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Self-Learning AI Explained

Artificial intelligence (AI) has been adopted by more and more businesses over the years. According to a survey from PWC, 86% of respondents said they think AI will be mainstream tech in their businesses this year.

As AI has become more mainstream, the technology has also advanced. There are many different types of artificial intelligence. Most modern forms involve supervised learning, the process of teaching machines via large labeled training datasets. 

While this form of machine learning works great for some cases — particularly when  there’s a lot of data that can be clearly labeled — it doesn’t work for everything. That’s where unsupervised learning, or self-learning AI, comes in.

What Is Self-Learning AI?

Self-learning AI is artificial intelligence that can train itself using unlabeled data. On a high level, it works by analyzing a dataset and looking for patterns that it can draw conclusions from. It essentially learns to “fill in the blanks.” 

A recent Wired article compared it to teaching someone to speak another language in a structured educational setting versus immersing them in the language in real life. 

While a person who learns Spanish for five years in school might have a solid understanding of the language and how to use it, it takes much longer for the student to learn than a person who simply moves to Mexico for a few months. Self-learning AI is taking the concept of learning-by-doing and translating it to AI.

What Are the Benefits of Self-Learning AI?

Self-learning AI is especially useful when training a machine on a concept that does not have a lot of training data available. It can also come in handy for training computers on processes that researchers don’t know a lot about, making creating labeled training datasets difficult.

Many are calling self-learning AI the future of AI, partially because it can be done (in theory) much faster than supervised learning. 

If all AI learning was done under the careful watch of a machine learning engineer or data scientist meticulously creating datasets, advancements would move very slowly. With unsupervised learning, AI can move at a much quicker pace.

Another benefit of self-learning AI is that once a new skill is learned, it can be more easily transferred to other similar skills. When deep learning is done in a supervised way, the machine must start from nothing and add new actions to their skillset as they go. However, once the environment is changed, the skills don’t always transfer over so easily. 

What Is an Example of Self-Learning AI?

Cybersecurity is one of the top areas where self-learning AI is currently being used, since it is better than most people at identifying changes and patterns indicating a breach. 

Due to the fact that AI using unsupervised learning learns from the data environment instead of a specified dataset, it’s able to be on the lookout for more anomalies that human researchers might not even know about.

Your Career in Artificial Intelligence

If you’re interested in working on the cutting edge of technology, becoming an AI engineer is a great choice. According to Glassdoor, AI engineers make an average salary of $136,000 a year. The first step on your path to your dream role in artificial intelligence is to get some education. Check out Udacity’s AI Nanodegree program in the School of AI.

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Jennifer Shalamanov
Jennifer Shalamanov
Jennifer is a content writer at Udacity with over 10 years of content creation and marketing communications experience in the tech, e-commerce and online learning spaces. When she’s not working to inform, engage and inspire readers, she’s probably drinking too many lattes and scouring fashion blogs.