3 Applications of RNNs in the Current Business Landscape
Due to the power embedded in recurrent neural networks (RNNs), these deep learning models are currently being implemented in a wide range of varying business applications. These neural networks can be trained in accordance with sequential data, or data in which a particular point within the dataset will be dependent on the other numerous points within the said dataset. To illustrate this point further, weather predictions are a common example of the utilization of sequential data, as all weather observations will correspond to a particular time of the day. All this being said, 3 common applications of RNNs in the business world today include machine translation, speech recognition, and call center analysis.
Machine translation
While translation has historically been a painstaking and tedious process that often relied on highly trained and experienced interpreters, advents in artificial intelligence and deep learning have led to the creation of software programs that can translate words and phrases from one language into another in a matter of minutes. Likewise, many software applications that enable users to automatically translate files will leverage recurrent neural networks to get the job done. More specifically, the input for machine translation applications that utilize an RNN will be the source language of the translation, while the output will be the target language that the user desires to translate.
Going back to the concept of sequential data, human languages are another common example of such data, as the words that a person says when communicating with another person will obviously be dependent on the words that both they and other the other person have stated previously in the conversation. As such, the feedback loops that are contained within recurrent neural networks allow software developers to train their deep learning algorithms to recognize the hundreds of syllables, words, and sentences that comprise the dozens of human languages that are spoken worldwide, making effective and accurate translation a tangible reality.
Speech recognition
In keeping with the complexity and nuance of the human language, recurrent neural networks are also commonly applied in software programs that are used to recognize human speech patterns. Perhaps the most widely known examples of speech recognition software within the business landscape today are the popular AI assistants that have been produced by multinational corporations such as Apple, Amazon, and Microsoft in recent years. However, the ways in which an RNN will be utilized to create a speech recognition software program will vary from those that are used to facilitate machine translation.
For example, instead of training a deep learning algorithm to recognize specific words or phrases within a particular language, RNNs that are used to create speech recognition software programs will be trained to recognize phonetic segments within sound waves. To this point, the inputs within these deep learning models will take the form of acoustic signals or phenomes, while the outputs for such models will take the form of patterns of phonetic segments that are put together in a logical and singular sequence. Through these technological functions, consumers can tell Apple’s Siri AI assistant to pause their favorite song, dial the phone number of their close friend, or close their online email account, among other things.
Call center analysis
As businesses continue to take advantage of technological services in order to gain a better understanding of the respective needs and desires of their customers, another common application of recurrent neural networks in the business world is in call center analysis. To take it a step further, the application of the deep learning models in regard to the analysis of phone calls is also one of the most common uses for RNNs within the field of audio processing in general. Due to the global nature of many businesses and corporations such as Walmart, Mcdonald’s, and Apple, these businesses must ensure that their call centers are able to provide customers with the assistance they need in a relatively short amount of time.
To achieve this goal, software developers can create RNNs that are trained to recognize specific patterns within calls that are made to a particular customer service center, such as the emotions in a customer’s voice, as well as the sentiments that a customer expresses while speaking with a representative. Through these metrics, business organizations can gain insight into when a customer is satisfied with the service they have received, as well as instances where a customer has faced issues in relation to this service. Moreover, these metrics can also be implemented into other call centers that are in the possession of a particular business organization, allowing these organizations to serve their customers more efficiently.
Artificial intelligence and deep learning have completed changed the ways in which consumers and businesses alike interact with the products and services that are currently available in markets around the world. Due to the immense data analytical capabilities of artificial neural networks, any problem that involves large amounts of information can be solved in new and intuitive ways, giving people a new way to approach business decisions. With all this being said, machine translation, speech recognition software, and call center analysis are just three ways in which recurrent neural networks are being used to create new business products, in addition to many others that are also being used.
