Deep Learning Applications in Artificial Intelligence (AI): 5 types
Deep learning technology is one of the most demanded IT trends, standing behind numerous innovations. Deep learning already applies 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 is pivotal 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 autonomous vehicles, hands-free, voice-activated devices, or voice recognition on phones, tablets, TVs, and smartwatches, deep learning is a driving force behind these groundbreaking innovations.
In 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 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, Deep Learning technology allows you to determine whether there are letters in the picture. 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' lives! For example, it is easier to understand what kind of dishes are written on the menu. All you need is to 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 inscription recognition on pictures even further to video analysis. Oxford Visual Geometry group has launched a service based on neural networks that allows searching for relevant BBC news. The program helps you to find your desired videos using the keywords that appear in the video, even many years ago.
An example of a search for a story is 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, help to give color to black and white photos and even videos. A high-precision neural network calculates all image 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 released the WaveNet system based on deep neural networks, which can convert text to audio format. Unlike voice assistants like Siri, WaveNet allows you to create much more realistic-sounding voices by sampling real human speech and modeling signals. WaveNet’s learning allowed the system to create a 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 speak and understand what you are saying. A vivid example is — the LipNet system, developed using neural network technology by scientists at Oxford University. LipNet has become the world’s first system to recognize lip speech, not just individual words, but whole sentences. For this, the system processes the video sequence into a plurality of fragments and layers. The results are impressive:
Speech recognition technology gives a qualitative impetus to the development of medical technologies (for example, the creation of fundamentally new hearing aids) and protection systems—for example, reading lip speech by watching video from surveillance cameras.
Those are just a few of the opportunities offered by 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, deep learning applications 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, development delay in children prediction, and 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 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. Well-trained Deep Learning models can effectively detect malware in the digital realm, classify new and previously unknown samples, and provide 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 and detect and deal with spam and other forms of social engineering.
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Originally published at https://stfalcon.com.
