And then there are chatbots that have characteristics of both models. ReviewPro’s chatbot, for example, is powered by AI while also using a rule-based structure. This means that it will ask follow-up questions to the guest but uses AI to understand the intent of the guest so it can skip redundant questions.
As the CTO of ReviewPro, Dimitry Lvovsky, explains, “What makes our type of solution interesting is that by deploying both types together, even if you are in a “flow” the bot still uses AI to understand what you want. So, the bot may ask you, when do plan to check-in? The guest may reply three days from now, and the bot would understand what day that is. Similarly, the user might just reply, what time is breakfast – the bot would understand that the guest now has a different intent and ask if they want information rather than completing the booking.”
What are Chatbots?
Messaging platforms are at their prime, and the use of Chatbots is the new trend. Presented as the new disruptive technology for the communication industry, “Chatbots” has become the new buzzword.
In the 90’s “bots” (short for ‘robots’) were automated programs that run over the web. Some of them were running automatically while others only executed commands when they received a specific input. Chat bots were one of the first types of automated programs to hit the market under the name “bots” and were pretty popular in the 90’s, as online chat rooms knew their peak. However these bots were scripts that looked for a certain text pattern to respond online chat rooms participants with automated actions.
Output generation
Now when the AI chatbot knows what the user means, it works on presenting them with the best possible, relevant answer that matches the query.
With the help of natural language generation (NLG), the chatbot produces a written response, which is then communicated to the user.
Over time and after several millions of thousands of interactions, your conversational AI chatbot would have collected a mountain of structured data. Every chatbot interaction is either a success or failure with the user. Based on this user experience, the AI chatbot relearns and refines its responses the next time. This information helps the chatbot stock up on multiple intents and utterances to serve as the basis for its perpetual learning. It helps you train your chatbot to render better, more satisfactory user experiences.
In conclusion
For many applications, the chatbot is connected to the database. The database is utilized to sustain the chatbot and provide appropriate responses to every user. NLP can translate human language into data information with a blend of text and patterns that can be useful to discover applicable responses. There are NLP applications, programming interfaces, and services that are utilized to develop chatbots. And make it possible for all sort of businesses – small, medium or large-scale industries. The primary point here is that smart bots can help increase the customer base by enhancing the customer support services, thereby helping to increase sales.
The First AI Winter
Soon after, in the 1980s, the AI research resumed.
However, from 1974 to 1980, the research hibernation period was no less remarkable and was called “The Winter of AI.”