Top Applications of Deep Learning
What is Deep Learning?
A neural network architecture-based deep learning technique is a subset of machine learning that imitates the way how human beings gain knowledge. In neural networks, “deep” refers to the number of hidden layers and the no of hidden layers in traditional neural networks is two to three, while deep neural networks can hold 150 or more than that. As part of data science, including statistics and predictive modeling, deep learning is an important component. A key benefit of deep learning is that it speeds up and simplifies the process of gathering, examining, and analyzing immense amounts of data that is utilized by data scientists.
Through the help of deep learning, computer models can retain to perform classification tasks from images, text, or audio directly. In order to prepare models, a considerable set of labeled data and a neural network architecture holding many layers is used. Deep learning algorithms vary from traditional machine learning algorithms in that they are stacked in a hierarchy from the simplest to the most complex.
As an example of deep learning, picture a toddler whose first word is “cat.”. As the toddler points to objects and says the word cat, he learns what a cat is and what a cat isn’t. In response, parents say, “Yes, that’s a cat,” or “No, that’s not a cat.” As the toddler points to objects, he becomes more aware of the traits that all cats share. By building a hierarchy, the toddler clarifies a complex abstraction — the concept of a cat — by using the knowledge gained from the preceding layers of the hierarchy.
Stats from relevant recent sources of its usage in various industries:
Deep learning has a lot of applications and in the next few years, the market for deep learning will reach almost $1 billion. Check out the following statistics:
- Platforms behind voice assistants were created by deep learning, and the pandemic increased the use of these devices. From Mar-April 2020, twenty-five per cent of people used it several times a day, up from 20% between Dec 2019-Jan 2020 (Voicebot.ai, 2020).
- The market size of deep learning software in the US by 2025 (Statista, 2019): $80 million.
- By 2027, the global deep learning market is expected to reach $44.3 billion at a CAGR of thirty-nine per cent (ReportLinker, 2020).
- Deep learning techniques account for 40% of the annual value created by analytics (McKinsey).
- Google’s Deep Learning program detects breast cancer with an accuracy of 89% (Health Analytics).
After checking out these interesting stats about deep learning you must be curious about knowing — which are common applications of deep learning in artificial intelligence (ai)?.
So, let’s begin with applications of deep learning.
Applications of deep learning:
- Digital Marketing:
Everything is going digital currently, including marketing. Marketing on the internet is more prevalent than traditional methods today because traditional methods are no longer in-demand. Marketing professionals can gauge the efficacy of their campaigns by leveraging deep learning in digital marketing.
By utilizing data and its output, it is revolutionizing the marketing industry, and based on deep learning algorithms, companies can very accurately predict customer demand, customer satisfaction, and create a specific target market for their brand. The modern marketing professional can truly use it as an invaluable asset to keep their business competitive.
SEO which is very essential for many websites for engagement is one region where AI is likely to outshine human marketers. This is because SEO is data-driven and search engines use algorithms to define the order in which websites appear on their results pages, which makes it a natural fit for the technology.
Although privacy is a major concern, once the deep learning algorithm recognizes a person, there is no boundary between a successful marketing campaign and a satisfied customer. The return on investment, as well as engagement rate, can get a boost if customer experience on various digital platforms is improved by analyzing the customer’s behavior.
2) Deep Learning in Natural Language Processing (NLP):
An algorithmic approach to interpreting and manipulating human speech, known as natural language processing, falls under the areas of linguistics, computer science, and AI.
As humans, we need years and years of human interaction and exposure to various social environments to learn and understand the variation in tones and patterns of a language so we cannot expect a machine to learn all these things by itself.
With the help of deep learning and constructing correct responses to every situation, NLP i.e Natural Language Processing trains machines to do that easily.
Various algorithms are used in NLP for analyzing the data which makes the system capable of producing human language or recognizing tonal variations in a human voice.
Deep learning is gaining popularity nowadays in a no. of areas of natural language processing which includes providing answers to various questions, model building, etc. It has been used quite often for upgrading the text analytic functions and features of natural language processing. These proposals have helped a lot in converting text which was not structured earlier into beneficial information.
3) Chatbots:
Chatbots are computer software programs that mimic human conversation via text or audio messages. Chatbots are very common when we use an online platform nowadays and today’s AI systems are able to understand users’ requirements, and preferences and recommend what actions to execute with very less or bare minimal intervention from humans. A number of popular conversational assistants are available in the market today including Siri which is developed by Apple, Cortana which is developed by Microsoft, and Alexa developed by Amazon and Google Assistant.
With the advent of Chatbots, all platforms can now provide their visitors with a customized experience. Machine learning algorithms and deep learning algorithms are used by chatbots to generate a combination of replies. Having been trained with a lot of data, chatbots can understand customer requests, and difficulties faced by them and also guide and assist the customer in resolving their problem in a very simple way.
In addition, it has many other benefits like it saves time for customers and with the advent of chatbots, companies are employing less no. of employees to reduce their costs and improve their customer experience.
4) Deep Learning in Virtual Assistants:
Virtual Assistants like Alexa which is developed by Amazon, Siri by Apple, and Google Assistant are popular applications for deep learning. These are used in many households and offices to make day-to-day tasks easier. The number of people using these assistants is increasing and these assistants are becoming smart and learn more and more about you and your preferences whenever you interact with them. Deep learning is used by virtual assistants to understand our interests, like our favourite hangout places or our favourite tv shows. In order to understand what we say, they consider human speech. A virtual assistant can also translate our voice into a textual format, schedule meetings for us, etc.
Virtual assistants can do everything from handling to instantly and automatically answering our work calls for helping us and our team manage tasks. A virtual assistant can also assist us in composing and mailing emails to your boss, clients, teachers, etc by summarizing the documents.
