- Machine learning teaches computer systems to use algorithms to learn and adapt without explicit instructions by analyzing data patterns.
- There are four categories of machine learning: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.
- Machine learning is used in various industries such as eCommerce, virtual assistants, traffic pattern predictions, and fraud detection.
How does a bank or other financial institution detect fraud without engaging an investigating agent? And how do Amazon and other online retailers decide what you’re most likely to be shopping for and make buying suggestions? How has it become almost second nature to have machine voice assistants attend to you when you call your manufacturer or service provider? These, among other results, are evidence of machine learning (ML) at work.
Thanks to ML, many tasks that were formerly done by humans are now done by machines. There are ethical questions and concerns about machines in the workplace and job losses. However, it’s crucial to acknowledge that these tools excel at tasks beyond human ability. Machine learning does not only make businesses more productive but they help organizations obtain insights that would otherwise be impossible.
Read on to find out what machine learning is and how it’s used in the real world.
Machine Learning: An Exact Definition
Machine learning is the teaching of computer systems to use algorithms to learn and adapt without using explicit instructions by analyzing and drawing inferences from data patterns.
A Complete Explanation of Machine Learning
Machine learning is a form of artificial intelligence (AI) that is used to train machines to imitate human behavior. Human beings learn from past experiences and, using what they already know, they can improve on those experiences. In the same way, machines can be taught to learn from past experiences. The machines will consequently explore data and identify patterns that can then be used for decision-making. With ML, little or no human intervention is necessary.
When a business utilizes ML, it can accomplish almost any task as long as it teaches the machine using sufficient accurate data. ML enables companies to automate operations previously performed by humans. The kinds of tasks that trained machines can perform are innumerable. Think about answering customer queries, performing bookkeeping services, analyzing customer sentiment on social media, and much more. As long as a task can be completed using known data patterns, that task can be automated using ML. This is possible because machines are actually imitating intelligent human actions and, with time, the machines acquire the ability to handle huge amounts of data.
There are four categories of machine learning — supervised learning, unsupervised learning, semi-supervised learning, and reinforced learning. Let’s break them down in detail below.
Supervised Machine Learning
Supervised ML is also known as teaching by example. With this learning model, weighted data is used to aid an algorithm to learn. The data used for teaching the algorithm is pre-sorted and categorized using a set of characteristics. By learning the basis on which data is categorized, the machine will in the future, have the ability to sort raw data that hasn’t been tagged or pre-sorted.
Unsupervised Machine Learning
While supervised learning uses pre-sorted and tagged data, unsupervised machine learning makes use of algorithms to sort and cluster data that are unlabelled and unstructured. In real life, there are situations where there are no clear distinctions between different categories of data.
If, for example, you’re shopping for a TV at an online store like Amazon, it’s possible that you might also desire a TV stand or soundbar. The online retailer’s recommendation of additional items for TV shoppers is based on unsupervised ML. It predicts what you’re likely to buy along with the chosen product.
Semi-Supervised Machine Learning
Semi-supervised learning uses a combination of supervised and unsupervised techniques. With this machine learning model, the machine is taught using some labeled data and some data that are unlabeled. Labeled data sets an example and improves the algorithm’s accuracy through learned techniques from the structured data.
With reinforcement ML, an algorithm is required to make complex decisions as it operates with uncertainty. While the programmers create the rules, they don’t offer suggestions on the course of action that the machine should take. It’s up to the machine to come up with an optimal solution to a problem. With this learning model, the machine is learning through trial and error and the programmer assists by either reinforcing or discouraging the machine’s choices.
The History of Machine Learning
Artificial intelligence and machine learning are buzzwords today, and you might get the impression that these are recent developments. Yet, you may be surprised to learn that the concept of machine learning goes back almost a century and that what we know of the concept today is a distillation of many years of research. Since the 1940s, machine learning has benefitted from the contributions of many scholars and, noting the stage at which we are today, many more changes are expected in the future.
The term “machine learning” was coined in 1959 by an IBM employee, Arthur Samuel. During this period, another phrase that was used for ML, and which captured the essence of the technology, was “self-teaching computers.” But even before Arthur Samuel’s work, various attempts had been made by mathematicians to understand how human activity relates to neural behavior. Of special significance are the attempts by Alan Turing, a British scientist who is widely considered the founding father of artificial intelligence and modern cognitive science. One of Turing’s greatest contributions to machine learning is the Turing Test, a test that was supposed to find out if a computer had any real intelligence.
The Emergence of Machine Learning Programs
In 1952, American computer scientist, Arthur Samuel, created the first computer learning program. The program was based on the checkers game and it emerged that the IBM computer used for this program performed better with each subsequent game. What this proved was that the computer was studying the moves that created a winning strategy and incorporating those moves into its program.
