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Present-Day Applications for Machine Learning

Machine learning (ML) is an artificial intelligence application that uses algorithms to find patterns in data. Typically, this involves a massive amount of data that other methods wouldn’t be well-suited for. Data sets, in this context, include everything for text, images, video content, and everything else. If it’s stored digitally, a machine learning algorithm can use it.

What makes machine learning particularly impressive is that the systems don’t require specific programming. They “learn” to find data patterns without any real dependence on humans, which is what makes ML so useful in the field of AI. While machines have yet to reach anything resembling true human intelligence outside of science fiction, artificial intelligence does currently have the ability to learn from examples, solve problems, and even manipulate data once patterns are found. This is thanks largely to machine learning and similar computation systems.

Machine learning isn’t just something that exists in lab experiments or in theory. In fact, it’s been around in some form since 1986 thanks to Geoffrey Hinton, often called the father of deep learning. Put simply, deep learning is now considered a subset of ML, and it utilizes what’s called the deep neural network (so-called because of the multiple layers for input and output in artificial neuron networks) to find and amplify patterns so the next level down the neural network can perform predictive tasks. Here are just a few ways machine learning is being used at this moment.

Search Engines

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Modern search engines use machine learning to better understand search queries and optimize search results pages. Updates to search engine algorithms, such as the BERT update, allow search engines to not only understand the keywords involved in a search query but also the intent behind the search. Improved natural language understanding means that more relevant results are relayed to the end-user.

It’s not just limited to search either. The recommendation systems in your favorite music and video streaming apps also take advantage of machine learning to determine the most relevant things to recommend to you. These are based both on patterns of previous searches and streams, so the system can determine what you’re likely to want next.

Image and Speech Recognition

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Perhaps the most common use for a machine learning model at present is in image recognition. Deep learning algorithms can be used to rapidly train programs to recognize images such as symbols, faces, vehicles, or practically anything else. This gives machines the ability to quickly read through extensive documents that would take humans many hours to complete. Machines can also quickly scan images for security purposes, such as facial recognition to enter a secure area.

Speech recognition is also extensively used in our digital assistants and has led to the rapid adoption of IoT (internet of things) technology. This technology lets digital devices of all kinds communicate with each other over the internet. Common home uses include smart security systems and smart fridges.

Education

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Machine learning is particularly useful in the education field as it can locate patterns in student’s work and help instructors create lessons that are better tailored to current students. It’s also led to great advancements in distance learning, something that’s become even more prevalent these days thanks to the pandemic.

Algorithms can help educational programs determine which categories students are mastering and which ones they’re having issues with so that study materials and future practice tests reflect current needs. This way, students don’t have to continue proving they know things they’re already comfortable with and will receive more instruction on the topics they actually need. Learning algorithms can even help update annual exams to include updated information and ensure questions on the tests are relevant.

This is simply scratching the surface of machine learning. It has applications in practically every field including finances, healthcare, marketing, and much more. The field of machine learning is an exciting one to follow, and there are sure to be more groundbreaking advancements each year.