The adoption of machine learning and subsequent development of neural network applications has changed the way we perceive information from a business standpoint. If previously, the information was a commodity with a value limited to its instantly accessible features, now it is a resource the value of which depends on one’s skill to interpret it - the ability to make the most out of the available information.
The information can be used:
- Determine patterns and other significant features present in data
- Extract relevant insights
- Implement them into the business operation
- Predict future development
This process requires complex systems that consist of multiple layers of algorithms, that together construct a network inspired by the way the human brain works, hence its name - neural networks.
In this article, we will look at one of the most prominent applications of neural networks - recurrent neural networks and explain where and why it is applied and what kind of benefits it brings to the business.
A Rcurrent Neural Network is a type of artificial deep learning neural network designed to process sequential data and recognize patterns in it (that’s where the term “recurrent” comes from).
The primary intention behind implementing RNN neural network is to produce an output based on input from a particular perspective.
The core concepts behind RNN are sequences and vectors. Let’s look at both:
- Vector is an abstract representation of raw data that reiterates its meaning into a comprehensive form for the machine. It is a kind of text-to-machine translation of data.
- The sequence can be described as a collection of data points with some defined order (usually, it is time-based, there can also be other specific criteria involved). An example of sequence can be time-series stock market data - a single point shows the current price while its sequence over a certain period shows the permutations of the cost.
Unlike other types of neural networks that process data straight, where each element is processed independently of the others, recurrent neural networks keep in mind the relations between different segments of data, in more general terms, context.
Given the fact that understanding the context is critical in the perception of information of any kind, this makes recurrent neural networks extremely efficient at recognizing and generating data based on patterns put into a specific context.
In essence, RNN is the network with contextual loops that enable the persistent processing of every element of the sequence with the output building upon the previous computations, which in other words, means Recurrent Neural Network enables making sense of data.
How Does Recurrent Neural Network work?
Just like traditional Artificial Neural Networks, RNN consists of nodes with three distinct layers representing different stages of the operation.
- The nodes represent the “Neurons” of the network.
- The neurons are spread over the temporal scale (i.e., sequence) separated into three layers.
The layers are:
- Input layer represents information to be processed;
- A hidden layer represents the algorithms at work;
- Output layer shows the result of the operation;
The hidden layer contains a temporal loop that enables the algorithm not only to produce an output but to feed it back to itself.
This means the neurons have a feature that can be compared to short-term memory. The presence of the sequence makes them “remember” the state (i.e., context) of the previous neuron and pass that information to themselves in the “future” to further analyze data.
Overall, the RNN neural network operation can be one of the three types:
- One input to multiple outputs - as in image recognition, image described with words;
- Several contributions to one output - as in sentiment analysis, where the text is interpreted as positive or negative;
- Many to many - as in machine translation, where the word of the text is translated according to the context they represent as a whole;
The key algorithms behind RNN are:
- Backpropagation Through Time to classify sequential input- linking one-time step to the next
- Vanishing/Exploding gradients - to preserve the accuracy of the results
- Long Short-Term Memory Units - to recognize the sequences in the data
Generating text with recurrent neural networks is probably the most straightforward way of applying RNN in the context of the business operation.
From a business standpoint, text generation is valuable as a means for streamlining the workflow and minimizing the routine.
Natural language generation relies on Recurrent Neural Networks predictive algorithms. Since the language is sequentially organized with grammar and bound into cohesion with semantics - it is relatively easy to train a model to produce generic text documents for multiple purposes.
Let’s look at the most common:
- Text summarization - the process involves condensing the original text into a distillation of critical points and its subsequent reiteration into a cohesive summary. Summarization is used in project management to quickly onboard new members and keep an eye on the progress in general. This approach is also used to create news digests and streamline the news article production pipeline.
- Document generation - commonly used in banking and insurance to create custom forms based upon templates adapted for the specific client with relevant information.
