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Jarvis is a flexible application framework for multimodal conversational AI services that delivers real-time performance on NVIDIA GPUs.

Today, NVIDIA released Jarvis 1.0 Beta which includes an end-to-end workflow for building and deploying real-time conversational AI apps, such as transcription, virtual assistants and chatbots. Jarvis is a flexible application framework for multimodal conversational AI services that delivers real-time performance on NVIDIA GPUs.

This release of Jarvis includes new pre-trained models for conversation AI and support for Transfer Learning Toolkit (TLT) so enterprises can easily adapt apps to their specific use case and domain. These apps are able to understand context and nuance offering a better experience to users.

With Jarvis, enterprises get state-of-the-art models, ~10x speedup in development time using transfer learning with TLT, and fully optimized and GPU-accelerated pipelines for creating intelligent language-based applications that can run in real time.

Highlights from this version include:

• ASR, NLU, and TTS models trained on thousands of hours of speech data.
• TLT with zero coding approach to quickly re-train models on custom data.
• Fully accelerated deep learning pipelines optimized to run as scalable services.
• End-to-end workflow and tools to deploy services using one line of code. 

Conversational AI is opening new opportunities in every industry, from finance and healthcare to consumer services. 

Early adopters of Jarvis include InstaDeep, a company creating virtual assistants in the Arabic language. NVIDIA Jarvis played a significant role in improving their application’s performance. Using the NeMo toolkit in Jarvis, they were able to fine-tune an Arabic speech-to-text model to get a Word Error Rate as low as 7.84%.

One of the largest mobile network operators in Russia, MTS, is working with Jarvis for chatbots and virtual assistants for customer support. With Jarvis, they saw remarkable accuracy by fine-tuning the ASR models in the Russian language and higher throughout performance with TensorRT optimizations. 

Ribbon is leveraging Jarvis in their real-time communications and call processing platform to do advanced AI text-to-speech. Business and government organizations record tens of millions of calls every day, but it’s nearly impossible to search them to pull out important insights. Through Jarvis, recordings can now be turned into text so that AI tools can quickly search and analyze this data.

In the area of healthcare, Northwestern Medicine is working with Artisight to make hospitals smarter.

“At Northwestern Medicine, we aim to improve patient satisfaction and staff productivity with our suite of healthcare AI solutions,” said Andrew Gostine, MD, MBA, CEO of Artisight. “Conversational AI, powered by NVIDIA Clara Guardian and Jarvis, improves patient and staff safety during COVID-19 by reducing direct physical contact while delivering high-quality care. Jarvis ASR and TTS models make this conversational AI a reality. Patients now no longer need to wait for the clinical staff to become available, they can receive immediate answers from an AI-powered virtual assistant.”

Meanwhile Intelligent Voice, which has a system that uses speech recognition technology to capture calls, convert them into text and automatically send transcripts, saw great results with Jarvis.

“At Intelligent Voice, we provide high performance speech recognition solutions, but our customers are always looking for more,” said Nigel Cannings, CTO at Intelligent Voice. “Jarvis takes a multi-modal approach that fuses key elements of Automatic Speech Recognition with entity and intent matching to address new use cases where high-throughput and low latency are required. The Jarvis API is very easy to use, integrate and customize to our customers’ workflows for optimized performance.”

NVIDIA Jarvis and Transfer Learning Toolkit are available freely for download to members of the NVIDIA developer program today. On the ‘Getting Started’ page, you will find several resources such as samples, Jupyter notebooks and tutorial blogs for new users.

The NVIDIA Deep Learning Institute (DLI) released the Accelerated Data Science Teaching Kit, co-developed with Professor Polo Chau from Georgia Institute of Technology and Professor Xishuang Dong from Prairie View A&M University.

This week, the NVIDIA Deep Learning Institute (DLI) released the Accelerated Data Science Teaching Kit, co-developed with Professor Polo Chau from Georgia Institute of Technology and Professor Xishuang Dong from Prairie View A&M University. 

The comprehensive teaching materials cover fundamental and advanced topics in data collection and pre-processing, accelerated data science with RAPIDS, scalable and distributed computing, GPU-accelerated machine learning, data visualization and graph analytics, and addresses the growing need of teaching data science skills to students in higher education and research institutions.

This first release includes focused modules covering: 

• Introduction to Data Science and RAPIDS
• Data Collection and Pre-processing (ETL)
• Data Ethics and Bias in Data Sets
• Data Integration and Analytics
• Data Visualization
• Scalable and Distributed Computing with Hadoop, Hive and Spark

More modules are planned for future releases.

