Jul 5, 2018
Deep Learning vs. Machine Learning
Anyone who follows the latest trends in information technology has sensed the tech world is getting closer by the day to an incredible breakthrough. From smart devices and robots that can open doors and perform somersaults, to new technologies like deep learning, artificial intelligence and machine learning -- the tech world that many foresee occurring less than 10 years from now looks very different from the tech world we see today. To understand where technology is going in the near future, it is important to understand the nuances between terms like deep learning, machine learning and artificial intelligence. In this post, we will define these various terms to help increase the understanding of where tech giants such as Google, Apple and Microsoft are headed in the not so distant future.
Artificial Intelligence Defined
The terms "machine learning" and "artificial intelligence" are often used interchangeably, but they are not exactly the same. Artificial intelligence is a broader concept used to define the entire approach in which some type of machine in some form, is used to mimic human cognitive function(s).
Machine learning is actually a subset of artificial intelligence. Machine learning concepts basically center around the ability to create machines that use algorithms to receive and parse data, learn from that data and apply what they've learned in order to change their algorithms. As more data is received into the increasingly intelligent algorithms, the machines can solve more issues and present more solutions.
Deep learning is a subset of the broader application of machine learning. Deep learning is machine learning, but it works with much larger amounts of data and with training, can make its own decisions rather than relying on a human to correct an inaccurate prediction or outcome. For example, there are many smart devices that can currently report an error back to IT personnel, who then apply their knowledge to fix the issue. In the case of deep learning, a machine will attempt to go further by applying its own "brain" to arrive at a solution or positive outcome.
Data of course, is at the heart of all learning and currently machine and deep learning concepts require accurate, clean data in order to arrive at useful predictions and outcomes. The next logical step would be the creation of machines capable of learning how to properly evaluate and cleanse the data they receive as input. Given how far technology has advanced already, this certainly seems possible.
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