Deep learning as the digital trend of the 2020s

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Digitalization is occurring at a rapid speed and it is only going to reach new heights in the 2020s. Artificial Intelligence (AI) continues to be one of the leading topics in the IT sphere. Voice and face recognition are just examples of the impact AI has made in our daily lives. As a result, society is now adjusting to the digital era and accepting technology as an integral part of life in the 21st century. The digital sector is growing, with more and more companies making the necessary shift towards a successful digital future. It comes as no surprise that one of the leading trends of the decade is set to be Deep Learning. Deep Learning is a function of AI that works in processing data, creating patterns and making decisions. It has evolved over the past decade and will continue to be one of the biggest digital trends in the 2020s. This article will explain the ins and outs of Deep Learning, explore its various applications and provide predictions for the future of artificial intelligence.

Understanding Deep Learning
Deep Learning is, first and foremost, a branch of Artificial Intelligence. It is based on imitating the way the human brain works. Deep Learning is considered part of a broad system of artificial neural networks and a class of machine learning algorithms. Another name for it is “deep neural network”. It is characterized by the use of multiple layers to extract higher-level features from a raw input. Deep Learning is an essential element of the digital revolution.

How Deep Learning works
The digital world holds an enormous amount of data, otherwise known as “big data”. Big data is collected from all types of websites, such as social media platforms, search engines, online shops, streaming services and more. The data is most commonly unstructured, which means that it would take years to manually extract relevant information from and comprehend. This is where Deep Learning comes in – it works by using AI techniques to learn from vast amounts of unstructured data. Its biggest advantage is that it makes the process of gathering and analyzing data extremely faster. Deep Learning models are typically based on artificial neural networks. The neurons in the neural networks are grouped into three types of layers: input layer, hidden layers and output layer. All of them have different functions – the input layer receives input data, the hidden layers perform mathematical computations and the output layers give out output data. It may look simple at first glance, but training a neural network is a very complicated process which requires a large data set.

Why it’s called Deep Learning
The term “deep” refers to the layers in the artificial neural networks. It is common for them to have over ten or in some cases, even over one hundred layers. The artificial neural networks are inspired by the processes in the human brain. Deep Learning dives into the layers of the ANNs and extracts relevant data. Generally, the deeper into the hidden layers you go, the more complex features are extracted. The process allows computers to learn and react to complex situations as human beings would, or in some cases even better.

Deep Learning vs. Machine Learning
Deep Learning is considered a subcategory of Machine Learning, but there are some key differences in the way they operate. The main thing that sets Deep Learning and Machine Learning apart is the fact that Machine Learning algorithms require structured data, whereas Deep Learning relies on artificial neural networks to extract information from unstructured data. Machine Learning is a method that involves learning by understanding labeled data which is used to produce more outputs with sets of data. If the latter are not exact, human intervention is required. Deep Learning, on the other hand, does not require any human intervention. The layers in the neural networks automatically extract data, learning from their own errors along the way. Deep Learning depends on the quality of the data to a large extent. If the data is not good enough, even the artificial neural networks are prone to making mistakes. Another point is that since Machine Learning requires labeled data, this method is unable to deal with huge amounts of data. Deep Learning neural networks can be applied on a larger scale compared to Machine Learning. While Machine Learning is suitable for simpler tasks and calculations, Deep Learning is intended for more complex operations. 
Both Deep Learning and Machine Learning revolve around the use of data, but the difference is that Deep Learning requires a much larger amount. So, generally Machine Learning can be used when data can be structured. Machine Learning can help automate business operations like marketing, advertizing, information gathering and more. Deep Learning should be preferred when there is a large amount of data. It can solve problems that are too complex for Machine Learning, but is generally more expensive and difficult to develop. 
A subdivision of Machine Learning, Deep Learning is the more intricate branch of artificial intelligence. It has the capacity to perform complicated functions and deal with large amounts of data. Deep Learning is an undeniable advantage in the corporate world, so it is no surprise that companies are integrating it into their systems. The artificial intelligence industry is developing at a rapid speed and the uses of Deep Learning are expanding.

The many applications of Deep Learning
Deep Learning can be observed in a number of fields. Chances are, even if you are not a digital native, that you have encountered Deep Learning while using modern technology. Examples of Deep Learning include, but are not limited to, translation applications, image recognition, speech recognition and digital assistants especially, recommendation engines, image enhancing and more. Deep Learning is used not only in IT, but across all industries.

Virtual Assistants
One of the first things that come to mind when you think of artificial intelligence are probably virtual assistants. They are one of the most popular and easily accessible applications of Deep Learning. Virtual assistants use Deep Learning to understand your commands by evaluating language. They gain information about your accent and voice and learn to provide a human-like interaction. Virtual assistants can reply to your questions, make appointments, take notes, respond to calls and learn about your preferences – from your favorite places to eat to your favorite songs. Deep Learning comes in handy when using virtual assistants, because they can coordinate tasks and send automatically generated emails. They use applications such as text generation and document summarization, and even assist in creating a reply to an email.

Face and Visual Recognition
What once seemed like a concept from a futuristic action movie, face scanning and recognition is now a fact. It has become widely accessible due to smart phones applying it as a security method, and not only that – online applications have the ability to recognize facial features in photos and identify people. Deep Learning comes in especially handy in this case, because it can sort hundreds of photos according to locations, faces, events and dates detected in photos. It is even possible to search for a particular photo through a large library by using visual recognition systems.

