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As we continue to develop new technologies, we learn more about what computers are capable of doing. In fact, computers are becoming so advanced that we’re training them to act and think more as humans do. Computers are even “learning” how to function autonomously and can continuously improve on their own — all thanks to machine learning.
What Is Machine Learning?
Machine learning is the study of using algorithms and data that allow computers to perform tasks without instructions or input from human users. Different experts have created their own definitions to describe machine learning, but at its core, machine learning is characterized by computers performing autonomous improvement using real-world examples and data to do so, rather than a continual human input.
AI vs Machine Learning
At first glance, machine learning seems to have an almost interchangeable definition with “artificial intelligence” (AI). After all, Merriam-Webster defines AI as “a branch of computer science dealing with the simulation of intelligent behavior in computers; the capability of a machine to imitate intelligent human behavior.” However, upon closer inspection, it’s clear that these two terms refer to entirely distinct things.
AI encompasses many different processes and practices, including things like neural networks and image processing; machine learning is one of these subsets of AI. So while AI can take many different forms, such as a self-driving car or a digital assistant like Siri or Alexa, machine learning describes a particular aspect of AI function: computers learning autonomously.
How Do Computers Learn?
Put simply, a human user puts data into the computer, which then analyzes the data and looks for patterns in it. When the computer finds a pattern, it adjusts how it processes or manages data to reflect what it found. After the computer finds enough patterns, it can begin to make predictions. Generally, if a larger amount of training data is put in, the computer will become more accurate, faster.
In practice, machine learning is more complicated than that and there are two main forms: supervised and unsupervised learning. Each form requires large amounts of input data to train the machine learning algorithm, but they differ in how they interact with the data.
- Supervised learning: For this form of machine learning, the training data is categorized or labeled with the “correct” outcome. The computer is then given unlabeled information to process. It will compare the new data with the old and then determine the outcome based on the previous example. Supervised learning is especially well-suited to classifying items into different categories and regression when the output is a real value such as “dollars” or “pounds.” This is the most common form of machine learning and it’s generally more reliable than unsupervised learning.
- Unsupervised learning: In unsupervised learning, the data is not labeled before being put into the algorithm. The machine then attempts to find patterns in the data on its own. Unsupervised learning works particularly well when identifying similarities in groups and clustering them together, as well as identifying anomalies or abnormalities in data. It’s more difficult to measure the accuracy of an unsupervised learning algorithm since there are no training data to compare it to, but it can still provide valuable results and insights.
How Is Machine Learning Used?
Machine learning has a variety of applications in modern life. We’ve already found uses for it in industries ranging from healthcare to cybersecurity, and as this technology continues to develop, we’ll likely find many more. Other common uses of machine learning include:
Machine Learning in Marketing
Machine learning may prove to be especially useful in marketing because of its ability to identify patterns in data that humans might not otherwise notice. This can be particularly helpful when looking at user behavior; machine learning algorithms can analyze massive amounts of user data from multiple sources, such as social media pages and interactions with a website, to better determine how marketers and brands can engage with customers.
Among its many uses in marketing, machine learning can help marketers decide what ads to display to certain customers, identify the best time to send out individual offers or incentives, and even help improve the overall customer experience.
Search engines typically use algorithms to organize relevant results when users input a query. Machine learning is often used to update and refine these algorithms to improve the quality of results given to users. It can also be used to better understand searchers’ queries, classify users to make searches more personalized, and determine the best rate for crawling different websites or data sets.
Machine Learning in Weather Prediction and Climate Science
Machine learning also has uses in meteorology and climatology, as it can be used to analyze and predict weather patterns. Websites and apps that use APIs to scrape weather data are useful for consumers, but that kind of technology also empowers weather prediction by autonomous computer systems, providing them with ever more information. Further, machine learning can also play a role in obtaining and analyzing larger climate patterns, which can aid in the development of more accurate and detailed climate prediction models.
Machine Vision: Recognizing Text, Images, and Faces
A specialized type of machine learning, machine or computer vision is a computer’s ability to “see,” inspect and analyze images or videos. By analyzing images and converting visual elements into data, machine vision can recognize text in an image, identify faces, and even improve or generate images. High-powered computers aren’t the only devices capable of this kind of machine learning; using the right kind of API with optical character recognition, you can even turn your cell phone into an image reader, pull text out of physical images, even perform live translation of written text. Machine vision is being used in a variety of industries for a number of purposes, including social media and law enforcement.
Machine learning is an exciting new field in the world of AI, and though we’ve made great strides in developing this technology, it has many applications that have yet to be explored. As computer scientists continue to refine the capabilities of machine learning, we’ll determine even more ways in which it can be used, and it may become even more important to daily life than it already is.