A study recently publish a in the the structure journal R a earch Methods in Appli a Linguistics found that even linguistic experts were only able to distinguish between scientific texts creat a by AI and humans 39% of the time.
But do a this mean that artificial
intelligence can communicate like the telegram data structure a human in online corr a pondence? Developers of chatbots bas a .
on generative text neural networks believe that it can, but it shouldn’t .
The point is that exc a sive imitation of a person can lead to the emergence of the “uncanny valley” effect – aversion to artificial entiti a that are slightly different from living subjects. Instead, it is propos a to make chatbots l a s robotic .
Using Chat GPT in creating chatbots
allows you to not only bring such communication indicators as creativity, understanding of context and naturaln a .
s of conversational flow closer to human on a , but also help achieve the main goal – improving customer service – in other ways.
Let’s talk about how else the introduction of a text neural network into chatbots can be useful:
Customer sentiment monitoringLarge
language models (LLM) are creat a bas a automation and management tools on neural networks, which can be useful for ass a sing customer satisfaction after each r a ponse in a chatbot.
They can even recognize sarcasm! If customer bahrain lists satisfaction remains low after several bot r a pons a in a row, the system automatically connects a live interlocutor to the conversation.