It’s only natural: powering conversational AI with natural language processing

Many organizations are creating unique experiences with conversational AI applications. Here’s how natural language processing (NLP) can help you reinvent the customer experience.

Natural language Using machine learning to reveal the structure and meaning of text

Organizations around the world have made AI technologies critical components in their quest to become more agile, data-driven enterprises. And having revolutionized internal operations with AI and machine learning (ML), they’re turning their attention to the customer experience.

Delivering truly conversational chatbot experiences is the goal, but for many organizations, conversational AI is still in its infancy. Research from Forrester shows that many consumers are frustrated by their experiences with chatbots, with 54% of US online consumers saying that “interacting with a chatbot will have a negative impact on [my] quality of life.”

Welcome the next-gen chatbot

While there may be a consumer backlash against substandard chatbots that replicate the rigid menu trees of the touchtone era, many organizations are doing exciting things with conversational AI that have a profound impact on people’s lives. Take, for example, the virtual companion that chats with Alzheimer’s patients about any topic they want and Nestlé India’s groundbreaking chatbot that provides personalized child-nutrition advice for parents.

Customer-focused businesses of all kinds can follow their lead, creating engaging, conversational experiences by using NLP to understand customer intent and sentiment like never before. But to run NLP workloads quickly and effectively, considerable processing power is needed to analyze and infer meaning from millions of data sources.

NLP comes of age

In the early days of conversational AI, relatively simple rule-based models were used to answer basic questions. The most advanced chatbots today, however, can do so much more and learn from every interaction. They can recognize a variety of customer intents, apply context based on historic and real-time data, and understand a broad spectrum of sentiments. And supporting all these capabilities is the latest generation of NLP technologies.

Today’s NLP combines syntactic analysis to identify what a piece of text says and semantic analysis to understand the intent and sentiment behind the text – i.e. what it really means.

Understand customer needs, improve customer experiences

By using sentiment analysis through NLP, you can design more engaging, personalized experiences based on a complete understanding of customer needs. Some organizations, for example, are using  advanced machine learning and natural language technologies to rethink how they serve the most relevant content to their customers.

Just look at Meredith Digital, a producer of content that reaches 140 million online adults in the United States every month. It needs to stay ahead of consumer trends to keep its sites relevant and engaging. So, it uses machine learning to automate and accelerate content classification across its portfolio of media properties. And it also uses sentiment analysis to monitor hot topics and emerging trends in consumer-interest areas like food and beauty.

The impact on its customers and its business is profound, as Grace Preyapongpisan, the company’s VP of Business Intelligence says: “We can better identify and respond to emerging content trends, creating more relevant, higher-impact audience experiences.”

GPU acceleration is critical

NLP involves processing huge volumes of written data—from social media, chat transcripts, feedback forms, Speech to Text (STT) files, and other sources. That’s where GPUs excel, offering the power to process more rich text in a shorter time, and improving your ability to infer meaning and sentiment.

The large scale parallel processing capabilities of NVIDIA® GPUs allow you to rapidly run huge analysis and inference workloads. With GPUs, you can conduct advanced semantic searches on millions of documents in milliseconds and query vast data sets of more than a hundred million documents in less than a second.

This level of processing power is vital to support and accelerate effective NLP initiatives. And with a powerful new partnership, delivering GPU-accelerated NLP capabilities is far simpler.

Discover the Power of Two

The processing power of NVIDIA GPUs is strengthening NLP and delivering conversational AI use cases that feel natural and intuitive. Backed by GPUs, the most advanced NLP technologies can help you realize the promise of conversational AI and deliver automated experiences that are personalized, contextual, and engaging.

And by running leading GPUs on the right cloud platform alongside powerful conversational AI services, you can make it faster, simpler, and more cost-effective to harness the transformational capabilities of conversational AI.

This is where the partnership of NVIDIA GPUs and Google Cloud comes in. With the Power of Two, you can better understand your customers and create the experiences that put your organization ahead of the pack.

Learn more about harnessing the power of NVIDIA GPUs and the Google Cloud platform at

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