Conversational AI

New AI Technologies Announced at GTC 2020 Keynote

AI-Generated Summary

  • NVIDIA Maxine is a cloud-native video streaming AI platform that runs state-of-the-art AI models and optimized pipelines in real time for services such as video conferencing.
  • TensorRT 7.2 delivers 30x faster AI effects compared to CPU for video workloads, 2.5x faster recommenders with fully connected layer optimizations, and 2x lower latency for RNNs enabling real-time fraud and anomaly detection.
  • NVIDIA Riva Open Beta provides an accelerated SDK for building multimodal conversational AI services that run under 300 ms latency, cutting end-to-end latency in half and offering 7x higher throughput than CPUs on the SpeechSquad benchmark.
  • As part of Riva, NeMo 1.0 Beta simplifies conversational AI model development with PyTorch Lightning APIs, PyTorch interoperability, and Hydra framework integration for easy customization.
  • NVIDIA Merlin Open Beta is an application framework for end-to-end deep learning recommender systems that accelerates data preprocessing, model training, and inference on GPUs, with faster training than default data loaders in TensorFlow and PyTorch.

Next Steps

  • Sign up for early access to NVIDIA Maxine to evaluate the cloud-native video streaming AI platform.
  • Explore the TensorRT open source repo for the latest samples, parsers, and notebooks.
  • Get the new version of NeMo to develop state-of-the-art conversational AI models.
  • Register for GTC to learn more about the latest GPU-accelerated AI technologies.
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At GTC 2020, NVIDIA announced updates to 80 SDKs, including tools to help you build AI-powered video streaming solutions, conversational AI, recommendation systems, and more.

Announcing NVIDIA Maxine

Today, we announced NVIDIA Maxine, a cloud-native video streaming AI platform for services such as video conferencing. It includes state-of-the-art AI models and optimized pipelines that can run several features in real time in the cloud.

Sign up for early access to Maxine.


Announcing TensorRT 7.2

Today NVIDIA announced TensorRT 7.2, the latest version of its high-performance deep learning inference SDK. 

Highlights include:

  • 30x faster AI-effects compared to CPU for video-based workloads, enabling super-resolution, noise removal, and virtual backgrounds to run in real time.
  • 2.5x faster recommenders with optimizations for fully connected layers used in MLPs
  • 2x lower latency for RNNs compared to earlier, enables apps such as real-time fraud and anomaly detection

TensorRT 7.2 will be available in Q4, 2020 from the TensorRT page. The latest version of samples, parsers, and notebooks are always available in the TensorRT open source repo.

Add this GTC session to your calendar to learn more: Get the Highest Inference Performance using TensorRT


Announcing the NVIDIA Riva Open Beta

Today, NVIDIA announced Riva Open Beta, an accelerated SDK for enterprises to build multimodal conversational AI services that run in real-time on GPUs.  It includes state-of-the-art DL models, tools for composing new models, transfer learning, and deployment, as well as optimized services that run under 300 ms latency. Riva cuts end-to-end conversational AI latency to half and offers 7x higher throughput compared to CPUs on the SpeechSquad benchmark.

As part of Riva, we also announced NeMo 1.0 Beta. NeMo is an open-source toolkit to develop state-of-the-art conversational AI models in three lines of code. In the latest version, you get:

  • Simplified APIs based on PyTorch Lightning
  • Built on PyTorch, interoperable with PyTorch modules
  • Easy customization of models with popular Hydra framework integration.

This version of NeMo is optimized on A100 as well as earlier architectures with Tensor Cores. Get the new version of NeMo.

Resources:

Add these GTC sessions to your calendar to learn more:


Announcing NVIDIA Merlin Open Beta

Today NVIDIA announced the latest release of NVIDIA Merlin, an open beta application framework that enables the end-to-end development of deep learning recommender systems, from data preprocessing to model training and inference, all accelerated on NVIDIA GPUs. With this release, Merlin addresses common pain points around optimization and interoperability.

During preliminary testing, Merlin provides faster training on GPU in TensorFlow and PyTorch using our data loaders than default data loaders. This latest release reaffirms the NVIDIA commitment to accelerating the workflow of researchers, data scientists, and machine learning engineers and democratizing the development of large-scale, deep learning recommender systems.

Add this GTC session to your calendar to learn more:

Resources:

Register for GTC this week for more on the latest GPU-accelerated AI technologies.

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