Deep learning, a subset of machine learning, is a way of conducting automated data analysis via artificial neural networks, which are algorithms that effectively mimic the human brain’s structure and function. While it remains a work in progress, there is unfathomable potential — and plenty of applications and use cases.
Common Deep Learning Applications
- Fraud detection
- Customer relationship management systems
- Computer vision
- Vocal AI
- Natural language processing
- Data refining
- Autonomous vehicles
- Investment modeling
What Is Deep Learning?
Deep learning is a type of machine learning (which is a subset of AI) that consists of layers of neural networks that process data and learn to define and classify certain objects within that data. Each layer of neural networks uses the insights of the previous one to improve the accuracy with which they classify objects and make predictions.
Deep learning can handle larger sets of complex data, process unstructured data and recognize new features within data on its own, displaying a great degree of automation.
Why Use Deep Learning?
Deep learning’s ability to recognize new features within data sets means human workers can spend less time having to train deep learning neural networks. These networks can then learn how to perform new tasks on their own, speeding up workflows through automation.
Ideal for Complex and Unstructured Data
If a company’s data is complex or raw, teams can rely on deep learning to process the data and provide timely insights. This advantage has become crucial as organizations are faced with the task of organizing massive amounts of big data.
Ability to Scale With Growing Data
Gathering more data can put a strain on traditional machine learning algorithms, but deep learning can keep up with larger quantities of data. This trait makes deep learning neural networks ideal for companies operating in cloud and edge environments.
Deep learning is able to learn tasks quickly with its layers of neural networks, and these networks also work to enhance their abilities and predictions. That means deep learning can tirelessly perform tasks with a degree of accuracy that human workers are unable to replicate.
Deep learning can save teams time and resources by automating tasks, making use of unstructured data and adapting to increasing amounts of data. It can also single out flaws in products or errors in code. This way, businesses can reduce the costs of product-related accidents and having to recall faulty devices.
Future of Deep Learning Applications
As the world becomes more interconnected, automated and data-saturated, society as a whole may come to rely more heavily on deep learning for everyday functions, whether that’s spotting credit card fraud or powering autonomous vehicles. And deep learning’s ability to process complex and large data sets makes it the perfect solution for companies that may have outdated data platforms and systems.
To get a sense of how deep learning is being applied today, take a look at 20 innovative deep learning examples below.
Top Deep Learning Applications to Know
Fraud is a growing problem in the digital world. In 2022, consumers reported 2.4 million cases of fraud to the Federal Trade Commission. Identify theft and imposter scams were the two most common fraud categories.
To help prevent fraud, companies like Signifyd use deep learning to detect anomalies in user transactions. Those companies deploy deep learning to collect data from a variety of sources, including the device location, length of stride and credit card purchasing patterns to create a unique user profile. Mastercard has taken a similar approach, leveraging its Decision Intelligence and AI Express platforms to more accurately detect fraudulent credit card activity. And for companies that rely on e-commerce, Riskified is making consumer finance easier by reducing the number of bad orders and chargebacks for merchants.
Customer Relationship Management
Customer relationship management systems are often referred to as the “single source of truth” for revenue teams. They contain emails, phone call records and notes about all of the company’s current and former customers as well as its prospects. Aggregating that information has helped revenue teams provide a better customer experience, but the introduction of deep learning in CRM systems has unlocked another layer of customer insights.
Deep learning is able to sift through all of the scraps of data a company collects about its prospects to reveal trends about why customers buy, when they buy and what keeps them around. This includes predictive lead scoring, which helps companies identify customers they have the best chances to close; scraping data from customer notes to make it easier to identify trends; and predictions about customer support needs.
Deep learning aims to mimic the way the human mind digests information and detects patterns, which makes it a perfect way to train vision-based AI programs. Using deep learning models, those platforms are able to take in a series of labeled photo sets to learn to detect objects like airplanes, faces and guns.