- Report Generation - in this case, text generation serves as a form of data visualization. Except, instead of turning data into bars and charts, and graphs, the text is transformed into a formatted document with template sentences covering key points. Here’s an example of this kind of report: “There were 100 visitors on-site during 24 hour period, which is two visitors more compared with the previous 24 hour period. Twenty-five visitors came from Facebook, 10 of which bounced off instantly, while the other 15 made from 5 to 20 clicks on the following page”.
- Conversational Interfaces and chatbots are amongst the most prominent casual uses of text generation. In this case, the algorithm is trained on the knowledge base combined with the behavioral intent scenarios. For example, a lead generation scenario is designed to gather information about the potential client, while a customer support scenario is designed to assist customers with product use. In addition to text generation, Conversational UI also requires a sentiment analysis component to correctly dissect the input message (more on that later).
Machine translation is another field where RNN is widely applied due to its capability to determine the context of the message.
Here’s why - high-quality translation can be a bridge towards the expansion of the foreign language market. In a way, translated content can be considered as a broad form of service personalization.
From a technical standpoint, it seems like machine translation operation is a mere substitution of words representing certain concepts with the equivalent terms in the other language.
TВшуhe languages tend to have different structures of the sentences and modes of expression of the concepts, which makes it impossible to translate the message behind the words by deciphering the words. Instead, a machine translation algorithm needs to understand the meaning of the news first and then match it with the appropriate words.
These days, the most prominent machine translation application is Google Translate. Also, there are numerous custom recurrent neural network applications used to localize content by various platforms. Just look at eCommerce platforms like Amazon, AliExpress, and eBay. They all use machine translation to adapt content like product cards and helps with the efficiency of the search results.
Humans tend to think visually and have an extensive visual shorthand reference board that helps them to navigate in the world. Until recently, this peculiar feature of the human mind was not taken into consideration when it comes to customer services. Now it’s a full-fledged feature commonly used in a variety of fields, such as search engines, eCommerce stores, and OCR apps.
Image recognition is one of the major points of computer vision. It is also the most accessible form of RNN to explain.
At its core, the algorithm is designed to recognize one unit of input (the image) into multiple groups of output (the description of the image).
The image recognition framework includes
- A convolutional neural network that processes the image and recognizes the features of the pictures,
- Recurrent neural networks that use the known features to make sense of the image and put together a cohesive description.
The benefits of image recognition for business are obvious - it is a streamlining tool that makes it easier for the customer to operate with the service, find relevant images, navigate through information, and make purchases.
The most prominent industries for image recognition are Search engines, eCommerce, Social Media.
Let’s look at them closer.
- Search engines are the basic application of visual search. Google, Bing, and DuckDuckGo are the most prominent examples of recurrent neural network image recognition. The goal is to images that fit the input query or find images that look like an input image. To do that, the image is recognized and described. The resulting information is then used to find relevant search matches.
- In the case of eCommerce, image recognition is used for object detection and visual search purposes. The goal is to improve the product database and make it easier to navigate. In addition to that, visual search contributes to the product recommendation and consequently to the service personalization. The way Amazon and AliExpress use visual search results in vast streamlining of the user journey and better engagement with more possibilities of further purchases.
- In the cases of Social media like Facebook or Instagram, the primary use case of image recognition is face recognition. The difference between face recognition and basic image recognition is an additional layer of processing. There is a general recognition of the shape of the face, and then there is the matching of the unique credentials of the individual face based on available samples. The same principle is also used for transformative filters in photo editing applications.
The adoption of conversation interfaces is growing with each passing day. It is easy to see why - it is a more practical way of doing things, one step further for machines and humans talking in the same language.
Virtual assistants like Alexa or Siri are becoming commonplace in everyday life, and the majority of eCommerce marketplaces and company websites integrate chatbots that can help users with their causes within a couple of casually formulated phrases.
The technology that brings them together is speech recognition with deep recurrent neural networks.
From a technical standpoint, Speech (or sound in general) recognition and image recognition have a lot in common. The basic framework of the algorithm is more or less the same.