While there were immense advances henceforth, evidence that machines can be taught and that they’re capable of thinking came in the form of an unprecedented event in 1997. An IBM computer called Deep Blue beat the then-reigning chess world champion, Gary Kasparov. This event proved that machines can handle complex calculations in scientific fields, surprising skeptics.
Tech giants such as Google, Microsoft, and Facebook have been at the forefront of machine learning and their efforts have produced revolutionary results. Currently, one of the greatest inventions from machine learning is OpenAI’s ChatGPT, a natural language processing algorithm that just needs a few prompts to generate texts like a human.
How Does Machine Learning Work?
A machine learning system is forever evolving, which means that a computer gets better at performing tasks over time. The system could produce three possible outcomes. First, the machine learning system could use the input data to produce descriptive results. Such results are historical and basically tell us what happened. Based on the input data and by studying patterns, a machine learning system could also predict what is likely to happen in the future. Finally, a properly working machine learning system could prescribe what it considers the best solution to solve a problem.
For a machine learning system to operate optimally, it’s crucial that it is taught with as much data as possible. ML is highly efficient due to its ability to handle vast amounts of data, far beyond human capabilities.
As a subfield of artificial intelligence, machine learning is related to other AI sub-fields like deep learning and neural networks.
What Are the Applications of Machine Learning?
Machine learning is used widely in a vast number of industries. In fact, it’s hard to think of an industry that can’t harness the awesome capabilities of ML. Below are some of the industries that have found machine learning indispensable.
In an age when the search for goods and services begins online before we visit a physical store (if we even have to), eCommerce sites rely on ML to get in touch with customers. Using machine learning tools, stores track customer behavior based on their browsing and buying history. This information helps them suggest relevant products or services that the customer might need.
When you contact Apple with a query, you will most probably be served by their virtual assistant, Siri. Other companies including Amazon have such assistants, and they serve customers by gathering immense amounts of data. They can give a helpful answer based on the history of similar inquiries that have been made in the past. Today’s virtual assistants have a full array of machine-learning tools to help them serve customers with ease. These assistants are equipped to recognize speech and can also engage in speech-to-text conversations.
Traffic Pattern Predictions
Intelligent transport systems (ITS) rely on machine learning to accurately predict traffic flow in a certain location. The best example of the use of ML to predict traffic is Google Maps. If you need to know the kind of traffic to expect on the road you’re using, entering your location on Google Maps gives you both the current state of traffic and also predicts how the situation could change later.
When a financial institution or law enforcement authorities want to catch fraudulent characters, machine learning could be their biggest ally. Machine learning could catch fraudsters by alerting the concerned authorities about unusual transactions. Moreover, since machine learning could use other capabilities such as face recognition, image recognition, and speech recognition, it can positively identify a fraudulent character.
Machine learning is used in a host of other industries, including search engines. It is also widely used by email service providers.
Examples of Machine Learning in the Real World
When you consider how machine learning is used today, it’s hard to imagine how some critical industries ever survived without it in the past. Let’s look at some practical examples where ML is now mandatory.
Health facilities and research institutions have vast amounts of data to handle. In a hospital setting, the personnel needs to schedule visits, prepare patient treatment schedules, and maintain complete patient medical histories. When you consider that a health facility could be handling thousands of patients in a year, there is no effective way to serve them without using ML.
Medical research heavily relies on machine learning. Institutions involved in cancer research, for instance, have to deal with complicated datasets which cannot be analyzed without the use of machine-learning techniques. Machine learning techniques are also required in the development of drugs. Testing pharmaceutical products, which typically consist of numerous ingredients, would be costly and time-consuming without the assistance of machines.
The Entertainment Industry
How does Netflix know the movie suggestions to send you? The modern consumer has so many choices that they might be overwhelmed when choosing from streaming services. Providers of entertainment services use ML to send personalized recommendations based on your past activities.
Almost every modern business wants to find out what people are saying about it on social media. The likes, mentions and reposts about a business give such companies insights that would cost tons of money in research. By using ML, companies access priceless and vast amounts of data that help in improving customer service. This helps them know the kind of improvements their customers are looking for.
Machine learning is widely used in other industries including translation services, spam detection, and self-driving cars.
While ML is of critical importance to the world, what we must never forget is that machines are trained by human beings. If there’s no oversight, trainers may transfer their biases to the machines, as humans are prone to errors and biases.
For an organization, this can lead to the production of data that exhibits discriminative tendencies based on race, sex, religion, or sexual orientation. While progressive organizations have much to reap from ML, any program will only be useful and bias-free when it incorporates input from people of varied backgrounds.
|Supervised Machine Learning
|Uses pre-sorted and categorized data to teach algorithms
|Unsupervised Machine Learning
|Uses algorithms to sort and cluster unlabelled and unstructured data
|Semi-Supervised Machine Learning
|Combines supervised and unsupervised techniques, using both labeled and unlabeled data
|Algorithm learns through trial and error, with programmer reinforcing or discouraging choices
The image featured at the top of this post is ©Laurent T/Shutterstock.com.