Technology today is one of the most promising aspects of society. Innovations in new elements of the technology industry are constantly generating buzz in the public eye. As a result of this, investments in this billion-dollar industry are expanding increasingly. Consequently, deep learning is a fast-growing embodiment of the direction the industry is heading. The importance of deep learning as a part of today’s fast-growing tech industry is the consideration we will present in this article.
Meaning and Core Concepts of Deep Learning
Deep learning is a complicated topic that branches into many aspects of Artificial Intelligence. However, it is still very much distinct from the different parts of AI. How? Well, deep learning emphasizes unsupervised learning. In other words, deep learning uses machine learning algorithms that can practice and improve without constant help from a human. Deep learning achieves this through the use of Artificial Neural Networks (ANN). ANN’s mimic the way the biological brain processes information. But, deep learning does not wholly depend on ANN. However, significant aspects of deep learning algorithms take inspiration from ANN. Deep Learning uses layers to classify the raw input it receives. The order of the layers is hierarchical to distinguish the complex aspects of the data from the less complex.
For example, an image of a tree uploaded to be processed by the algorithm. First, the deep learning network may analyze the curves, edges, and lines in the image of the tree. The level one information passes on to level two. Following this, the network processes it into more defined lines that establish connections and create diagonals and slightly more complex shapes. At stage three, the system creates proper shapes like circles and rectangles within the image. Step four recognizes the leaves, stem, and bark of the tree. The process passes on to new stages until it acknowledges the image of a tree. In some cases, the type of tree is a part of the process.
Difference between Machine Learning and Deep Learning
Machine learning and deep learning possess some inherently distinguishing features that make them operate under separate names. Machine learning is programming an AI by teaching it how to identify and giving those identifications distinct categories. AI requires supervision at the early stages, but it provides excellent results over time. In comparison, deep learning is programming an AI by giving it the ability to construct to identify. In a nutshell, the image of a tree is identifiable when the Machine Learning AI has seen enough trees before. However, a deep learning AI understands what makes a tree a tree through its use of hierarchy.
Importance of Deep Learning
Well, how useful is deep learning? The importance of deep learning is easy to recognize for the average person. But, in the hands of researchers, the benefits are game-changing. Deep Learning is excellent because it is capable of proving to be very practical in real-world applications. Deep learning uses the earlier mentioned unsupervised learning, which boosts its benefits.
In comparison, a machine learning program depending on labels to process data requires those labels put in place from a person. For a person to label all this data, it requires a lot of energy and labor. This process is expensive and proves to be time-consuming. Deep learning can learn from structureless data. Researchers are, therefore making it valuable to practical applications in the real world.
Deep learning is relevant because it creates a new level of communication between humans and computers. It possesses essential functions that make machines work without dependence on a human. The tasks of deep learning enable it to remain in a learning process throughout its use. This development process is so that it will impact organizations and industries over time in undiscovered ways. It is also affecting the jobs available in ways that are debatable as advantages or disadvantages to the average person. Therefore, managers, owners, and employers will benefit from developments and innovations in deep learning. This knowledge helps them know where the market is heading.
For companies that specialize in a product or service, deep learning is useful in researching the market for the best strategies that provide opportunities for the business to grow successfully. Deep Learning also enables the algorithm to analyze large volumes of complex ‘big data’ successfully to deliver results that are more accurate than individual output.
Real-World Applications of Deep Learning
Deep learning can use Artificial Neural Networks to analyze and recognize speech. Automatic speech recognition is a benefit of deep learning that displays its capacity for innovation. A machine that understands human speech through the use of deep learning algorithms shows the extent of how powerful unspecialized machine learning is. In the present day, speech recognition software is available to consumers on a large scale. Everyone with a device that can access the internet can obtain speech recognition. Different tech companies have their voice recognition AI, and they are convincingly effective. At earlier stages of this tech, the software did not always understand a large variety of dialects and accents. In 2020, Apple Siri, Google Now, Microsoft Cortana, Amazon Alexa, and many more effectively recognize speech due to the advanced multi-second interval methods of deep learning.
Image recognition is another benefit of deep learning. As earlier explained, the tree example applies to other complex images. The use of image recognition in deep learning helps the network to recognize the subjects in pictures presented to it. Some deep learning algorithms even allow the AI to go beyond the layers needed to identify the subject. Instead, they allow the algorithm to add new layers based on other examples of the subject to create a new version of the image.
This software is identifiable in Faceapp, an AI software that uses deep learning to recognize an image. It then uses its extensive archive of images to make a well-educated guess of what the subject in the picture would look like — either old, young, with or without facial hair and many other features. The app isn’t perfect, and some flaws might be noticeable, however, it shows immense promise and fear of what deep learning can do.
Deep learning is now applicable to visual arts. It makes use of deep artificial neural networks to recognize and create artworks. Deep learning can understand the art style of a painting and can determine the period in which that painting is from. Deep learning is also able to create art on its own by making use of the previously stored samples of art and producing its unique visual artwork. Furthermore, deep learning is capable of Neural Style transfer, which is what enables it to adopt its art style. It can generate beautiful and appreciated images as a result of that.
Toxicology and Drug Discovery
The importance of deep learning is well highlighted in its high potential for success in the medical field through the use of its neural networks. Deep learning can run simulations on new drugs to determine what components might prove to be dangerous before real test subjects have the drug applied. This application is a game-changer because doctors and scientists can now test more experimental drugs at different levels through a deep learning network’s prediction features.
An example of this is in the AtomNet deep learning system. AtomNet uses a predictive structure-based rational drug design model for deep learning and drug testing. AtomNet is what has helped in battling multiple sclerosis and the Ebola virus by predicting the candidate’s biomolecules to know how to tackle the problem.
Video games also benefit from deep learning. The neural networks of deep learning can study the terrain. This capability makes it possible for deep learning to generate terrain needed for video game environments. Deep learning does this by using its compiled access to various environments all over the world to produce extremely accurate terrain. This feature saves developers a lot of hours. The environments created are incredibly realistic on advanced game engines. This application of deep learning also applies to fauna and nature in general. Because of this, many developers are expanding games to be larger than ever. Procedurally generated video games are improving significantly and becoming even more popular.
What does the future hold?
It is 2020, and deep learning is indeed proving to be useful to consumers. The benefits are numerous, and it promises a future when humanity can work simultaneously with machines to create and improve on our world. But, all is not gold, and deep learning is not entirely trusted worldwide. The potential for dangers is alarming in deep learning. The use of this technology is not secured, and the machines can sometimes convincingly deceive humans.
Recently, the deep fakes that surfaced on the internet are an example of this. Videos can now be produced using the image and likeness of an individual with the use of deep learning programs. The flaws are minimal, and this technology is already here. The access might not yet be prevalent. But, the wrong people with knowledge of its potential can prove to be catastrophic. But, as it stands, the good outweighs the bad, and the importance of deep learning continues to grow.