What is machine learning?

Hand pointing at a hologram of icons depicting machine learning

Sep 1, 2022

Machine learning is a branch of artificial intelligence (AI) developed to use data and algorithms to mimic human learning. This specialised technology enables computers to autonomously predict and problem-solve in real time by learning from and improving upon previous experiences – much like humans do.

The concept of machine learning has been around for decades. The first successful attempt to computationally model a neuron took place back in 1943 by Mcculloch & Pitts, and it was followed by Alan Turing in 1950, who proposed the Turing test which could investigate if a machine had the abilities to think like a human. In 1958 Frank Rosenblatt was the first scientist who successfully demonstrated the perceptron. However, the term machine learning was popularised by Arthur Samuel in 1959 after developing a self-learning checkers game for an IBM computer. This groundbreaking work was some of the first in AI development, which quickly gained momentum.

Due to computational limitations, all these years AI and machine learning were applied mostly in theory. However, after the rapid development of technology, machine learning has been used widely in several applications lately.

Artificial intelligence is a data-driven science. Almost any task can be automated with machine learning. However, given that AI is a data-driven science, data must fulfil certain requirements for machine learning to be applied successfully. Hence, for example, quantity, quality, data patterns or classified set of rules play an important role in algorithm selection and are major contributors to the successful application of machine learning. The revolution of AI is changing the industrial landscape.

From risk assessment and medicine to marketing and sales, through implementing machine learning algorithms, companies are able to transform processes that were previously only possible for humans to perform.

According to Fortune Business Insights, the market for this in-demand technology is predicted to grow to more than 188 billion USD by 2029.

How does machine learning work?

A machine learns by looking for patterns amongst huge data sets, and when it identifies one, it adjusts to reflect the “truth” of what it has discovered. The more data the machine is exposed to, the more intelligent it becomes – growing, changing, adapting and developing new knowledge, much like a human brain.

When enough patterns are identified, the computer can begin to make predictions, identifying insightful information without needing to be told where to look. Mathematical functions such as loss and cost functions can be used to evaluate how well the algorithm models the data. Then through the optimisation process hyperparameters of machine learning algorithms will be adjusted in order to minimise the aforementioned mathematical loss and cost functions.

The optimisation process is of paramount importance because it contributes to minimising the error between the predicted and estimated values. This process changes slightly depending on the machine learning algorithms used.

What are the different types of machine learning?

There are three main types of machine learning, the first one considers the field of study which includes supervised, unsupervised and reinforcement learning. This includes the most popular learning problems which are most often encountered in real-life use cases. The second one considers more hybrid types of learning and includes semi-supervised and self-supervised. In the third field of study more broad techniques are included such as active, online and transfer learning.

Depending on the problem there are many types and categories of learning approaches as well as hybrid combinations and ensemble techniques. Each type of learning includes several types of algorithms or techniques. Under the three main types fall various other types of machine learning as well, such as multi-instance learning, inductive learning, deductive inference, transudative learning, active learning, online learning, and multi-task learning transfer learning. 

Supervised learning

Supervised learning is the most utilised machine learning mode amongst technology leaders – and it’s the most reflective of the way that humans learn. In supervised tasks – as the name suggests – the algorithms are fed information. 

The computer is presented with a collection of labelled data points which form a ‘training set’. This inputted information helps the computer find data patterns, form classifications, and learn to identify what the user is looking for in future.

Supervised learning is useful in a variety of business scenarios, including inventorying, sales forecasting, and detecting risks. 

Specific applications include:

  • Predicting housing prices
  • Detecting fraudulent transactions
  • Differentiating low from high-risk loan applicants

Unsupervised learning

In unsupervised learning, the training data is brand new – it’s unknown and unlabeled, meaning that no human eye has observed it before. In this sense, the machine learning algorithm mimics code-breaking. The machine learning algorithms cannot be directly guided by the input data – so the computer must learn to search for patterns by itself. 

Unsupervised learning is formed of:

  1. Clustering: Clustering involves grouping sets of similar data (based on defined criteria). It’s useful for segmenting data into several groups and performing analysis on each data set to find patterns.
  2. Dimension reduction: Dimension reduction reduces the number of variables being considered to find the exact information required.

Reinforcement learning

With reinforcement learning, the machine learns through a process of trial and error. By defining the rules, the machine learning algorithm then tries to explore different options and possibilities, monitoring and evaluating each result to determine which one is optimal. 

After interacting with the data sets over time, the computer can then make a measured decision on the best course of action to produce the desired result. An example would be the development of self-driving cars.

What are some examples of machine learning?

From aiding subatomic scientific discoveries to ‘suggesting’ social media content, autocorrecting text or sending spam emails to junk, applications of machine learning are vast, underpinning the everyday use of our smart devices while paving the way to ground-breaking global technological advancements.

There are a multitude of ways that machine learning manifests in daily life. Some recognisable examples include:

  • Recommended content: Machine learning helps brands better understand their consumers. From pushing products to personalising user experiences, machine learning is utilised by marketing and sales personnel in almost every field. Netflix watchlists, Amazon best-buys and suggested Facebook friends all rely on these algorithms to create ‘user profiles’, noting online behaviours such as likes, follows and purchases to 
  • Virtual assistants: Apple’s Siri, Amazon’s Alexa, and Google Now are all popular virtual assistants that we’ve integrated into our homes, workspaces – and even our classrooms. These devices are not only trained in voice recognition, but collect and refine data with every interaction – from charting your daily schedule and regulating your alarms to playing your preferred musical artists.

Machine learning is a remarkable technology, trained to detect more than the human eye – and it’s creating real-world impact. More prolific examples of the ways AI application is transforming industries include:

  • Medical diagnosis: Machine learning has quickly infiltrated the medical profession, assisting providers in a variety of patient care, forming intelligent health systems and shaping diagnostic and decision-support tools, and has proven to be prevalent in disease diagnosis, drug discovery, and patient risk identification.
  • Image recognition: Everytime you unlock your iPhone or tag a friend in a Facebook image, you’re making use of machine learning, as the algorithm has been built to recognise faces. Everyday examples aside, facial recognition technology has been transformative in identifying potential threats or criminals, maintaining quality control within industrial production – and even finding missing persons.

Even in its earliest forms, machine learning has made a case for itself in the improvement of our daily lives – and if the forecasts are true, this fast-growing tech will only serve to make the world greener, safer, healthier, and more secure with every integration.

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