Deep Learning Applications in AI
Deep learning technology is one of most demanded IT trends as it stands behind numerous of innovations. Deep learning already is applicable to various spheres of life and business: customer services, marketing, operations, sales, and even governmental affairs. Let’s discover what are common applications of deep learning in AI and their influence on our lives.
The IT market is increasingly moving toward the so-called SaaS (software as a service). Services based on artificial intelligence technologies show rapid growth. In 2022, the global deep learning market reached a value of $12.67 billion. It is anticipated to expand from $17.60 billion in 2023 to a substantial $188.58 billion by 2030, showcasing a remarkable Compound Annual Growth Rate (CAGR) of 40.3% throughout the forecast period. What are the most incredible applications of Deep Learning? Let’s find it out!
What is Deep Learning
Deep learning plays a pivotal role in numerous artificial intelligence (AI) applications and services, enhancing the intelligence and automation of existing AI-driven products. It represents the segment of AI that excels in executing both analytical and physical tasks without human intervention.
In essence, deep learning constitutes an intricate facet of machine learning, imparting computers with the ability to emulate human-like responses. Whether it's autonomous vehicles, hands-free voice-activated devices, or voice recognition on phones, tablets, TVs, and smartwatches, deep learning serves as a driving force behind these groundbreaking innovations.
In the realm of deep learning, computers learn directly from data inputs, such as images, text, or sound. These models consistently achieve exceptional accuracy, often surpassing human capabilities. Deep learning models rely on vast datasets, demanding substantial computational power, and operate effectively by employing neural networks with multiple layers, reminiscent of the human brain's architecture.
Deep learning resides within the realm of machine learning, which, in turn, is a subset of artificial intelligence.
5 Fascinating Applications of Deep Learning
1. Removing the language barrier
Google Translate app now uses the Deep Learning technology for visual translation. How does it work? The application uses a deep neural network for text recognition when scanning a picture. In other words, the Deep Learning technology allows you to determine whether there are letters on the picture and then, when the letters are identified and words are recognized, the application translates the inscription from the pictures into your native language:
This innovation greatly facilitates tourists life! For example, it is easier to understand what kind of dishes are written in the menu. All you need — just scan the page and obtain the necessary information in real time. Google experts say that the app works really fast and does not overload the smartphone’s RAM.
2. Super search
The Deep Learning technology allows us to move from inscriptions recognition on the pictures even further — to analysis videos. Oxford Visual Geometry group has launched a service, based on neural networks, that allows searching for a relevant BBC news. The program helps you to find your desired videos by the keyword that has been appearing in the video, even many years ago.
An example of a search a story by the word «Hollywood»
3. Unlimited possibilities for work with images
A system based on Deep Learning provides plenty of opportunities for image processing, with their help it is possible to add effects (for example, to make photos to a picture in the style of famous artists), increase clarity, etc.
Interesting top applications of Deep Learning under the title Let there be color! for example, helps to give color to black and white photos and even videos. High-precision neural network calculates all image’s nuances and divides a picture into layers to determine the depth of the colors and transitions. As photo processing, the system trains and now can handle old photos and even videos:
4. A step closer to communicating with machines
In 2016, Google has released the WaveNet system based on deep neural networks, which can convert text to audio format. Unlike voice assistants like Siri, WaveNet allows you creating much more realistic sounding voices by sampling real human speech and modelling signals. WaveNet’s learning allowed the system to create the human voice that is close to real human speech and even music pleasing to the ear. The detailed learning process of the system can be found here.
5. Speech recognition
Using Deep Learning machines can not only speak but also understand what you are saying. A vivid example — LipNet system, that was developed using neural network technology by scientists at Oxford University. LipNet has become the world’s first system that can recognize lip-speech, and not just individual words, but whole sentences. For this, the system processes the video sequence, dividing it into a plurality of fragments and layers. The results are impressive:
The speech recognition technology gives a qualitative impetus to the development of medical technologies (for example, the creation of fundamentally new hearing aid) as well as protection systems — read lip-speech by watching the video from surveillance cameras.
Those are just a short list of opportunities offered by the Deep Learning technology. This innovation is the basis of self-driving cars, robotics and analytical systems.
Learn What researchers do to eliminate AI mistakes in the process of deep learning.
FAQs
What are the major areas where deep learning is being applied in AI?
Today, the applications of deep learning in artificial intelligence extend the boundaries of many software solutions and even robotics. Speaking about the major areas where deep learning is applied, we can’t but mention virtual assistants in multifaceted areas, natural language processing, image recognition, colorization of black and white images, entertainment, game playing, news aggregation, translation, etc. Fraud detection in the financial sphere, demographic and election predictions in politics, healthcare issues, and development delay in children prediction, self-driving cars are vital areas where AI and deep learning mechanisms are applied.
What are the challenges and limitations of deep learning in AI applications?
While the common applications of deep learning in artificial intelligence are more or less clear, challenges and limitations may be obscure, however, they exist. The main challenge is the need for large data amounts and computational resources. Since the neural networks learn only from observations, they only know the details included in the training information. More parameters will be needed if you need more accurate and powerful models. It may call for more data and also for increased hardware requirements. Neural networks can provide incorrect or misleading outputs, because they are exposed to subtle data perturbations or modifications, incapable of multitasking, and can’t adapt to fluctuating scenarios or diverse environments. One more challenge of deep learning is the lack of explainability and interpretability of the results and decisions.
How is deep learning being applied in the field of cybersecurity and malware detection?
Deep learning applications are not limited to the cases mentioned above. Deep learning well-trained models can effectively detect malware in the digital realm, classify new and previously unknown samples, and provide their in-depth analysis. Neural networks are useful for assessing API and system calls, network traffic analysis, deviations from normal software behavior detection, and identifying potentially harmful activities. So, deep learning algorithms help prevent intruders from accessing the systems, as well as detect and deal with spam and other forms of social engineering.
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