Learn how generative AI can be used to create human-like interactions with conversational AI and how to get started with HPE GreenLake and NVIDIA AI Enterprise.
A fire ablaze, generative AI has been engulfing headlines, as well as business strategies and initiatives. Generative AI is a subfield of artificial intelligence (AI) that uses advanced machine learning techniques to generate unique content or data, such as text, images, music, or even 3D models, from given data. It involves training AI models to understand patterns and structures within existing data and then using that knowledge to generate new original content.
These models have been trained on massive amounts of data, and work by predicting the next word or pixel to produce a creation. In terms of generating new content in the form of text, a big disruptor has been the use of Large Language Models (LLMs). LLMs have disrupted traditional machine learning by pushing the boundaries of natural language understanding and generation. They are designed to understand and generate human-like text across a wide range of languages and topics.
One stream of application is to create human-like interactions, also known as conversational AI. Conversational AI is a subset of generative AI that focuses specifically on utilizing generative models to understand and respond to user input in a conversational human like manner.
As shown in Figure 1, conversational AI can be applied in many domains, including telecommunications, financial service, healthcare, retail, and more. Enterprises want to take advantage of the billions of interactions with customers they have every day and build conversational applications to use insights from these conversations and build better products, such as customer care agent assists, virtual intelligent assistants, and digital avatars.
To be effective, these conversational applications need to enable interactions with users in many languages, understand industry-specific jargon, and respond in real time.
Stripped down to basics, a conversational AI pipeline includes speech AI and natural language processing (NLP). A speech AI system includes two main components: an automatic speech recognition (ASR) system and a text-to-speech (TTS) system. ASR is also known as speech-to-text, speech recognition, or voice recognition system. It converts the raw speech audio signal into text for processing by subsequent components. TTS is also known as speech synthesis. It turns the text into audio.
The technology behind speech AI is complex. It involves a multistep process requiring a massive amount of computing power and several deep learning models that must run in tens of milliseconds. Here are a few of the LLMs that participate in conversational AI workflows:
Conversational AI also uses NLP powered by LLMs, which includes natural language understanding to comprehend meaning and intent behind user messages or spoken language.
Natural language understanding helps the system extract relevant information, keywords, and determine the user's goals and requests. So, consider natural language understanding as one of the key features and components of conversational AI.
Another key feature or component of NLP is natural language generation. Natural language generation is the process of generating human like responses or messages based on the system's understanding of the user's input. This allows the AI to provide meaningful and contextually appropriate responses. All pulled together with dialogue management.
Conversational AI systems have mechanisms for managing multi-turn conversations that keep track of the conversational context and remember prior interactions and decide on how to respond to users based on that conversational history, bringing even more humanness to this whole process.
In summary, as displayed in Figure 2, a conversational AI pipeline begins with ASR where audio is converted to text, followed by interpretation by the NLP system, and then TTS converts the text response into human-like speech.
A more specific application of the use of conversational AI can be seen in call centers. Instead of spending several minutes documenting customer engagements and feeding information back into the system following events, AI can deliver a seamless customer support experience by using natural language inputs, leaving human agents to handle more complex issues. Enterprises can employ conversational AI recognition applications to analyze video and audio content instantly, streaming insights from every customer interaction. Streaming insights help organizations achieve better customer service outcomes and accelerate and automate daily processes, so they can respond promptly to customer needs.
For conversational AI to be effective for implementation in call centers or other use cases, specific requirements need to be met:
- The models used in each component of the conversational AI pipeline need to be trained or finetuned to each specific use case, domain specific knowledge and language.
- Once functional, the conversational AI pipeline must be efficient at making inferences in real time.
- The implementation of conversational AI must keep data privacy and security as a priority.
To meet all requirements, enterprises need a solution stack that is scalable and flexible. For model training, finetuning, and inferencing, a two-fold approach is required. From an infrastructure perspective, storage and compute need to be optimized to handle the complex AI workloads and from a framework perspective, a platform that can accelerate AI and training and inference is needed. Enterprises are wanting to implement conversational AI but just like when early humans were introduced to the flame, they don’t know how to approach this new technology and want a robust architecture.
Putting the right solution in place
HPE and NVIDIA AI Enterprise offer a comprehensive AI solution that provides an open, cloud-like platform that is hybrid by design. Enterprises can start small with a pilot project and scale up to production needs for conversational AI using HPE GreenLake and NVIDA AI Enterprise for an end-to-end scalable generative AI solution. This solution offers a simplified cloud experience for on-premises generative AI solution and can integrate into existing workloads.
Now let’s see how HPE GreenLake and NVIDA AI Enterprise end-to-end scalable generative AI solution meets the specific requirements in the use case of implementing conversational AI in call centers.
From an infrastructure perspective, during training and finetuning the latest NVIDIA L40s GPUs can significantly speed up the process of updating model parameters based on large datasets. During inference, GPUs are used to make predictions or generate responses in real-time.
HPE GreenLake provides a simplified, secure, self-service hybrid cloud running on fully managed HPE infrastructure which includes NVIDA certified GPU optimized compute. These GPU-optimized compute servers need to be fed data quickly to maximize performance during model training, finetuning, and inference. HPE GreenLake for File is an enterprise-grade, scale-out file storage that meets the performance demands of the AI lifecycle of model training, finetuning, and inference. From a framework perspective, NVIDIA AI Enterprise offers Riva, a speech AI toolkit for customizing models and for accelerating model development, training, and deployment of the HPE Machine Learning Development Environment (MLDE) and HPE Machine Learning Data Management (MLDM).
Deploying conversational AI on-premises helps ensure regulatory compliance and safeguards sensitive information and data privacy. HPE GreenLake and NVIDA AI Enterprise take the complexity of a highly integrated stack required for implementing a conversational AI solution and helps make it seamless with flexibility in terms of scaling. Deploy AI with a trusted model, robust architecture, and harness the power of fire 2.0.
Learn more
Read the white paper: Accelerating Conversational AI with HPE GreenLake and NVIDIA AI Enterprise for an end-to-end scalable Generative AI solution
Denise is on the worldwide Hybrid Cloud team at HPE. She is passionate about technology and her currrent focus is on Big Data, analytics, and AI.
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