Conversational AI

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Conversational AI is a combination of technologies, such as chatbots and voice assistants, that users can communicate with to receive assistance. An effective conversational AI application combines machine learning, natural language processing (NLP), and sometimes even IVR and ASR to achieve the most human-like interaction possible. 

Machine learning is leveraged in order to train the system to respond with increasingly more accuracy over time. For example, an automated satisfaction survey can reward or penalize the algorithm and train it for future scenarios. 


Machine learning is leveraged in order to train the system to respond with increasingly more accuracy over time. For example, an automated satisfaction survey can reward or penalize the algorithm and train it for future scenarios. 


Elements of Conversational AI


As previously mentioned, conversational AI combines natural language processing with machine learning. In order to foster continuous AI algorithm improvements, there is a constant feedback loop flowing between natural language processing operations and machine learning. 

There are a few key elements within both these fields that enable conversational AI to process, understand and respond to incoming data in a natural way. 


Machine Learning (ML) consists of a series of algorithms, datasets, and features that consistently improve themselves over time. As input information increases, the AI becomes progressively better at identifying patterns and making accurate predictions. 


In conjunction with machine learning, natural language processing (NLP) analyzes language within conversational AI and knows how to generate a response. Natural language processing is made up of five steps (as seen in the diagram above) that can be broken down in the following way:


  • User Input: Users provide input in the form of voice or text through a website or an app.


  • Input analysis: If text-based, the conversational AI will use natural language understanding (NLU) to interpret the meaning and understand its intentions. If voice-based, the system will use a combination of automatic speech recognition software (ASR) and NLU to decipher the data.

 

  • Dialog Management: This is where the computer decides what dialog state to provide the user with and often depends on the objective of the conversation. In other words, dialog management is what enables bots to conduct contextual communication with its users. For example: a user might order a shirt and have the bot take the order; if the user suddenly says “actually, change it to jeans” the bot will then interpret this message as a reference to the user’s previous ‘order’ and make the change. There are two main types of dialog management approaches: handcrafted and probabilistic. While the probabilistic approach comes off as more ‘human’, it is not as resilient in the conversation in comparison to the handcrafted approach (which is more robotic but also more resilient). 


  • Response Generation: A response is formulated with natural language generation (NLG), a constituent of natural language processing, and then communicates it to the user via the system output. 


  • System Output: The text will then be displayed to the user, and if voice-based, an interactive voice recognition (IVR) system is used in order to communicate the message.


Benefits of Conversational AI


  • Cost Efficiency:  A renowned byproduct of utilizing conversational AI is its ability to drive cost-efficient solutions to businesses of all sizes.
    Operating a customer service department can be costly, especially ones that work outside of office hours. Leveraging conversational interfaces can reduce costs by eliminating additional salaries and training staff while providing round-the-clock assistance via virtual assistants and/or chatbots. Furthermore, seeing as most customer support interactions are based on information retrieval, conversational AI software can take on repetitive questions while human resources are reserved for more complex customer queries



  • Scalability: Scaling resources using conversational AI software is much more effective and inexpensive than hiring and on-boarding new employees. Conversational AI can therefore facilitate and improve a business’s performance when entering a new geographical market or experiencing an unexpected peak in demand


Conversational AI Challenges  


Conversational AI only began being deployed by businesses a few years ago. The nascent stage at which this technology is currently at means that there are several challenges and pain points to take note of when deploying conversational AI. 



  • Linguistic Pain Points:  Whether the input is in text or voice format, language remains a strong pain point for conversational AI due to many dialects, background noises, slang, and spelling mistakes it must comprehend with human interactions. Among these, the heaviest hurdle the software must learn to overcome is deciphering the human factor. Things such as tone, sarcasm, and emotion need to be understood for the machine to know a user’s intent and respond appropriately. 


  • Security: When deploying conversational AI, one must do so with high-security standards in order to build trust with end-users and increase exposure over time. The reason users are sensitive when disclosing their information when interacting with conversational interfaces is due to its potential to serve as a gateway for nefarious parties online. 


Conversational AI Use Cases 


People commonly associate conversational AI applications with chatbots and voice assistants as facilitators for customer support services. Nevertheless, experts in the field view these applications as 'weak' uses of conversational AI as extensive analytics embedded within its core enables it to conduct more impressive human-like experiences. The narrow-focused performances carried out by today's bots and voice assistants are an inadequate representation of its actual capacity. 'Strong' AI is based on human-like consciousness and can resolve a broad range of complex problems.  

Despite it not being used to its full potential, conversational AI is still highly lucrative for many businesses and is used in many different ways, including:



  • Customer Support: Chatbots are transforming customer support services into efficient and pleasurable experiences by managing queries revolving around shipping, product recommendations, and other individual issues.   


  • HR Processes: Taking on a new employee can be quite costly, both from a financial and timely perspective. Now, however, tedious tasks such as training and onboarding are being automated through conversational AI applications. 


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Conversational AI
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