The difference is in the way the sound is recognized. Unlike visual information, where shapes of the object are more or less constant, sound knowledge has an additional layer of the performance. This makes recognition more of an approximation based on a broad sample base.
Here's how it works:
- The input information is first processed and recognized through the convolutional network. The result is a varied collection of input sound waves.
- The information contained in the sound wave is then classified by intent, key credentials (basically, keywords related to the query)
- Then input sound waves are recognized into phonetic segments and subsequently pieced together into cohesive words via RNN application. The result is a mosaic of phonetic segments seamlessly put together into a singular whole.
Just like image recognition, speech recognition is first and foremost, the tool to streamline the workflow and make it more comfortable for all categories of users - from tech-savvy ones to novices.
Let's look at the most prominent applications of speech recognition RNN:
- Conversational UI is the biggest field of use for speech recognition these days. This kind of UI can be designed for a certain purpose, such as customer support (with custom generated responses from the knowledge base) and service navigation (with customized explanations of how to use certain features of the service or where to find certain kinds of content). They can also be more action-oriented organizers integrated with other applications (like Google Assistant);
- Chatbots are smaller relatives of fully-fledged Conversational interfaces. Their main purpose is to provide relevant information. Such applications can be used on-site and also on social networks like Facebook. In addition to providing information, chatbots can be used to generate leads and initiate the start of the communication (for example, such a service is provided by Hubspot marketing platform).
- Speech-to-text applications. Sound is another medium where content marketing can thrive. Due to a variety of reasons, not every user has time to read a blog post from start to finish, but they are likely to listen to it. However, recording read-outs with voice actors can be a bit too much on the budget. Hopefully, modern speech-to-text applications are capable of doing a serviceable and cost-effective job without calling much attention to their mechanistic nature. Such claims have sample banks with phonetic segments performed in different languages that are arranged in the form of the input text. Blogging platforms like Medium are currently trying out these features, and many separate services provide speech-to-text transformations, such as SpeechNote and VoiceNotebook.
If you want to read more about Conversational UI, we have an article about it right here.
Navigating in the vast spaces of information is one of the major requirements in the data-driven world. As one of the premier recurrent neural network examples, semantic search is one of the tools that make it easier and much more productive. In addition to that, semantic search simplifies the continuous updates and revisions of the knowledge base.
These days, semantic search is widely used in a variety of fields that:
- involve high turnaround of sensitive information or vast knowledge bases;
- require accessibility and speed to provide a decent level of workflow efficiency.
Here’s how semantic search RNN application works:
- The input message is analyzed for context. The process involves - feature extraction and context recognition.
- The result is a deconstruction of the input message to its moving parts. In addition to that, the algorithm looks for the related queries and checks them for relevance to the current query.
- Then, the processed input is checked and matched with the available knowledge base.
- The matches are presented as output results.
Semantic Search is commonly used in the following fields:
- Customer support - semantic search is to navigate the product/service knowledge base and also customer cards. You can also read our case study for the project that involves the usage of a semantic search for customer support.
- Banking - in this case, semantic search is used to navigate through customer documents and double-check the validity of the proceedings at each step. It is also one of the tools for fraud detection when it comes to document or handwriting fraud.
- Project documentation - SS is used to navigate through documents and simplify access to the information. In addition to that, the Semantic search is often used to implement changes or corrections into a large amount of documentation quickly.
- Employee Onboarding and general Q&A - in this case, semantic search makes it easier to understand the ins and outs of the organization for the newbies and turns the knowledge base into an easily accessible reference tool.
- Search engines like Google and Amazon are amongst the most progressive types of semantic search. In this case, SS also involves a web scraper that looks for the relevant results. The input query is processed as usual, but it is also matched with the standard search patterns and more specific criteria such as corresponding region, language or type of content (depending on the query). In addition to that, the resulting information contributes to further service personalization.