The applications for image recognition are expansive. Neurala uses an algorithm it calls Lifelong-DNN to complete manufacturing quality inspections. Others, like ZeroEyes, use deep learning to detect firearms in public places like schools and government property. When a gun is detected, the system is designed to alert police in an effort to prevent shootings. And finally, companies like Motional rely on AI technologies to reinforce their LiDAR, radar and camera systems in autonomous vehicles.
Agriculture will remain a key source of food production in the coming years, so people have found ways to make the process more efficient with deep learning and AI tools. In fact, a 2021 Forbes article revealed that the agriculture industry is expected to invest $4 billion in AI solutions by 2026. Farmers have already found various uses for the technology, wielding AI to detect intrusive wild animals, forecast crop yields and power self-driving machinery.
Blue River Technology has explored the possibilities of self-driven farm products by combining machine learning, computer vision and robotics. The results have been promising, leading to smart machines — like a lettuce bot that knows how to single out weeds for chemical spraying while leaving plants alone. In addition, companies like Taranis blend computer vision and deep learning to monitor fields and prevent crop loss due to weeds, insects and other causes.
When it comes to recreating human speech or translating voice to text, deep learning has a critical role to play. Deep learning models enable tools like Google Voice Search and Siri to take in audio, identify speech patterns and translate it into text. Then there’s DeepMind’s WaveNet model, which employs neural networks to take text and identify syllable patterns, inflection points and more. This enables companies like Google to train their virtual assistants to sound more human. In addition, Mozilla’s 2017 RRNoise Project used it to identify and suppress background noise in audio files, providing users with clearer audio.
Natural Language Processing
The introduction of natural language processing technology has made it possible for robots to read messages and divine meaning from them. Still, the process can be somewhat oversimplified, failing to account for the ways that words combine together to change the meaning or intent behind a sentence.
Deep learning enables natural language processors to identify more complicated patterns in sentences to provide a more accurate interpretation. Companies like Gamalon use deep learning to power a chatbot that is able to respond to a larger volume of messages and provide more accurate responses. Other companies like Strong apply it in its NLP tool to help users translate text, categorize text to help mine data from a collection of messages and identify sentiment in text. Grammarly also uses deep learning in combination with grammatical rules and patterns to help users identify writing errors and gauge the tone of their messages.
When large amounts of raw data are collected, it’s hard for data scientists to identify patterns, draw insights or do much with it. It needs to be processed. Deep learning models are able to take that raw data and make it accessible. Companies like Descartes Labs use a cloud-based supercomputer to refine data. Making sense of swaths of raw data can be useful for disease control, disaster mitigation, food security and satellite imagery.
The divide between humans and machines continues to blur as virtual assistants become a part of everyday life. These AI-driven tools display a mix of AI, machine learning and deep learning techniques in order to process commands. Apple’s Siri and Google’s Google Assistant are two prominent examples, with both being able to operate across laptops, speakers, TVs and other devices. People can expect to see more virtual assistants and chatbots in the near future as the industry is on track to undergo plenty of growth through 2030.
Driving is all about taking in external factors like the cars around you, street signs and pedestrians and reacting to them safely to get from point A to B. While we’re still a ways away from fully autonomous vehicles, deep learning has played a crucial role in helping the technology come to fruition. It allows autonomous vehicles to take into account where you want to go, predict what the obstacles in your environment will do and create a safe path to get you to that location.
For instance, Zoox has used AI technologies to help its fully autonomous robotaxi vehicles learn from some of the most challenging driving situations to improve their decision-making under various circumstances. Other self-driving car companies that use deep learning to power their technology include Tesla-owned DeepScale and Waymo, a subsidiary of Google.
While some software uses deep learning in its solution, if you want to build your own deep learning model, you need a supercomputer. Companies like Boxx and Nvidia have built workstations that can handle the processing power needed to build deep learning models. NVIDIA’s DGX Station claims to be the “equivalent of hundreds of traditional servers,” and enables users to test and tweak their models. Boxx’s APEXX W-class products work with deep learning frameworks to provide more powerful processing and dependable computer performance.
