Over the past few years, artificial intelligence has experienced tremendous growth. Much of that has to do with the wide availability of GPUs that make parallel processing faster, cheaper, and more powerful than ever before. It also has to do with the simultaneous one-two punch of practically infinite storage and a flood of data of every stripe (that whole Big Data movement) – images, text, transactions, mapping data, you name it.

The latest advancements in artificial intelligence can be overwhelming to understand, but what it all boils down to is two concepts that you most likely heard a lot about: machine learning and deep learning. These words are thrown around so much nowadays that they may appear to be interchangeable buzzwords, which is why it is important that you know the difference between the two.

Examples of both machine learning and deep learning can be found pretty much anywhere. This is how Netflix knows which movie to recommend to you, how Facebook knows whose face is in a particular photo. So what are these two terms that dominate the discussion regarding artificial intelligence and how do they differ? Let’s take a look.

What is Machine Learning?

Machine learning is a combination of algorithms that parse through data and then apply what they have learned to make an informed decision. As we mentioned above, the reason Netflix knows which movie to recommend you next is because it uses machine learning algorithms to associate the listeners’ preferences with other viewers who have similar tastes.

Machine learning also powers many other automated tasks that span across multiple industries, everything from cybersecurity experts tracking down malware to stockbrokers looking for beneficial trades. They are designed to serve as virtual personal assistants and machine learning performs well in this regard. However, when delving down into it, machine learning is a lot of complex math and coding that also serve mechanical functions the same way that a car, flashlight or television does.

Machine learning came directly from the minds of the early AI crowd, and the algorithmic approaches over the years included decision tree learning, inductive logic programming, clustering, reinforcement learning, and Bayesian networks among others. As we know, none achieved the ultimate goal of General AI, and even Narrow AI was mostly out of reach with early machine learning approaches.

When something is capable of machine learning, it means that it is performing a function with the data that was put into it and gets progressively better and better at that particular function. Imagine walking into a dark room and saying “It’s dark in here” and your flashlight automatically turns on because it recognized the word “dark.” This way machines can learn new tricks and it gets really exciting when we take a look at deep learning.

Deep Learning vs. Machine Learning

Practically speaking, deep learning is a subset of machine learning meaning that technically it is machine learning and functions in a similar way. This is probably why the terms are often used interchangeably, but their functions are different. Even though basic machine learning models gradually get better and better at a particular function, they still require guidance. If a machine learning algorithm returns an inaccurate prediction, an engineer needs to intervene and make adjustments. However, with a deep learning model, algorithms can determine whether or not a prediction is correct without any outside help.

If we return to our flashlight example, such a flashlight could be programmed in such a way that turns on automatically when it senses the word “dark” and it may even be able to pick up phrases containing the word “dark” as well. If this flashlight had a deep learning model, it could determine that it needs to turn on when it senses additional cues such as “I can’t see anything” or “There’s no light in here.” A deep learning model is capable of learning via its own method of computing.

How Does Deep Learning Work?

A deep learning model is designed to continually analyze data with a structure similar to the way a human would draw such conclusions. In order to achieve this goal, deep learning uses a layered structure of algorithms called an artificial neural network (ANN) which was inspired by the biological network of the human brain. This allows machine intelligence to be much more capable than standard machine learning models. Even though it is difficult to ensure that a deep learning model makes correct conclusions, but when everything works correctly, it truly is a scientific marvel and a potential cornerstone of artificial intelligence.

For example, the Google AlphaGo which is a computer program that learned to play an abstract board game called Go, which requires sharp intellect and intuition. By playing against professional Go players, AlphaGo’s deep learning model learned to play at a level that has never been seen before in artificial intelligence, without ever being told when it should make a specific move. When AlphaGo defeated world renowned Go players it caused quite a buzz since it was now the gold standard in the game of Go.

As deep learning becomes even more refined, we will see even more advanced applications of artificial intelligence. Deep learning has enabled many practical applications of Machine Learning and by extension the overall field of AI. Deep Learning breaks down tasks in ways that make all kinds of machine assists seem possible, even likely. Driverless cars, better preventive health care, even better movie recommendations, are all here today or on the horizon. AI is the present and the future. With deep learning’s help, AI may even get to that science fiction state we’ve so long imagined.

Best,

Skywell Software team

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