Top Deep Learning Applications in 2022

Deep learning is a subfield of machine learning that is inspired by the structure and function of the brain. Deep learning algorithms are designed to learn in a way that is similar to how the brain learns. This allows them to make predictions or decisions based on data in a way that is more accurate than traditional machine learning algorithms.

Deep learning is a relatively new field of machine learning and is currently one of the most promising areas of research. Deep learning has been used to achieve state-of-the-art results in many different fields, including computer vision, natural language processing, and robotics.

Why Deep Learning? Slide by Andrew Ng, all rights reserved.

A computer model learns to carry out categorization tasks directly from pictures, text, or voice using deep learning. Deep learning models may attain modern precision, sometimes even outperforming human ability. A sizable collection of labeled data and multi-layered neural network architectures are used to train models.

A computer model learns to carry out categorization tasks directly from pictures, text, or voice using deep learning. Deep learning models may attain modern precision, sometimes even outperforming human ability. A sizable collection of labeled data and multi-layered neural network architectures are used to train models.

We never dreamed that deep learning technologies would enable self-driving vehicles and voice-activated assistants like Alexa, Siri, and Google Assistant just a few years ago. Today, however, these inventions are a regular part of our lives. Deep learning continues to intrigue us with its limitless potential, including fraud detection and pixel restoration.

Let’s discover more about how deep learning is used in many sectors.

Self-Driving Vehicles

The primary factor behind autonomous driving is deep learning. A system is fed a million pieces of data to create a model and train the computers to learn and evaluate the outcomes in a secure setting. The Uber Artificial Intelligence Labs in Pittsburgh are trying to integrate numerous intelligent features, such as food delivery possibilities, with the usage of driverless cars in addition to making driverless cars more commonplace. Uber earned $1 billion in 2019 to fund further research and increase the safety of its vehicles. The handling of novel situations is the main problem for autonomous vehicle engineers. With growing exposure to millions of scenarios, a deep learning algorithm’s regular cycle of testing and implementation ensures safe driving. To maneuver through traffic, detect pathways, signs, pedestrian-only routes, and real-time factors like traffic volume and road obstructions, sophisticated and concise models are being developed using data from cameras, sensors, and geomapping. Global industry growth for autonomous cars is 16% a year. 

Fraud news detection and news aggregation

Deep learning is heavily utilized in news aggregation, which supports attempts to tailor news to consumers’ preferences. The unpleasant and ugly information may now be removed from your news stream with a filter. Even though it may not appear new, reader personas are being defined with greater complexity to filter out content based on a reader’s interests and geographical, social, and economic factors. When it comes to influencing reader opinion (Bhartiya Janta Party vs. Indian National Congress), elections (Read Donald Trump Digital Campaigns), and utilizing personal data, the Cambridge Analytica scandal is a classic case in point (Facebook data for approximately 87 million people was compromised).

Natural Language Processing (NLP)

One of the most challenging things for people to learn is how to comprehend the complexity of language, including its syntax, semantics, tonal subtleties, expressions, and even sarcasm. Humans learn to respond appropriately and uniquely to each situation via constant training from birth and exposure to various social contexts. The global market for Natural Language Processing (NLP), which was first anticipated to be worth US$13 billion in 2020 but has now been updated to US$25.7 billion, is expected to expand at a CAGR of 10.3% from 2020 to 2027 despite the COVID-19 controversy.

Virtual Assistants

Virtual assistants like Alexa, Siri, and Google Assistant are the most well-known use of deep learning. Each time you speak with one of these assistants, they have the chance to get to know more about your voice and accent, giving you a second chance to communicate with others. Deep learning is a technique that virtual assistants employ to learn more about you, your preferences for dining out, your favorite music, and your favorite locations. They acquire the ability to follow your instructions via interpreting spoken language to do so. By 2024, there are predicted to be 8.4 billion assistants on various gadgets, which is more than the world’s present population. Google Assistant is the most accurate voice assistant, with a 98% accuracy rate. Amazon’s Alexa has a 93% accuracy rate, while Apple’s Siri has a 68% accuracy rate.

