10 Ways In Which Industries Are Leveraging Deep Learning

From advertising to entertainment, deep learning has unique applications that invite innovation.

Deep learning is a computer software that is inspired by the way neurons in a brain. It's a subset of machine learning that is used to solve complex problems and generate an intelligent solution, which was rather difficult to do before the inception of this technology. To analyze data and make predictions, deep learning uses artificial neural networks. Ever wondered what's the technology behind Apple's Siri and Amazon's Alexa? It's these artificial neural networks that train machines to respond to instructions. This technology has made its way into almost every industry with applications that help companies innovate.

How Are Industries Leveraging Deep Learning Technology?

1. Virtual Assistants

Almost everyone is familiar with Alexa, Siri, Google Assistant, and Cortana. These cloud-based virtual assistants use deep learning algorithms to understand natural voice commands and respond to a query or a task. Apart from personal use, another application for virtual assistants is in the automobile industry to provide innovative features for a better customer experience.

2. Chatbots

Chatbots fill the gap between customer-company interaction. While a company cannot expect a human to be present 24/7 to answer customer queries, chatbots act as a digital personal assistant by solving problems in seconds. A chatbot is an AI application that uses text or text-to-speech conversion to carry about its tasks. It's capable of communicating like humans by using deep learning algorithms to generate several reactions. From retail to healthcare, chatbots have become essential in almost every industry.

3. Disease Diagnosis

Talking about healthcare, deep learning has an important application in the healthcare industry. This technology has enabled computer-powered disease detection and diagnosis and has helped research and drug discovery teams diagnose life-threatening diseases like cancer and diabetic retinopathy using medical imaging.

4. Generating Accurate Recommendations

Netflix, Amazon Prime, YouTube, Spotify, and iMusic became a hit due to their relevant movie, songs, and video recommendations. How does it know what you like? That's the work of deep learning. Based on a user's browsing history and online content consuming behavior, the systems generate suggestions. Deep learning techniques also enable adding sounds to silent movies and generate subtitles automatically.

5. Music Composition

People who are into music composition and production use machines that can learn notes, structures, and patterns of music. Deep learning-based generative models like WaveNet can develop raw audio independently. Music21 is a python-based toolkit that's used for computer-powered musicology. A user can train the system to develop music by teaching music theory fundamentals, generating music samples, and studying music.

6. Enabling Predictive Advertising

In the advertising industry, deep learning algorithms help publishers and advertisers increase the visibility of ads and boost campaigns to optimize a user's experience. Advertisements are a costly affair, but deep learning models can reduce costs by decreasing the cost per acquisition of a campaign by 50%. It can also help professionals create data-driven predictive ads, help sort real-time bidding of ads, and target the ad to the right audience.

7. Detecting Fake News

When a conversation about advertising is brought up, it is usually accompanied by fake news and bias online. One way to tackle fake news is by using deep learning to customize the news categories according to the user's preferences. One can filter out news as per geographical and economic parameters; Neural networks enable the development of classifiers that can help in the detection and removal of fake news and biased news from a user's feed.

8. Robotics

As deep learning algorithms mimic the human brain, this technology is used in robotics to train the machine to perform human-like tasks. These robots use real-time updates to detect obstacles in their way and pre-plan their route. Deep learning can also be used in machines that carry goods around hospitals, factories, warehouses, etc.

9. Adding Color To Images

Deep learning-based image colorization means converting a grayscale image to a colored one. ChromaGAN is a picture colorization model in which a generative network is framed in an opposing model that learns to color by adding perceptual and semantic understanding of class distribution and color.

10. Image Captioning

Captioning an image takes computer vision to understand the meaning of the image and a language model to turn that understanding into legible words. Recurrent neural networks like LSTM are used to convert labels into comprehensible sentences. Tech giant Microsoft has built a captioning bot where a user can upload either an image or a URL of the image and the result will be a textual representation of that image.