Why conversational AI is now in prime time

Conversational artificial intelligence serves as the interface between a person and a computer. And therefore organizations and companies establish interesting two-way interactions. This technology is commonly associated with call centers and virtual assistants / chatbots, although it can be applied in virtually any vertical sector.

Thanks to machine learning and advances in artificial intelligence in the last two years, conversational artificial intelligence has grown beyond chatbots to include a variety of use cases.

Conversational AI has been around for several years, but the technology was only ready for a lab. Today, however, thanks to advances in AI and machine learning models, conversational AI is poised to enter the corporate world, especially in the area of ​​customer experience.

Earlier this past November, a panel of experts from T-Mobile, RingCentral and Hugging Face gathered at the NVIDIA 2021 GTC conference to discuss how conversational AI has improved their businesses and share the trends shaping the future of this emerging technology. . At this conference, NVIDIA also introduced Riva Custom Voice, a new set of tools that can be used to create custom voices with just 30 minutes of voice recording data.

Innovation around text-to-speech and data-to-speech “will transform the way virtual assistants and chatbots connect and respond,” said Kari Briski, vice president of software product management for AI / HPC ( High Performance Computing) from NVIDIA. There is a great opportunity to use the data to create new models of conversational AI that take into account people’s accents and different audio environments, such as noisy cafes and outdoor sporting events.

AI is capable of learning by itself

“Moving forward, we will see AI defining its own data,” said Prashant Kukde, assistant vice president of conversational AI at RingCentral. For example, AI could act as a real-time background filter to remove an accent when a non-native speaker speaks. At the same time, the person on the other end of the phone would hear the familiar accent. This two-way conversational AI concept is just one example of innovation in this area, Kukde said.

RingCentral is currently focused on applying artificial intelligence to the spontaneous conversations typically found in virtual meetings. The Unified Communications as a Service (UCaaS) provider is integrating conversational artificial intelligence into its existing product portfolio. Recently, it launched a new automated summarization feature that generates speech-to-text meeting summaries to provide attendees with a better experience and, as a result, improve productivity.

Why it should be present in all contact centers

T-Mobile’s conversational artificial intelligence implementations range from helping T-Mobile employees to customers living out of town. T-Mobile uses artificial intelligence in its contact centers to document conversations between customers and customer service agents, both through chatbots and self-service. The mobile operator is also using artificial intelligence to transcribe voice-to-text conversations to help agents working in call centers.

When COVID-19 hit, T-Mobile call centers were flooded with requests due to financial difficulties caused by the pandemic. T-Mobile was able to automate this simple task by implementing a chatbot. What T-Mobile didn’t expect was to get such a high return on investment (ROI) from starting a small side project that it became a widely used tool.

“We thought the chatbot would only live during the coronavirus season. But in the first 18 months of its life, we got a 750% return on investment from this chatbot,” said Heather Nolis, lead engineer in machine learning at T- Mobile. “A lot of routine tasks are done in our call centers and no people are needed. In fact, we have found that about 30% of our customers do not want to speak to a person and would prefer a conversation assistant.”

Conversational AI continues to evolve

Over the past three years, conversational AI has evolved to include new types of models that provide better predictions for summarizing and categorizing text, understanding feelings, and doing new things in both speech and vision. This means organizations will need to embrace a more pragmatic AI that addresses the business problem rather than the solution, said Jeff Boudier, product manager at Hugging Face, creator of the Transformers open source library of natural language processing (NLP). .

“One of the main challenges of the last three years has been putting science in the hands of professionals,” said Boudier. “Pragmatic AI requires the use of open source technologies as much as possible. That is a hallmark of machine learning – it is driven by science. It is a living system that people are building.”

Hugging Face’s Transformers Library encourages contributions from many people from different industries. There are more than 1,600 public data sets available in approximately 200 languages. Anyone can access 70,000 free templates, provided by a community of 1,000 (growing) contributors. Data sets range from text classification and transcription of audio recordings to object recognition in photos and videos.

An open and collaborative future is the ultimate goal of conversational AI. But before you can do that, businesses need to understand why they are creating chatbots. RingCentral’s Kukde believes companies should gradually introduce conversational AI and position it so that people don’t feel like it’s taking their work away. When AI is rolled out gradually, organizations have time to better train employees, he added.

Mr. Nolis believes that a good strategy for businesses is to create chatbots that give users a good experience rather than giving them suggestions they already know. It is important to understand where chatbots should and should not be used, so that people really like talking to chatbots rather than tolerating them and hoping to one day reach a real person.

“Anyone creating a chatbot should listen to their users examining the data they already have from social media interactions, complaints, and conversations with customer service agents,” Nolis concludes. “If the artificial intelligence agent that we are building cannot do what a human can do, then we will let a human take care of it, because we really care about the quality of our customer service.”

Source: “.com”

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