NICE launches a conversational CX with ChatGPT-enabled CXone
NICE, the artificial intelligence (AI)-powered self-service and agent-assisted customer experience (CX) software provider, has announced its integration of CXone Expert with OpenAI's generative modelling used in ChatGPT.
CXone Expert is the industry's leading cloud-native customer service knowledge management solution, providing effortless, faster self-service that delivers accurate answers for resolving customer issues. With this integration, organisations can create CX-rich, human-like conversational consumer experiences without engaging agents.
CXone Expert leverages NICE Enlighten AI models, custom-built for CX, and organisation-specific data to create unique conversational AI models. The integration with OpenAI's generative modelling ensures that the resulting answers to consumer self-service inquiries are immediate and highly accurate and that they are also semantically constructed in a human-friendly manner, optimised for consumer understanding.
Combining the strengths of CXone Expert, with its easy-to-use content retrieval and conversational search capabilities, and ChatGPT technology, with its revolutionary approach to AI-driven natural language conversations, NICE is ushering in a new era of CX, where consumers are immediately routed to the correct answers with no need for transfers or call-backs while creating exceptional self-service experiences that feel familiarly human.
CXone has a Virtual Agent Hub which simplifies the deployment of conversational bots and virtual agents for customer self-service. The choice is customers. They can use advanced IVA (SmartAssist powered by Amelia) or any third-party partners, including Google, Microsoft, and Amazon. This way, customers can orchestrate unique conversational self-service experiences with complete control and continuous contact flow for lower costs and better customer experiences via voice and chat.
“This ground-breaking integration between CXone Expert and ChatGPT technology is a game changer for CX. By combining NICE’s deep CX-Industry specific Enlighten AI models with the innovative Conversational AI capabilities of OpenAI's generative modelling, we are evolving self-service to its inevitable next level, providing brands with powerful new capabilities to enhance customer experience, create more efficient customer interactions, and boost their brand engagement in a way that feels natural, friendly, and human,” says Barry Cooper, President, CX Division, NICE, highlighted the advantages of the new development.
Chat Generative Pre-Trained Transformer, or ChatGPT, is a chatbot launched by the artificial intelligence research and deployment company, OpenAI in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and is fine-tuned (an approach to transfer learning) with both supervised and reinforcement learning techniques.
“We’ve trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer follow-up questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests. ChatGPT is a sibling model to InstructGPT, which is trained to follow an instruction in a prompt and provide a detailed response,” says OpenAI.
“We trained an initial model using supervised fine-tuning: human AI trainers provided conversations in which they played both sides—the user and an AI assistant. We gave the trainers access to model-written suggestions to help them compose their responses. We mixed this new dialogue dataset with the InstructGPT dataset, which we transformed into a dialogue format.”
“To create a reward model for reinforcement learning, we needed to collect comparison data, which consisted of two or more model responses ranked by quality. To collect this data, we took conversations that AI trainers had with the chatbot. We randomly selected a model-written message, sampled several alternative completions, and had AI trainers rank them. Using these reward models, we can fine-tune the model using Proximal Policy Optimization. We performed several iterations of this process,” explains OpenAI.