In Artificial Intelligence (AI), intents play an important role in understanding the meaning of what a person is saying. After classifying the intent, the chatbot can then provide an appropriate response that is relevant and personalised. 

An intent refers to what a person has in mind when interacting with a chatbot or virtual agent. Essentially, it represents what the customer wants to do or enquire about. 

When someone interacts with an AI system, they express their intentions through language. When a user sends a message, the AI uses Natural Language Understanding (NLU) to analyse the content. It then responds accordingly based on the intent behind the message. 

On the otherhand, entities are the key pieces of data that provide details about the intent. 

For example, a customer might say: “I will pay my account this Friday and will pay forty pounds.” In this example: 

The intent – Account Payment

The entities – Date: “this Friday” i.e. 1 December 2023 and Amount: £40 

Here's a breakdown of the key aspects of intents in AI for chatbots: 

1. Intent Classification: The process of determining the user's intent from their input. AI systems use various techniques, including Natural Language Processing (NLP), Machine Learning, and pattern recognition, to classify intents. A single message can sometimes match multiple intents and  if it is ambiguous, the system picks the most likely intents. 
    
2. Intent Training: The process of providing training data to an AI system for it learn to identify different intents. This involves creating a collection of user imputs, using real-life examples, and associating them with their corresponding intents.
     
3. Intent Management: The ongoing process of fine-tuning intent definitions as the AI system engages with customers. As users interact with a system, their messages are added to an intent training dataset. This allows the system to constantly improve at identifying intents over time based on real usage patterns.

To sum up, intents are categories that help AI systems understand user goals and have conversations with them by looking beyond individual words to infer underlying purpose and context. This makes them important for building effective conversational AI chatbots. By understanding intents, AI systems can achieve better user engagement, provide more personalised experiences, and ultimately fulfill their intended purpose of helping users. 

To learn more, see:  

• A Look Under the Hood at the AI Tech that Powers Webio’s Conversational Customer Engagement 
• AI Chatbots: The Three Layers of AI Automation