What’s next in Conversational AI?

Conversational artificial intelligence (AI) refers to technologies, like chatbots or virtual agents, which users can talk to. They use large volumes of data, machine learning, and natural language processing to help imitate human interactions, recognizing speech and text inputs and translating their meanings across various languages.

Conversational AI combines natural language processing (NLP) with machine learning. These NLP processes flow into a constant feedback loop with machine learning processes to continuously improve the AI algorithms. Conversational AI has principle components that allow it to process, understand, and generate response in a natural way.

Conversational AI starts with thinking about how your potential users might want to interact with your product and the primary questions that they may have. You can then use conversational AI tools to help route them to relevant information. In this section, we’ll walk through ways to start planning and creating a conversational AI.

NVIDIA’s conversational AI SDK

AI-driven services in speech, vision, and language present a revolutionary path for personalized natural conversation, but they face strict accuracy and latency requirements for real-time interactivity. With NVIDIA’s conversational AI SDK, developers can quickly build and deploy state-of-the-art multimodal AI services to power applications across a single unified architecture, delivering highly accurate, low-latency systems with little upfront investment.

Full Pipeline Inference in Fractions of a Second

Accelerate the Full Pipeline, from Speech Recognition to Language Understanding and Speech Synthesis.

NVIDIA provides solutions to Execute full conversational AI pipelines consisting of automatic speech recognition (ASR) for audio transcription, natural language understanding (NLU), and text-to-speech (TTS) in well under the 300 ms latency bound for real-time interactions, freeing up room to increase pipeline complexity without sacrificing user experience

Features

State-of-the-Art Models	Harness conversational AI models from NGC™ that are trained on various open and proprietary datasets for more than 100,000 hours on NVIDIA DGX™ systems.
Custom Skills	Customize speech, language, and vision skills on your domain using TAO Toolkit
Rapid Deployment	Deploy optimized models in the cloud, in the data center, and at the edge with a single command
End-to-End Acceleration	Accelerate at pipeline scale and execute model inference in well under the 300 millisecond (ms) latency bound

What’s next in conversational AI?

NVIDIA Solutions for conversational AI Applications

The successful adoption of conversational AI hinges on the delivery of natural
interactions that feel human. As conversational AI is on the rise, NVIDIA provides both training and development solutions

Train Smarter with NVIDIA TAO Toolkit

Run Training on NVIDIA DGX A100 Systems

Simplify Deployment with the NVIDIA Riva Framework

Deploy at the Edge with NVIDIA EGX Platform

Top 5 Reasons to choosing NVIDIA Solutions

The most advanced data center accelerator ever made

NVIDIA DGX^™ A100 features eight NVIDIA A100 Tensor Core GPUs—the most advanced data center accelerator ever made. Tensor Float 32 (TF32) precision delivers a 20X AI performance improvement over previous generations—without any code change—and an additional 2X performance boost by leveraging structural sparsity across common NLP models.

Deploy at the Edge

NVIDIA EGX™ Platform makes it possible to drive real-time conversational AI while avoiding networking latency by processing high-volume speech and language data at the edge.

Multi-Speaker Transcription

Classic speech-to-text algorithms have evolved, making it now possible to transcribe meetings, lectures, and social conversations while simultaneously identifying speakers and labeling their contributions

Virtual Assistant

Virtual assistants can engage with customers in a nearly human-like way, powering interactions in contact centers, smart speakers, and in-car intelligent assistants. AI-driven services like speech recognition, language understanding, voice synthesis, and vocoding alone cannot support such a system, as they’re missing key components such as dialogue tracking

Conversational AI use cases

When people think of conversational artificial intelligence, online chatbots and voice assistants frequently come to mind for their customer support services and omni-channel deployment. Most conversational AI apps have extensive analytics built into the backend program, helping ensure human-like conversational experiences

UC#	UC Description
Online customer support:	Online chatbots are replacing human agents along the customer journey
Health Care	Conversational AI can make health care services more accessible and affordable for patients, while also improving operational efficiency and the administrative process, such as claim processing, more streamlined.
Internet of things (IoT) devices	Most households now have at least IoT device, from Alexa speakers to smart watches to their cell phones. These devices use automated speech recognition to interact with end users.
Computer software	Many tasks in an office environment are simplified by conversational AI, such as search autocomplete when you search something on Google and spell check.

Challenges of conversational AI technologies

There are some challenges with transitioning to conversational AI applications. Some examples include:

Language input	the biggest challenge for conversational AI is the human factor in language input. Emotions, tone, and sarcasm make it difficult for conversational AI to interpret the intended user meaning and respond appropriately.
Privacy and security	As Conversational AI is dependent on collecting data to answer user queries hence it is also vulnerable to privacy and security breaches.

References to the above article are taken from the NVIDIA Conversational AI

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