The kit also covers culturally-responsive topics such as fairness and data bias, as well as challenges and important figures from underrepresented groups.

Lecture slides and notes, hands-on labs, iPython notebooks, solutions (held in private repo), sample data sets, quiz/exam questions/answers, GPU compute resources via free AWS cloud credits, and free DLI online courses/certificates are all included. Lecture videos are planned for future releases.

The RAPIDS data science framework is a GPU-accelerated collection of libraries for executing end-to-end data science pipelines completely on the GPU. The primary objective behind using RAPIDS is to accelerate individual parts of the typical data science workflow, and thereby accelerating the complete end-to-end workflow in Data Preparation and Machine Learning. 

One of the first Jupyter notebook-based labs has students dive right into RAPIDS using pandas and cuDF. Pandas is a data analysis and manipulation tool built on top of the Python programming language to perform various tasks (e.g.: loading, joining, aggregating, filtering data). cuDF is a RAPIDS-based GPU DataFrame library that helps perform similar functionalities with GPU acceleration. 

Students are first tasked with understanding how to create DataFrame objects in cuDF, assigning values to those objects, and then calling methods and applying user-defined functions on the values. Once students have a grasp on working with cuDF DataFrames, they are tasked with creating one from a Netflix movie dataset from Kaggle. 

From there, students learn how to manipulate and interrogate the data, from dropping missing columns and values, querying, and finding unique values, to sorting, counting and grouping. The students will get a feel for how fast and easy it is by using RAPIDS and GPUs versus traditional methods also covered in the Teaching Kit. As a bonus task in the lab, students are finally asked to use cuDF One-hot encoding to convert the dataset’s movie and TV show titles to vectors of 0s and 1s to improve the accuracy of analyzing the data.

“Data Science unlocks the immense potential of data in solving societal challenges and large-scale complex problems across virtually every domain, from business, technology, science, engineering, healthcare, to government, and many more,” said Professor Polo Chau. “As data continues to grow in volume, velocity and complexity, there is an ever-increasing demand for data science talent and skill sets to help design the best solutions.”

This is the fourth Teaching Kit as part of the existing program of 7,000 qualified educators.

Get started with NVIDIA Teaching Kits >> 

Here comes another GFN Thursday, dropping in to co-op with you as we explore the world of Square Enix’s new Outriders game. Before we get into the rest of this week’s new additions, let’s head to Enoch and take a closer look at what makes People Can Fly’s upcoming launch special. Let’s Ride From the Read article >

The post Think Aggressively This GFN Thursday with Outriders Demo, 11 Additional Games appeared first on The Official NVIDIA Blog.

Using the highest-resolution images of a single DNA molecule captured to date, researchers in the U.K. discovered that coiled strands of genetic material twist and writhe while crammed in a cell.

Using the highest-resolution images of a single DNA molecule captured to date, researchers in the U.K. discovered that coiled strands of genetic material twist and writhe while crammed in a cell.

This previously unobserved movement was simulated on GPU-based systems including JADE, a University of Oxford supercomputer made up of NVIDIA DGX systems. Published in Nature Communications, this research could improve scientists’ understanding of DNA mechanics and inform the development of genetic therapies and diagnostics. 

“GPUs have massively changed the capabilities of biomolecular simulation,” said Sarah Harris, associate professor at the University of Leeds and a researcher on the project, a collaboration among multiple universities. 

By pairing high-resolution atomic force microscopy with HPC simulations, the team created the first videos of twisted DNA molecules called minicircles — where both ends of the molecule join together in a loop. Prior research suggests these minicircles might be markers of health, ageing, and disease. 

Using the GPU-accelerated AMBER package, the researchers were able to model DNA structure with state-of-the-art force fields based on highly accurate quantum mechanical simulations. The dynamics of the molecular simulations were visualized with VMD, which was also used to confirm the presence of hydrogen bonding interactions in the coiled DNA structure.

Adding a twist to a strand of DNA, the researchers found, made the molecules more dynamic, displaying dance-like moves that might help the DNA bind to other molecules. 

The minicircle simulations “are extremely exciting because they show, with remarkable detail, how wrinkled, bubbled, kinked, denatured, and strangely shaped they are, which we hope to be able to control someday,” said Professor Lynn Zechiedrich from Baylor College of Medicine, who provided the DNA minicircles for the study.  

The combination of microscopy images and simulation enabled the researchers to see not just the DNA’s double-helix structure, but the position of each atom within the loop, which contains between 250 and 340 base pairs. 

Read the full Nature Communications paper here. 

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