One of the most significant applications of Deep Learning is in medicine and healthcare. Because of the digital revolution, it has become possible to analyze genomes, develop new drugs, make medical imaging more easily accessible and predict potential health risks. Through Deep Learning and neural networks, diagnostics have become faster and more accurate, clinical research is enhanced, and healthcare costs have been reduced. Some of the goals of researches include finding cures for untreatable diseases, preventing shortages in qualified medical staff and standardizing pathology results and treatment courses by using artificial intelligence. Because of Deep Learning, it is now also possible to diagnose and treat developmental disorders such as autism, attention deficit and stuttering early. Researchers at the Computer Science and Artificial Intelligence Laboratory at MIT and Massachusetts General Hospital’s Institute of Health Professions have developed a computer system that can identify language and speech disorders at an early stage, which aids immensely to fighting them.

Film, Television and Photography
Thanks to Artificial Intelligence and Deep Learning, it is now possible to automatically colorize black-and-white images by taking grayscale files as input and producing colorized images as output. For such operations, high-quality convolutional neural networks in supervised layers are needed. Another application of Deep Learning comes in adding sound to silent movies. That is done through training a system with one thousand examples of videos with sound. The system then is able to synthesize sounds that match silent videos. Deep Learning uses neural networks to predict the best suited sound for the videos.

Self-Driving Cars
Autonomous driving is no longer a far-fetched concept thanks to Deep Learning. A system is able to build a model by receiving a million sets of data. The machines are then trained to learn and tested. Companies are developing driving assistances services as well as self-driving cars by developing complex algorithms. Potential self-driving cars are intended to be used not only to get from point A to point B, but also deliver food or goods. Self-driving cars are able to navigate by using 3D maps, but the goal of developers is to make them able to handle roads beyond those available on a pre-mapped system.

Fraud Detection
Detecting false information in news articles, or fraudulent transactions in banking and the financial sector is now also easy because of Deep Learning. It can be very hard to detect fake news, so using artificial intelligence to automatically filter the good from the bad has never been more important. Deep Learning operates on a sophisticated level to filter out news as per geographical, social, economical parameters along with the individual preferences of a reader. It can also automatically detect false or biased news and remove them from your feed. Deep Learning can help prevent credit card frauds and potentially save a lot of money for financial institutions. Fraud detection and prevention is done based on customer transactions and credit scores. Anything out of the ordinary is caught by the system and stopped.
Deep Learning is extremely useful in various fields. Some of its other applications include personalization, automatic machine translation, handwriting generation, advertizing, forecasting and even predicting earthquakes. The development and popularization of Deep Learning is by no means stopping. Along with digitalization, it is set to grow and reach even new heights in the new decade.

Deep Learning trends for 2020
Deep Learning has crept its way into our daily lives and will likely only become more popular. It is a critical advantage in the business world and a means through which a company can set itself apart from the rest. Businesses in 2020 are following some key AI trends which can give them a push forward in an increasingly competitive market.

Natural Language Processing
Natural Language Processing (NLP) deals with the interaction between computers and human languages. It is concerned with the way computers process and analyze large amounts of natural language data. Pre-trained language models have been at the center of events for a couple of years – they essentially create a black box which understands the language and are able to follow commands. They have made the application of NLP significantly faster, cheaper and easier. In 2020 and beyond, it is likely that linguistics will enhance Deep Learning by improving the interpretability of the data-driven approach in NLP systems. Pre-training and fine tuning have dominated NLP research and are continuing to develop. Neural machine translation is also on a rise, as developers are looking to improve the quality of simultaneous translations by optimizing neural network architectures.

Conversational Artificial Intelligence
Conversational AI is an efficient system that many businesses are adopting. Chatbots in particular bring a number of advantages for sales, marketing and customer service. The aim of developers is to improve chatbots by making interactions with customers more human-like. They are looking to improve dialog systems by tracking long-term aspects of a conversation, address the diversity of machine-generated responses, and train bots to recognize emotional responses.

Computer Vision
Computer vision systems have brought on many advantages for healthcare, security, transportation, retail, banking, agriculture, and more. Computer vision focuses on how computers understand digital images and videos. One of the main areas of computer vision research is currently 3D. Developers are working on generating depth maps and detecting 3D objects. Unsupervised learning methods are also gaining popularity. Stanford University has already been able to introduce object detection and recognition with unsupervised learning. Computer vision is also being combined with natural language processing.

Reinforcement Learning
Although not as popular as supervised and unsupervised learning, reinforcement learning is seen as a means of reaching Artificial General Intelligence. It is applied in fields where large amounts of simulated data are generated, like robotics and games. Some of the trending research topics in reinforcement learning include multi-agent reinforcement learning, off-policy evaluation and off-policy learning, and efficient exploration methods. Developers are looking to make reinforcement learning algorithms more sample-efficient and stable. 
Deep Learning has already influenced many sectors and improved the work process of millions. The fundamentals of industries have started to change, with companies adjusting to the new normal of the digital revolution. The question of what more could be achieved and how far artificial intelligence will go in the future inevitably arises. Although it cannot be said for certain, there are some indicators.

The future of Deep Learning
It is a fact that technology has started to exceed human performance in some sectors, such as medical diagnosis and image recognition. It is likely that artificial intelligence will continue to dominate the market. Businesses are predicted to become even more dependent on technology and Deep Learning algorithms specifically. Deep Learning will be able to make complex decisions on a broad range of topics. This will encourage businesses take more risks in regards of organization and structure. It is likely that AI departments will be opened, which will in turn increase the demand for qualified professionals on the job market. Small businesses and multinational corporations are predicted to benefit largely from Deep Learning, and apparently so will society. Artificial intelligence is likely to even be able to solve important global issues that humans cannot, such as global warming, for example. It has already exceeded our natural abilities in some areas, so the concept is not far-fetched. With Deep Learning, everything is possible.