Although not a new concept, Artificial Intelligence has gained significant popularity lately. The term Artificial Intelligence was coined back in the 50s, and the research around it was mostly confined within the research institution. Some of the earliest industrial application of artificial intelligence was around optical character recognition, expert systems and industrial robots, which were mostly developed as a rule-based AI system or a very basic machine learning.
What makes artificial intelligence interesting is the machine learning, a subfield within AI that deals with pattern learning. An evolution of statistical methods, machine learning, is a technique to teach computers to understand pattern within a given set of data so it can infer its generalized answer to any future input.
Another notable method within AI is a neural network, a technique within machine learning that is derived from the understanding of how the human brain works. Although researchers haven’t been fully able to mimic the human brain, functioning of individual neurons has been the basis of development of neural network techniques.
Recently, deep learning has been getting mainstream popularity compared to other machine learning techniques. Deep learning incorporates multiple hidden neural network and requires a large set of data to train it’s learnability. Information systems are harnessing massive amount of data along with a parallel development in the distributed software infrastructure to handle such data has led to the advancement in deep learning. Moreover, development of systems like CUDA platform that leverages GPU compute power, Tensor Processing Units, etc. have contributed significantly to deep learning progress as well.
Although there are various classes of Neural Networks based on how networks are arranged, this article focuses on Recurrent Neural Network in particular and its application in the industry. NVIDIA mentions RNNs as recurrent because they are an extension of regular artificial neural networks that add connections feeding the hidden layers of the neural network back into themselves – these are called recurrent connections. In other words, processing of a single consecutive data point in each network is dependent on how the previous data was processed. Given the algorithm’s dependency on consecutive input data, RNN is very useful in processing textual and speech data where each word in a sequence is dependent upon the previous word.
Without going too much into technical details, here are five main business applications of Recurrent Neural Network:
1. Text Summarization
This application is helpful to summarize content from any literature and optimize for delivery within software applications not built to render large volumes of text. For example, if a company wants to display key information from any literature within their apps or website, Text Summarization would be helpful.
2. Text Autofill or next text recommendation
Businesses looking to transform their data entry work by improving their workflow digitally can achieve faster automation through the implementation of Recurrent Neural Network.
3. Language Translation
In today’s global world, companies need to think about conducting business in various countries. Although most global professionals are well versed in English, sometimes it helps to translate the content produced by companies to a language spoken in their target market. Rather than hiring native translators to translate a massive volume of content, businesses can at least improve their translation process using Recurrent Neural Network.
4. Call Center Analysis
Customer satisfaction is the key to win and maintain customer loyalty. A customer support center plays a big role in providing the level of support to win that loyalty. Current customer metrics are measured on the output of the call rather than the call itself. Such metric quantifies what happened, however, analyzing the call itself will give businesses why the support rep succeeded in resolving their customer issue. This learning can then be reapplied to other similar scenarios or to train new support reps. So, to measure why a customer support rep succeeds, Recurrent Neural Network can be used to synthesize actual speech from the call for analysis purpose. Such synthesized speech can be then fed through a tone analysis algorithm to measure the emotion of the various parts of the conversation. This will help the business identify when the customer is angry or happy with the support rep. Although this observation can be made by simple human observation, using such tone analysis will help business quantify the results for future knowledge.
5. Digital Asset Management in Marketing
Companies are generating a lot more visual content these days. These contents range from videos and image produced by the companies, their agencies and even user-generated content from the social media. Manually tagging, classifying and captioning these contents is a cumbersome process. However, a Recurrent Neural Network in conjunction with Convolutional Neural Network can be used to build a Digital Asset Management (DAM) pipeline to classify, tag and produce captions for these visual assets.
