Machine Learning vs Human Learning
The process of learning is different for every person. Some people can learn quickly, while others need to put in more effort to memorize information. Over the years, humans have developed learning techniques that apply to machines.
Machine learning (ML) is the method for teaching machines to mimic human behavior. It is possible only because of advanced learning algorithms that can understand processes and mimic human behavior. But what’s the difference between human and machine learning? This article will explain that in more detail.
The process of learning goes through multiple stages. It revolves around observing things, finding patterns, explaining patterns with a theory, and then testing the theory to see if it matches the observations.
It all breaks down to recognizing a model based on a pattern that comes from multiple observations. If the model isn’t feasible, you can’t learn anything from it. Math, physics, chemistry, biology, and other sciences all work to identify and describe things that happen around us. That results in theories and natural laws we can learn and understand.
With that said, learning models don’t reflect the absolute truth. We learn only approximations of the things we observe around us. Humans have to keep upgrading the basic learning process to test out new observations and gain new insights.
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Computers and programming evolved over decades, and ML is a direct result of increasing computing power and advanced logic. Machine learning allows computers to automate tasks traditionally done only by humans.
ML models can learn how to translate speech, drive cars, manage heavy machinery, etc. Years of perfecting ML models and rising computing power have led to the expansion of artificial intelligence (AI) solutions capable of making accurate predictions about what happens in the real world.
With that said, ML is the process of teaching computers to identify patterns and make accurate predictions by analyzing large amounts of data. Most ML models today are advanced enough to recognize movies on images, identify speech accurately, pinpoint spam emails, and recognize patterns.
Unlike traditional software where humans have to code the information, ML models can learn from data without human help. But before an ML model can make predictions, it has to process massive amounts of data. There are multiple approaches to ML learning. The two main categories are supervised and unsupervised learning. Let’s see what that means.
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Supervised Learning Explained
Supervised learning is a process of teaching machines by example. ML models analyze massive volumes of data. That can be images, numbers, letters, etc. Once the system has enough examples, it will start grouping shapes, numbers, images, and other information to identify patterns. The process is long and requires a constant feed of data. Some models require millions of pieces of information before they are able to identify patterns.
Unsupervised Learning Explained
Unsupervised machine learning uses algorithms to find patterns in data. Once it analyzes huge amounts of data, it can identify similarities and segment the data into categories. Many businesses use this type of ML model to improve their online operations. For example, Google News uses ML to group articles by topics, while Airbnb uses ML to group apartments by location.
Even the most advanced unsupervised ML model can’t differentiate between different types of data. They can only identify patterns and group data by their similarities.
Application of Machine Learning
Machine learning systems are the backbone of the internet. Every time you get an Amazon product recommendation or a suggestion for a Netflix show to watch, an ML model is doing its job.
Google has one of the most advanced ML systems ever created. It combines multiple ML models to improve search result accuracy. Some models identify security threats, while others try to segment data and ensure that every search gets the right results.
Furthermore, all voice assistants such as Amazon’s Alexa, Apple’s Siri, and Google’s Assistant use machine learning to identify speech. They use a type of ML called natural language processing to recognize questions and provide answers.
However, machine learning solutions are finding their way into many different industries. Most businesses going through digital transformation utilize the power of ML and AI to improve their operations. There are many ML applications redefining business practices everywhere. That includes examples such as facial recognition cameras, use of customer support chatbots, practices such as predictive maintenance, etc.
Heavy industries already use advanced AI/ML solutions to increase production, boost safety, and improve existing processes. The Wizata industrial manufacturing software is an excellent example of how ML can help improve existing businesses and prepare them for the 21st century. ML applications will keep increasing in the near future, as machine learning becomes an integral part of most businesses everywhere.
The Difference Between Human Learning And Machine Learning
Now that we’ve explained how ML models work, let’s see how the process of learning is different between machines and humans.
Most people learn from trial and error, and machines are not much different. They also make errors while trying to solve problems. However, ML systems can store all analyzed data and use it to identify patterns at any time in the future. ML models have a generalized approach to data. That allows them to learn faster and find correlations between different information more efficiently than a human.
People, on the other hand, need classes and coaching to be able to solve problems. Even when they master problem-solving, they can only cope with difficulties they’ve seen in the past. People struggle to apply their knowledge to general issues accurately because they lack generalization.
Machine learning model development has reached its peak in the past few years. The growing need for advanced AI systems led to the creation of super-efficient ML models. New technologies such as NLP, increased computing power, and huge amounts of data work together to help ML mimic human behavior.
So many different factors have a direct impact on machine learning today. ML algorithms are becoming more and more complex, and ML models can now learn without human interaction.
Even though AI is close to mimicking human behavior, it is limited by the lack of logic. Human intelligence is still dominant, and AI is a long way from being able to copy real human behavior. People feel emotions that have a direct impact on the learning experience and creativity. Machines can’t feel, so they can’t invent anything original. They can only improve existing processes by big data analysis.
Machine learning has been around for decades, but it couldn’t advance due to the limited computing power in the past. However, as computers evolved, ML models became more accurate, eventually leading to the development of advanced AI systems that are currently redefining entire industries.
ML models are close to mimicking human intelligence, but they are still unable to copy human expertise. The technology will keep evolving, and it might surpass human capabilities in the future. The truth is that scientists still can’t figure out how a human brain works, and until they do, machines won’t be able to think exactly like humans.
Christopher is a digital marketing specialist at Digital Strategy One and a freelance blogger. He is focused on new web tech trends and digital voice distribution across different channels. In his free time Christopher plays drums and Magic: the Gathering.