Deep Learning | How is this technology enhancing AI technology

Artificial intelligence (AI) isn’t a sci-fi trope any longer. Even if you don’t realize it, odds are good you regularly encounter AI in your day-to-day life; this includes deep learning. This technology has seemingly countless practical applications across a wide range of industries. Whether it’s analyzing your viewing habits to recommend Netflix titles or using voice app development that adjusts to your preferences, AI is becoming increasingly commonplace.

Several innovations are responsible for this surge in popularity. Deep learning is one of them. When you understand what deep learning is and how it drives sophisticated AI systems, you can better grasp how AI’s value and usefulness will continue to grow.


What You Need to Know About Deep Learning

AI is a general term referring to computer programs that can “think.” Machine learning is a subset of AI which refers to computers using data to more effectively complete key tasks.

Within this broad framework is deep learning. This subset of a subset focuses on using algorithms that train computers to perform high-level tasks that typically involve greater amounts of data than simple machine learning tasks handle. This involves exposing the AI’s neural net to large amounts of data. As it learns, it grows more effective at making predictions, allowing the system to perform certain tasks more efficiently.

Although deep learning can be applied to a wide range of AI functions, it’s particularly valuable when used in the following capacities.


Speech Recognition

Human language is complicated. A single word can be pronounced in many different ways and have different meanings in various contexts. Additionally, speech patterns and syntax choices tend to vary from one speaker to the next.

With deep learning, speech recognition systems learn to better understand what a person is stating or asking by becoming familiar with the nuances of language. This innovation also allows speech recognition programs to answer appropriately when a user asks a question.


Smart Homes

A smart home must learn to predict when certain appliances and features should be activated, and when they shouldn’t. Doing so allows the system to decrease energy usage, meet your daily needs, and reduce your bills.

For example, an ideal smart home would be able to predict when it should turn the lights in a room on or off based on your previous behaviour. It will also be able to adjust the temperature according to your typical needs.

Connected with Internet of Things devices, it could even one day learn to start prepping your morning coffee or set your alarm for you. To successfully perform these tasks, a smart home must use deep learning algorithms to properly glean insights from your behaviour.


Image Recognition

This is another AI feature you may have already benefited from in your daily life. It’s especially likely if you take many smartphone pictures and store them in the cloud.

AI programs often identify common traits and characteristics from photos to group them into different albums. A basic example would be a program that automatically sorts photos of people into an album specifically meant for these kinds of pictures.

Thanks to deep learning, however, image recognition programs will soon be capable of much more than simply helping you organize your photos. For example, deep learning can allow AI to identify commonalities in medical images like X-rays or MRIs.

This can help physicians more efficiently diagnose patients. In the near future, such a program could even help medical researchers better understand conditions by identifying similarities between images from patients diagnosed with them.


Looking to the Future

It’s clear that deep learning can be an extremely valuable tool. That said, there are a few reasons some businesses may be slow to embrace it.

Currently, deep learning programs require very strong hardware to be effective. The neural systems computers use to take advantage of and implement deep learning algorithms are typically very large.

Some in the field have begun to realize that creating these networks is easier with GPUs (Graphic Processing Units) instead of the traditional CPUs (Central Processing Units). Although GPUs were originally developed to support 3D games, they also offer the necessary computing power to facilitate deep learning.

Furthermore, it’s important to understand that deep learning is currently a highly specialized field. The average programmer doesn’t have the experience necessary to leverage it to its full potential. Thus, businesses need to take the time to research candidates thoroughly before selecting a programmer for a deep learning project.

Luckily, as is often the case with emerging technologies, deep learning will become more accessible in the near future. Tech giants like Google and Intel are already working hard to develop hardware that supports it.

That’s why it’s smart to get started with deep learning sooner rather than later. Businesses that take advantage of it now will find themselves on the forefront of the next major technological revolution.

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