What Exactly Is Deep Learning & How Does It Differ From AI?

deep-learningYou’ve probably heard of the term artificial intelligence (AI). You may also have heard of machine learning (MI). However, the notion of deep learning is something less talked about and understood, even when it may actually represent the biggest technological breakthrough for our time.

This lack of knowledge by many makes it a perfect time to consider developing software using deep learning algorithms to stay ahead of the competition. With the help of IT outsource services that have the knowledge and experience with deep learning development, you can make a huge advancement before everyone does and leverage its benefits from the get-go.

But before getting to that, you have to truly understand what’s deep learning all about.

The problem of unstructured data

Deep learning is a technology that mimics aspects of the human brain to process large amounts of data and find patterns, issues, and insights. It is a subset of machine learning, which itself is considered a subset of AI.

The reason that deep learning has so much potential to influence software and even future product designs relates to data. There is a massive amount of data out there for the taking that is not easy to delve into, since it takes a lot of time to even process it, let alone get insights out of it.

This data is called unstructured data, which basically is information that can’t easily be put in rows and columns on a spreadsheet or easily analyzed. Structured data, on the other hand, can quickly be defined and logged to be used in data sets for analytical purposes.

Examples of unstructured data are social media postings (like Twitter feeds), eCommerce information (such as sales and leads), and videos. Finding patterns in this form of data is very time consuming and next to impossible to log. It’s random in nature and comes from a wide range of sources, which is why is so difficult to manage.

The other aspect that makes it difficult for humans to log and analyze this amount of unstructured information (80% of all data!) is that it’s incredibly vast and constantly changing. It would not make sense to have an army of employees looking at random social media feeds to find patterns or actionable insights on a daily basis.

This is where the idea of using computing systems and algorithms to evaluate this data comes in. Thus, deep learning makes its entrance. But deep learning is not just algorithms used to make sense of unstructured data. Deep learning consists of a neural network that functions like a brain and can do a lot of other stuff.

Neural Networks

If you know anything about how the human brain works, you know that it’s an incredibly complex mechanism. You also may realize that humans are just beginning to comprehend its functions and understand how it sends signals to other parts of the body through neurons.

Deep learning takes this concept to the digital age. The reason this system of neural networks is called “deep learning” is due to the way data is analyzed through layering. Various layers are used to extract information from the data being analyzed at progressively higher levels from raw inputs of the data. Hence why it’s called “deep.”

In order to explain how this works, let’s take a look at image processing or X-ray images for example. The lower layers within the neural network have closer access to the raw image data and may only identify image edges. The higher layers farther away identify more complex items within the images like bones or breaks shown on the scan. As more layers are added, the image is analyzed to a greater extent.

The deep learning algorithm is also able to learn over time which data features to analyze in each level. Insight is then continuously gathered from raw data.

How Deep Learning Diverges from Machine Learning

Like previously mentioned, deep learning is an aspect of machine learning that is much more focused on making sense of unstructured data. However, it’s also used for purposes similar to those of machine learning.

Like machine learning, deep learning algorithms allow for the system to learn from past experience or to adapt to new conditions. As the algorithms gain knowledge and experience with more data to analyze, the algorithms themselves improve without human intervention.

It may be difficult to describe how this works, but we can use a voice assistant like Apple’s Siri to explain the concept. As Siri listens to you, it will improve its understanding about the way you ask questions and how you use language. The AI at its core will start refining the results it gives you according to what it learns from each interaction. Thus, the suggestions and actions it takes on your behalf will get more precise and sophisticated over time.

Siri and other voice assistants make a great use of machine learning and this includes deep learning benefits. Deep learning is used within voice bots like Siri to analyze and find patterns in the vast quantities of data gathered from users. As a result of such data and insight, tasks like voice recognition and understanding accents can be improved over time.

As you may recall if you follow big tech, Apple and other companies have been found to store and analyze voice data as it was being recorded. Employees would sit and listen to conversations to analyze data, raising privacy concerns amongst users. Deep learning algorithms, on the contrary, look at the data to improve the product without the prejudice of humans.

Solving “Unsolvable” Tasks

Deep learning harnesses the power of multiple nodes working alongside one another as a neural network, that’s why it has huge potential to solve decade-long questions across various industries. Healthcare, in particular, can greatly benefit from it.

According to Umetrics, “In industries like Pharma and Biopharma, deep learning can help all the way from understanding how cells work using live-cell imaging to monitoring manufacturing using audio.”

However, healthcare is only one area where deep learning is making its mark. Computer vision, speech recognition, natural language processing, social network filtering, translation, bioinformatics, drug design, and medical image analysis are some other areas that can benefit from deep learning.

One example is the solar panel design. Solar panel power systems have been improved thanks to the information deep learning algorithms provided about power output efficiency. Another example is the cooling system in data centers. Deep learning has been used to improve the cooling efficiency in data centers to achieve a 30% reduction in costs.

Radiology is also a practice that is truly benefitting from deep learning technology. Analyzing images of fractures or other human ailments through the use of X-rays is no easy task. Finding patterns in them can be hard, too. Image analysis is a task where deep learning can truly shine.

These are just some of the examples where deep learning has left its mark. However, the potential is endless as unstructured data is all around us.

Conclusion

Deep learning is a technology with immense potential to change industries from healthcare to biotech in the coming future. Today, it’s just the beginning, but deep learning will continue to improve and its influence will be seen in future answers to decade-long questions like how to solve chronic diseases or provide better mental health treatment.

Now as the technology is still nascent and not every competitor has jumped on its bandwagon, it’s a great time for companies to start exploring deep learning technologies.