8 Examples of Deep Learning and Why It Matters

Ah, machine learning. The future of our world and the technology that will help employees in factories and stores around the globe. Of course, we also can’t ignore the type of machine learning that will make our lives a little bit easier at home, too. With so many new advances everyday, it can be hard to keep up with all the different terms and definitions that surround the technology.

For example, you may have heard the phrase ‘deep learning’ recently. For those who don’t know what it means, keep reading. Not only will we look at the definition of deep learning, you’ll learn how it works, why it matters, and get a few examples.

What’s Deep Learning?

Let’s start at the very beginning by saying that artificial intelligence (AI) refers to using data and technology to complete tasks that would traditionally require human intelligence. Under this bracket, we have machine learning, and this is where machines learn from experience over time before then making recommendations and helping based off this acquired knowledge.

If we go one step further then, we find deep learning, and this is a technique under the machine learning umbrella. Essentially, the idea is to learn by example and to get computers to act in a way that’s natural to humans. We’re going to look at some examples later in the guide, but one that has captured attention in recent years has been driverless cars.

Using nothing but technology, companies are attempting to create machines that can differentiate between a lamppost and a human, read stop signs, and understand the road just as much as a human. As technology improves and more companies invest in the field, the advancements are quite incredible. In fact, you’ve probably already benefited from this investment. Have you controlled a device with your voice? If you’ve given a command to a TV, phone, tablet, or any other device, you’ve used deep learning.

As well as sound, computers using deep learning will also complete tasks based on text and images. Ultimately, some experts believe that deep learning models will exceed the performance of humans while improving accuracy in every possible way.

If we can get technical for a moment, you might be wondering how such devices actually work. Well, essentially, they’re trained. By using neural network architectures (with lots of layers) and labeled data, devices get close to human intelligence and act in a way that seems natural to people.wai

Why Does Deep Learning Matter?

If you’re wondering why all of this actually matters and if deep learning is here for the long haul, the most important component to keep in mind is accuracy. We’re on a constant path of progression with technology, and this means that deep learning has a stronger recognition accuracy now than it has ever had in the past. Consumer devices are able to meet the needs of the user time after time. With products like driverless cars, it’s all about meeting the safety criteria and fulfilling its purpose on the road.

Interestingly, deep learning actually has a history that goes back to the 1980s, but it’s gaining traction in 2019 because of two main reasons:

• Computing Power — Although we knew about deep learning many years ago, we couldn’t do anything about it because we didn’t have the technology. For deep learning, we need high-performance GPUs with a parallel architecture. These days, it can be combined with cloud computing or clusters to make training more efficient. Rather than weeks or even days, training only requires a number of hours.
• Labeled Data — Deep learning also needs labeled data in large quantities. If we continue with our example of the driverless car, for it to be successful the development will need thousands of video hours and millions of images. In the past, this just wasn’t an achievable target. Now, it’s a different story.

Examples of Deep Learning

We know all about deep learning, and we’ve learned why it matters, so all that’s left is to show some examples. We won’t be able to cover them all here today, but we hope to provide enough so that you understand its wide and varied use. Over the years, deep learning has been introduced as a result of industrial automation and has found a home in products and services ranging from smartphones to medical devices.

Driverless Vehicles

Since we’ve mentioned it throughout this guide, it makes sense to start with driverless vehicles. For driverless vehicles to be safe on the road, they need to react to the changing environment around them. For example, they should stop when a child runs into the road and react when another vehicle acts in an unexpected way. Of course, this all comes with deep learning algorithms.

The more videos and images used, the better positioned the vehicle will be to react to what’s happening in front of them. As the algorithms get more and more data, they can act just like a human in terms of processing information and reacting quickly. For manufacturers and investors, they hope this processing will be faster than a human which will actually make the road a safer place in the long-term.

Virtual Assistants

Next up, you’ve probably got either Cortana, Alexa, or Siri in your home (most homes do these days) and it relies on deep learning. With the likes of Google and Apple, deep learning is required to understand our speech. If you’ve ever wondered how Siri knows how to respond to our requests, now you know — it’s all thanks to deep learning.

Chatbots

Along with service bots, these tools are used to help customers more effectively and efficiently. Rather than needing a team of employees to talk with customers, and potentially holding a queue of people waiting, chatbots allow customers to ask questions and get intelligent, considered responses whenever necessary. As time goes on, we’re starting to see chatbots that can respond to both text-based and auditory questions.

Translations

The world is smaller than ever before; people are moving between countries and companies are offering their products and services to people all over the globe. While this is great news, the biggest roadblock is language. Wouldn’t it be easier if there was a universal language for everyone? Thankfully, deep learning algorithms can help in this regard because they automatically detect and translate from one language to another. Whether it’s a traveler needing directions or even government officials meeting to discuss important economic changes, deep learning can help.

Medical Research

Earlier, we mentioned that deep learning was being utilized in the medical industry and it’s currently playing a role in detecting cancer cells. At UCLA, cancer researchers were able to use deep learning in an advanced microscope. Using high-dimensional data, the microscope is able to accurately pick out cancer cells from other cells.

Aerospace and Defense

Did you know that deep learning is assisting with matters of national security? One use is with satellites that need to identify areas of interest. Deep learning may also have a purpose in the armed forces. Rather than taking unnecessary risks, deep learning will allow officers to identify not only safe zones but unsafe zones for troops.

Facial Recognition

In truth, facial recognition itself has a number of uses so deep learning is really providing value in this arena. For example, some companies are introducing facial recognition stations for their employees. Elsewhere, platforms like Facebook are using deep learning in this way to recognize faces in photographs. In the future, there could be scope for customers to complete orders and pay for items using facial recognition.

The problem? At the moment, the biggest problem for facial recognition is the ability to recognize faces and people even when they change. At the moment, a different hairstyle or a new beard almost renders the system useless when the initial image is a poor one.

Tailored Experiences

As our final example, another deep learning function we see most days is the recommendations made by services like Amazon and Netflix. They make recommendations based on our history, and many of us have found products, films, and TV shows we didn’t even know existed with the help of this method. As the technology matures in the years ahead, the recommendations feature will only get better.

Summary

Deep learning and machine learning are allowing machines to match human intelligence, and it makes for an exciting future. While some uses are helping to protect our troops, others are helping in cancer research. Meanwhile, on a consumer level, we’re pushing closer to driverless cars and the perfect consumer experience. In the years ahead, it will be interesting to see where else deep learning goes.