here, there’s no training data set, and the outcomes are unchartered. Unlike supervised learning, there’s no training ‘teacher’ data set, so algorithms are left to their own devices to constantly discover and present new structures. The only thing that the machine has to guide it is input data, and binary logic mechanisms that all computers possess. These algorithms are used to segment text topics, recommend items, and identify data deviations.

What does this mean for business?

Ultimately, Machine Learning nourishes much of today’s automation abilities, making information, devices, and products more streamlined, and much easier for their employees and customers to use. Tasks within Machine Learning such as exploratory data analysis, model selection, and model production can be automated with Automated Machine Learning (AML) frameworks. As Hamel Husain, Data Scientist at Airbnb puts it:  ‘AML is a powerful set of techniques for faster data exploration as well as improving model accuracy through model tuning and better diagnostics.’

A simple example is the ‘Contact Us’ forms for company websites. Nowadays, instead of users having to select an issue and fill out endless fields, Machine Learning can examine a request and route it to the correct place. Apply that kind of example to other functions for everyday websites, and your customers are starting to interact and perceive your business in a much more user-friendly way – and they’ll enjoy the experience more, too.

On a wider scale, Machine Learning will create untold opportunities for digitized businesses. Oilfield operators can use Machine Learning to predict refinery failures and streamline distributions more economically. Ecommerce businesses can use Machine Learning capabilities to create intelligent AI chatbots, letting them take care of on-site chat sessions or even social media tweets and posts. The power lies simply in the ability for businesses to keep harnessing those opportunities by continuing to enculturate modern technology as part of their everyday practices.