Check Out MSP Case Study: How Semantic Search Can Improve Customer Support
RNN Text Classification - Sentiment Analysis
Natural Language Processing is one of the core fields for Recurrent Neural Network applications due to its sheer practicality. A large chunk of business intelligence from the internet is presented in natural language form and because of that RNN are widely used in various text analytics applications. The most prominent field of recurrent neural network natural language processing is sentiment analysis.
Sentiment analysis is one of the most exciting applications of recurrent neural networks. The reason for that is simple - versatility.
Here’s why - RNN can be applied to a wide variety of different aspects of the RNN sentiment analysis operation.
- The identification of opinion is usually relegated to convolutional
- Polarity recognition is an example of multiple inputs gathered into one output. The algorithm processes the message and determines
- Subject recognition is an example of various data to multiple outputs. This approach uses the capabilities of the Recurrent network to its fullest.
As such, RNN applications can gather vast amounts of diverse data that will bring more clarity regarding the perception of the product and will undoubtedly contribute to the decision-making process.
There are five major use cases for recurrent neural network sentiment analysis. Let’s take a closer look:
- Brand Management - sentiment analysis is used to track the perception of the general perception of the brand by customers from different audience segments. Subsequently, it is used to analyze the specific aspects of the perception - find patterns of interest and use it for the benefit of the business operation.
- Market Research - in this case, sentiment analysis is used to collect information regarding specific aspects of the market (use of technology, audience reaction, and involvements) across various platforms.
- Product Analytics - in this case, sentiment analysis is used to manage and analyze all sorts of customer feedback regarding the product or its specific aspects to plan further improvements.
- In the case of customer Support, sentiment analysis is used to analyze the feedback and manage the support operation. You get an intent analysis of the customer (i.e., what kind of help he needs), and then you get an insight into the customer’s opinion.
- Voice of the customer analysis uses SA to define and specify target audience segments - this includes customer’s wants and needs, expectations from the product, and so on.
If you want to read more about Sentiment analysis - we have an article describing the technology itself and also a section detailing its business use.
The lion’s share of fraudulent activities on the internet is performed via automated algorithms with clearly distinguishable patterns. In addition to that, traditional fraud like handwriting faking is widespread when it comes to document fraud.
In both cases, the recurrent neural network framework can be a powerful weapon against fraud of all walks, which is good in terms of effective budget spending and money-making.
- Data consists of pattern sequences that can be explored and assessed.
- This enables the algorithm to assume what may come next and determine the probability of a particular turn of events.
- On the other hand, pattern analysis enables to identify of anomalies in the behavior of the entities,
Overall, Fraud Prevention relies on predictive algorithms to expose illegal activity.
- In the case of ad fraud, RNN is used to determine suspicious /abnormal behavioral patterns.
- In the case of spam detection, RNN applies NLP tools to expose general patterns and subsequently block the message.
- In the case of ad fraud bot detection, recurrent neural network anomaly detection is used to identify suspiciously generic behavior of the supposed user and take him out of the analytics.
In a way, recurrent neural network stock prediction is one of the purest representations of RNN applications. It is all tweaking numbers to understand what the next figure might be.
The critical term is time series prediction, which is a representation of the number figure fluctuation or transformation over time. Apps like Stock Market Sensei use this approach.
The transformation includes a specific criterion that affected the changes (for example, the connection of the special price to the other expenses). The combination of the elements above is then taken into consideration upon calculation of the predictions.
The predictions themselves range by probability from the most to the least possible from the available data. As a result, the stock market trader gets more solid grounds for decision making and reduces the majority of risks.
Recurrent Neural Networks stand at the foundation of the modern-day marvels of artificial intelligence. They provide solid foundations for artificial intelligence applications to be more efficient, flexible in their accessibility, and most importantly, more convenient to use.
On the other hand, the results of recurrent neural network work show the real value of the information in this day and age. They show how many things can be extracted out of data and what this data can create in return. And this is incredibly inspiring.
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