Entertainment

To automatically create highlights for transmission, Wimbledon 2018 uses IBM Watson to analyze player expressions and emotions from hundreds of hours of video. They avoided a lot of work and expense this way. Deep Learning enabled them to use a player or match popularity and crowd response to create a more precise model (otherwise, it would just have highlights of the most expressive or aggressive players). Netflix and Amazon are improving their deep learning skills to give their viewers a tailored experience by building personas that consider show preferences, time of access, history, etc., to offer shows that a particular viewer will enjoy.

Visual Detection

Currently, deep learning Images can be categorized according to events, dates, locations identified in pictures, faces, a group of people, or other criteria. Modern visual recognition systems made up of numerous layers, from simple to complex, are needed to search for a particular photo inside a library (let’s assume a dataset as vast as Google’s image library). A large picture by heavily utilizing convolutional neural networks, Tensorflow, and Python, visual identification through deep neural networks is accelerating progress in this area of digital media management.

Fraud Detection

The banking and finance industry, which is burdened with the responsibility of fraud detection as money transactions move online, is another area that benefits from deep learning. Fraud prevention and detection are carried out based on finding trends in client transactions and credit ratings, identifying aberrant behavior, and identifying outliers. Developing autoencoders in Keras and Tensorflow will help financial institutions avoid spending billions of dollars on insurance and recovery for credit card theft.

Healthcare

The whole healthcare sector is undergoing change. Readmissions cost the healthcare industry tens of millions each year, making them a significant concern. However, healthcare behemoths are reducing costs while reducing health risks related to readmissions by utilizing deep learning and neural networks. Some of the Deep Learning projects gaining traction in the healthcare industry include assisting with early, accurate, and quick diagnosis of life-threatening diseases, augmenting clinicians to address the shortage of qualified doctors and healthcare providers, standardizing pathology results and treatment plans, and understanding genetics to predict future risk of diseases and unfavorable health episodes. Regulatory authorities are increasingly using AI to develop treatments for incurable diseases in clinical research. Still, doctors’ skepticism and the absence of a sizable dataset continue to be obstacles to applying deep learning in medicine.

Healthcare is expected to have the most intelligent devices for AI research and use by 2027. It is estimated that by 2022, machines in healthcare that can work without the help of a person will be 75% successful. By 2026, artificial intelligence has the potential to save the clinical healthcare business more than $150 billion.

Personalizations

Today, every platform attempts to leverage chatbots to provide its users with individualized, human-touched experiences. Deep Learning is assisting e-commerce behemoths like Amazon, E-Bay, Alibaba, and others in their efforts to offer seamless, personalized experiences in the form of product recommendations, personalized packages, and discounts and identifying significant revenue opportunities during the holiday season. Launching goods, services, or plans more likely to appeal to people’s psyches and encourage growth in niche markets is how even newer markets are surveyed.

Language Translations for Images

The translations between images and languages are a fascinating Deep Learning application. It is now possible to automatically convert photographic photos with text into the real-time language of your choice using the Google Translate app. Simply place the camera on top of the item, and your phone will use a deep learning network to scan the image, convert it to text using OCR, and then translate it into the target language. Because languages will eventually no longer be a barrier to communication, this application is quite helpful.

Restoration of pixels

Before the advent of deep learning, zooming into videos beyond their actual resolution seemed illogical. In 2017, Google Brain researchers created a Deep Learning network to determine a person’s face from very low-quality photos of faces. The Pixel Recursive Super Resolution was the name given to this technique. It considerably improves the quality of photographs, highlighting essential characteristics in a way that is just right for identifying personalities.

The image above shows a collection of images that includes an original set of 8 x 8 shots on the right and the ground truth, which was the true face that appeared in the photos at the time, on the left. Finally, the computer’s guess is contained in the center column.

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Prathamesh Ingle is a Consulting Content Writer at MarktechPost. He is a Mechanical Engineer and working as a Data Analyst. He is also an AI practitioner and certified Data Scientist with interest in applications of AI. He is enthusiastic about exploring new technologies and advancements with their real life applications