Artificial intelligence (AI) has risen dramatically in the past few decades. It’s discussed in every corner of the world, on everyone’s lips. Businesses and organizations apply AI to push company growth by providing better customer experience, predicting market trends, and developing effective marketing strategies.
Appearing as one of the subsets of Machine Learning, which is also a discipline of Artificial Intelligence, Deep Learning proves the best approach to engineer intelligent systems. It allows you to analyze data automatically via neural networks that follow the human brain’s structure.
In this article, we’ll briefly go through the definition and 10 common applications of deep learning in artificial intelligence.
Deep learning is a section of machine learning methods that uses artificial neural networks with more than three layers to conduct data analysis automatically.
For a better explanation, deep learning simulates the behavior of the human brain and then teaches computers to act similarly by learning from examples. This technology can help take various actions that require human intelligence, such as image identification, language translation, and decision-making.
Below are the 10 popular applications of deep learning in artificial intelligence that can blow your mind.
First and foremost, deep learning algorithms have been used to achieve state-of-the-art results in computer vision tasks such as image classification, object detection, and semantic segmentation.
It learns from the way our brain processes information and detects patterns. Platforms using this model will recognize an object via a list of labeled photos. Take ZeroEyes, an AI gun detection platform, as an example. It supports detecting weapons in public areas such as schools, parks, or spares so the police can be notified quickly.
Another typical sample of computer vision is detecting license plates via the traffic camera system. These platforms will capture the license plate of every vehicle to check and catch traffic violators.
Deep learning has also been made used to perform natural language processing tasks such as machine translation, text classification, and question-answering.
This technology enables robots to read messages and guess their meaning. We can even pick out complex patterns in sentences and paragraphs to match the context.
Chatbots are a case in point. They take advantage of deep learning to respond to messages and provide direct contact to a hive human automatically and accurately. The deep learning function in chatbots doesn’t just stop there. Deep learning also helps exploit data and spot sentiment in messages by translating and categorizing text.
Deep learning algorithms serve in designing robots/robot hands that can navigate autonomously in complex environments and perform tasks such as grasping and manipulation.
For instance, these tools assist hospitals, factories, manufacturing, and warehouses in carrying goods or products. This will save a lot of time and money due to the significant reduction of manual labor. Remarkably, not only can artificially intelligent robots handle technical tasks, but they also interact with a human.
Besides the industry, deep learning, computer vision, and robots play a key role in farming and agriculture too. Global agriculture will see huge growth in AI investment, from $1B in 2020 to $4B in 2026.
Deep learning gives farmers, co-ops, and agricultural development companies a helping hand in improving agricultural yields and quality. They can use these powerful technologies to identify the animal and human breaches and improve pest management via in-ground sensors. Plus, they’re able to forecast crop pricing based on yield rates.
Fraud scams have never been a new problem, especially in this booming growth of the digital world. According to TransUnion’s Consumer Pulse Q1 2022 report, 38% of Americans were impacted by digital fraud in Q1 2022.
The use of deep learning lets businesses and organizations detect patterns of fraud or abnormal behavior in financial transactions, insurance claims, and other data. A deep learning model must go through a process of collecting data, analyzing and segmenting it, and extracting required features. Then, it will apply algorithms to predict fraud and end up with a fraud detection machine learning model.
Deep learning brings a great benefit to the world by building recommendation systems that suggest items to users based on their past behavior.
If you once wonder why streaming platforms like YouTube, Spotify, or Netflix keep showing your favorite videos and audio, then it’s what deep learning is doing. They learn from your action, what you choose to listen/hear, and things that you never look at. As a result, users will receive a professional and unique experience for users.
However, the application of deep learning in recommendation systems doesn’t just stop at streaming services. Social networks like Facebook or TikTok take a similar approach to suggest relevant posts, videos and accounts for users in their feeds.
Systems using deep learning model authorizes the government to predict the demographics of each area. To achieve that, some models capture millions of street view images to detect cars and their specifications. Just based on cars’ characteristics, such as color, make, model, and body type, we can forecast the size of the population.
It’s worth mentioning traffic condition prediction, another useful application of deep learning in demographics. Learning from these street view images to find traffic density, systems with deep learning models like Google Maps will show us the right ways to get to the destination as well as recommend the shortest path.
Deep learning is well thought out to streamline the patient experience by processing tasks such as automated diagnosis, disease prediction, and image-based treatment planning.
To make this happen, it collects and studies the data of previous patients to provide us with the best care pathway, drug research, and clinical trials.
Deep learning is a key technology behind self-driving cars, helping them to perceive the world and make decisions about where to go.
Tesla would be the first option coming into mind when talking about intelligently self-driving cars. It’s the most popular company that designs and manufactures electric vehicles. It trains car models by utilizing unsupervised learning methods so these cars can detect and avoid people and objects on the road.
#10 Security
Facial recognition, intrusion detection, and monitoring of public spaces are common applications of deep learning in the security niche.
Email filtering serves as an example in this case. Once an email is sent to your inbox, the mail platform will automatically classify it as important, or spam. Thanks to deep learning, Gmail can easily filter emails by relying on email content, header, general blacklists, and permissions.
The idea of creating systems that can mimic human intellect is always of great interest. The help of deep learning is and will keep revolutionizing the way we learn and work. It’s applied in every critical field, from demographics to healthcare and security.
Explore our article about the 10 benefits of machine learning to get updated on how machine learning transforms the way different sectors function.
Content Map What Is Deep Learning? Top 8 Advantages of Deep Learning 6 Potential Disadvantages of Deep Learning Take Advantage of Deep Learning Ever wondered what powers self-driving cars or enables platforms like Netflix, Amazon, YouTube, and Spotify to provide you with personalized movie and music recommendations? Well, the answer is deep learning. Deep learning […]
Read More >
