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Distinguishing between chatbots and conversational AI

chatbots and conversational AI

There exists confusion when it comes to differentiating between chatbots and conversational .  Most people, use chatbots and conversational interchangeably to mean the same thing. However, these two solutions have vast differences in the business world based on their origin and specific purposes. However, both tools have emerged from technological innovation and advancement in recent years. 

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SwissCognitive, AI, Artificial Intelligence, Bots, CDO, CIO, CI, Cognitive Computing, Deep Learning, IoT, Machine Learning, NLP, Robot, Virtual reality, learningChatbots operate based on the limited and predetermined flow that can activate a psychotherapist’s conversation using a script. Chatbots usually carry out conversations by deploying a specific pattern that gives users an illusion of understanding on the part of the program. However, chatbots do not have an inbuilt framework for contextualizing events. Chatbots entail communication between humans and machines. In this case, humans tend to believe they are conversing with a fellow human. Chatbots are usually ideal for small-medium businesses or even big organizations that seek to fulfill a single task. 

On the other hand, conversational operates in a completely opposite manner compared to chatbots. For chatbots, they follow a rigid structure with a predetermined conversational flow while conversational is flexible. To meet its target, conversational is powered by Natural Language Processing, Natural Language Understanding, Machine Learning, Deep Learning, and Predictive Analytics. The result is always more dynamic and less constrained. 

The standard make-up of the conversational usually entails an automatic recognizer (ASR), a spoken language understanding (SLU) module, a dialog manager (DM), a natural language generator (NLG), and a text-to- (TTS) synthesizer. In this case, the ASR puts into account raw audio and text signals and puts them to words later relayed to the SLU. The SLU then captures the underlying semantics based on a specified sequence of words. 

The next step entails analyzing and identifying the dialog domain, then the DM interacts with users and assists them. It also overviews the needed semantic representation and decides the system’s action. The DM also has access to the knowledge database to retrieve the information the user is seeking. Furthermore, the DM also comprises the dialog state tracking and policy selection to help in making robust decisions. 

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