Investment modeling is another industry that has benefited from deep learning. Predicting the market requires tracking and interpreting dozens of data points from earning call conversations to public events to stock pricing. Companies like Aiera use an adaptive deep learning platform to provide institutional investors with real-time analysis on individual equities, content from earnings calls and public company events. Even some of the bigger names like Morgan Stanley are joining the AI movement, using AI technologies to provide sound advice on wealth management through robo-advisors.
Organizations are stepping up to help people adapt to quickly accelerating environmental change. One Concern has emerged as a climate intelligence leader, factoring environmental events such as extreme weather into property risk assessments. Meanwhile, NCX has expanded the carbon-offset movement to include smaller landowners by using AI technology to create an affordable carbon marketplace.
Online shopping is now the de-facto way people purchase goods, but it can still be frustrating to scroll through dozens of pages to find the right pair of shoes that match your style. Some e-commerce companies are turning to deep learning to make the hunt easier. Among Clarifai’s many deep learning offerings is a tool that helps brands with image labeling to boost SEO traffic and surface alternative products for users when an item is out of stock. E-commerce giant eBay also applies a suite of AI, machine learning and deep learning techniques to power its global online marketplace and further enhance its search engine capabilities.
While computers may not be able to replicate human emotions, they are getting better at understanding our moods thanks to deep learning. Patterns like a shift in tone, a slight frown or a huff are all valuable data signals that can help AI detect our moods.
Companies like Affectiva are using deep learning to track all of those vocal and facial reactions to provide a nuanced understanding of our moods. Others like Cogito analyze the behaviors of customer service representatives to gauge their emotional intelligence and offer real-time advice for improved interactions.
Ever wonder how streaming platforms seem to intuit the perfect show for you to binge-watch next? Well, you have deep learning to thank for that. Streaming platforms aggregate tons of data on what content you choose to consume and what you ignore. Take Netflix as an example. The streaming platform uses machine learning to find patterns in what its viewers watch so that it can create a personalized experience for its users.
Introduced back in 2015 by a team of Google engineers, the concept of deep dreaming has given another dimension to the realm of deep learning. Deep dreaming involves feeding algorithms to machines, which can then mimic the process of dreaming in human neural networks. A website called Deep Dream Generator has taken advantage of these algorithms, allowing creators to produce breathtaking digital art.
Companies can glean a lot of information from how a user interacts with its marketing. It can signal intent to buy, show that the product resonates with them or that they want to learn more information. Many marketing tech firms are using deep learning to generate even more insights into customers. Companies like 6sense use deep learning to train their software to better understand buyers based on how they engage with an app or navigate a website. This can be used to help businesses more accurately target potential buyers and create tailored ad campaigns. Other firms like Dstillery use it to understand more about a customer’s consumers to help each ad campaign reach the target audience for the product.
The success of a factory often hinges on machines, humans and robots working together as efficiently as possible to produce a replicable product. When one part of the production gets out of whack, it can come at a devastating cost to the company. Deep learning is being used to make that process even more efficient and eliminate those errors.
Companies like OneTrack are using it to scan factory floors for anomalies like a teetering box or an improperly used forklift and alert workers to safety risks. The goal is to prevent errors that can slow down production and cause harm. Then there’s Fanuc, which uses it to train its AI Error Proofing tool to discern good parts from bad parts during the manufacturing process. Energy giant General Electric also uses deep learning in its Predix platform to track and find all possible points of failure on a factory floor.
The healthcare industry contends with inefficiencies, but deep learning plays a crucial role in streamlining the patient experience. KenSci, a company under the Advata umbrella, uses AI technology that learns from past performance data to predict how much space and what resources teams need to provide proper patient care. In addition, PathAI harnesses the predictive abilities of AI to garner more accurate data from drug research, clinical trials and patient diagnostics. Deep learning has also been proven to detect skin cancer through images, according to a National Center for Biotechnology Report.
Top-performing athletes are able to be more intentional about the ways they improve their games, thanks to AI-driven data. Companies like Hawk-Eye Innovations have raised the level of professional play through advanced replay systems, ball-tracking technology and timely game data. However, this attention to detail isn’t reserved for sports royalty. Nex has built the HomeCourt app that basketball players of all skill levels can consult for insights on how to fine-tune their shooting motion and more.