Generative AI Frequently Used Terms

The landscape of business is now covered with the hype of AI. Here’s some definitions to help.

Generative artificial intelligence (Gen AI):

Gen AI works by training machines to recognize patterns and make correct predictions.

There are two basic types of models:  

1. Discriminative AI:
   Discriminative AI models are designed to classify and categorize data based on their training data sets. These models are often used for predicting the next word in a sentence, classifying information, and following predetermined steps. The output of discriminative AI models is selected from the existing options provided in workflows or datasets. These models are commonly used for tasks such as routing calls, sentiment analysis, chatbot interactions, interactive voice response (IVR) natural language understanding (NLU), and spam filtering. 
2. Generative AI:
   Generative AI models can combine reasoning with content creation (text, voice, or images) to convey accurate understanding and results to the end user. These modules are commonly used for various applications, such as IVR NLU, generative agents or assistants for voice and chat, and generative analysts for voice, text, and data. 

ReAct:

ReAct is combining reasoning with action. One of the most innovative uses of large language models involves their combination with external systems and tools, a process termed ReAct (Reason + Act). For instance, if a model predicts the next action as “search for the current exchange rate,” it can invoke a tool to perform this search, integrating the results into its next response. This ability to interact with and incorporate external data is a significant advancement in AI applications. 

Generative Agent:

Generative Agent encompasses various roles within contact centers, including generative analysts, generative assistants, and generative agents.  

• Generative analysts provide insights and analysis with limited or no interaction with the end user, such as improving interaction routing or validating interaction primary and secondary intents.  
• Generative assistants help employees and contact center staff with daily tasks and support human-to-human interactions.  
• Generative agents directly interact and maintain an entire interaction from beginning to end with human end-users, figuring out the intent of an interaction and carrying out process-based tasks to support user requests. 

Reflected Intelligence (RI):

RI is a solution that ensures that enterprise generative AI fulfills its promise of transforming business operations. RI goes beyond technical proficiency by mirroring an organization’s human intelligence and capturing employee knowledge, communication styles, and company culture. By understanding the customer and enterprise context, RI customizes responses, fosters trust, and drives adoption.  

Adversarial Generative AI:

Adversarial generative AI is used in real-time to fact-check the generative outputs before they are presented during an interaction. This adversarial generative AI would act as a discerning reviewer or “critic agent,” identifying any potential biases, inaccuracies, or hallucinations produced, thus enhancing the overall accuracy and reliability of the system. 

Human-in-the-Loop (HITL):

HITL is a blend of supervised machine learning and active learning where humans are involved in the development, training, testing, and tuning of an algorithm. This involves presenting the captured data to human experts for validation before the generative AI uses it. Human reviewers play a crucial role in areas like data annotation, content moderation, model training, and quality assurance.