In addition, virtual assistants are used in many places and are also being integrated into various devices, including microwave systems and cars. These assistants will continue to become more intelligent day by day, thanks to the internet and smart devices.
5) Fraud Detection and News Aggregation:
Especially money transactions are going digital nowadays, which is why deep learning is a valuable tool. A number of applications are being developed with the help of deep learning that can help in detecting fraud which can help save financial organizations a tremendous amount of money. Also, the news feed can now be filtered to remove all the unwanted news and readers can get to read the news that is based on their field of interest.
Detection of fake news is very essential nowadays because the internet is filled with a lot of sources of blogs, research papers, news, and many other forms of information and all of them are not faithful. Fake news is circulating very fast in today’s time with the help of bots and therefore it becomes extremely difficult to tell whether the news is fake or real.
In addition to developing classifiers to detect fake and biased news, deep learning can also be used to notify you about potential privacy violations and take out the content. The major challenge with training and validating a deep learning neural network for news detection is that the data is littered with opinions from all around the world and it’s difficult to determine whether the news story is biased or neutral.
6) Earthquake Prediction:
Earth science is grappling with the problem of earthquake forecasts because of their devastating outcomes. An earthquake forecast that is successful could save numerous lives. Scientists are attempting to predict earthquakes based on when and where they will happen, as well as on the basis of their magnitude
Von mises yield criterion is used by deep learning for the prediction of earthquakes and this application of deep learning helped the scientists to improve the time of earthquake prediction by fifty thousand per cent. We went from just guessing when an earthquake is gonna happen to being able to have a decent prediction as to when the earthquakes are gonna hit.
A deep learning model which is taught on a large amount of data will be able to learn from the data by extracting elements from raw data to recognize natural things and make good decisions about a broad range of subject areas. Additionally, it has become easier for large models to be trained because of refinements in computing power. Deep learning makes earthquake forecast possible due to its advantages.
7) Deep Learning in Visual Recognition:
Image recognition involves recognizing photos and organizing them into separate categories based on their features. Thus, image recognition software and apps can determine what is shown in a photo and differentiate them. I am pretty sure you have seen this in your social media application or on your mobile phone. Essentially, it sorts images based on the location of people in a photograph, occasions, etc.
Consider browsing through a collection of old photos for remembering some good old times. Some photos need framing, but first, we like to arrange them in proper order. Since there was no information about the photos, it was only possible to do this manually. All we could do was arrange them on the basis of the date on which the photo was taken, but sometimes the date is missing from downloaded photos. Due to Deep Learning, images can now be arranged according to locations at which the photos were taken or on the basis of looks, individuals, occasions, etc.
8) Customizations:
Platforms including Amazon, Flipkart, etc., are using Deep Learning to make their e-commerce offerings more personalized by offering suggestions for different products, customized packages, and deals.
Deep learning technology is used by platforms like Amazon Prime, and Gaana to screen videos, tv shows, and your most loved songs based on your viewing preferences. It helps you to stream famous shows and songs from your playlists through online streaming media. Also, we get a customized experience in online streaming platforms just because of deep learning, and ads are also recommended to us based on our viewing preferences. In terms of acceptance, this is the most popular application. For instance, after you have watched a movie starring Tom Holland, your Amazon account may suggest other movies starring Tom Holland. Using deep learning, users can experience customized streaming based on past streaming choices and experiences. Remember deep learning the next time you listen to a song and similar songs get queued up behind it.
There are some people who are creeped out by the content suggested to them based on their liking, but the data collected is all derived from previous dealings with your application.
9) Robotics:
A few robotics applications have been boosted by deep learning’s favorable results in the field of computer vision and deep learning is heavily used in robotics to execute human-like tasks. Robots are built to understand the world around them and it is very important for them to figure out which thing is what. If you go back 20 years robots could not figure out many basic differences like the difference between a soda bottle and a pen because they both have the same shape.
It’s no secret that the field of robotics proposes a distinctive set of challenges for learning algorithms which are:
It can be difficult and even impossible to code entirely fresh learning algorithms and elements for each job that robots execute.
The second challenge is that robots have to handle a great deal of variousness in the real world which makes it tough for many learning algorithms to handle.
But deep learning algorithms are general models that are able to learn straight from data, so they are well-suited for robotics. Certainly, robotics and artificial intelligence boost human capability, improving productivity and enabling the transition from simple thinking to human-like capabilities
10) Autonomous Vehicles:
The intent of driving is to react safely to external factors such as cars around you, street signs, and pedestrians to get from one point to another. Despite the fact that we are still some distance away from fully autonomous vehicles, deep learning has been pivotal in getting the technology to where it is today.
Self-driving is getting a boost in today’s time and is more powerful than ever due to a lot of advancements such as increased-performance graphics cards, strong processors, and vast quantities of info. In addition to easing traffic congestion, it will boost safety. Automobiles that drive themselves are autonomous decision-making systems. Inertia detectors and GPS are a few types of sensors that can provide data streams. The deep learning algorithms then model the data and make decisions according to the car’s environment.
Automated vehicles are able to foresee obstacles in their background and deduce a safe path to get from a particular location to another, by bringing into account where they need to go using deep learning.
Pony.ai, for example, employs deep learning to power its planning as well as a control module for its independent vehicle technology that allows automobiles to navigate roads with eight lanes, control mishaps, etc. Waymo, a Google subsidiary, is another self-driving car company that employs deep learning.
Conclusion
To conclude, deep learning enables computers to be like human brains in both performance and behaviour. It has become prominent in a wide range of industries. There are numerous applications and benefits of deep learning that can be explored in further detail. Our lives are becoming easier and more efficient due to deep learning, which is present in everything from automatic cars to voice assistants.
We hope that after reading this article you understood the meaning of deep learning and its applications in various fields.
