Paul Edwards is helping carry the age-old business of giving loans into the modern era of AI. Edwards started his career modeling animal behavior as a Ph.D. in numerical ecology. He left his lab coat behind to lead a group of data scientists at Scotiabank, based in Toronto, exploring how machine learning can improve predictions of credit risk. The team believes machine learning can both make the bank more profitable and help more people who deserve loans get them. They aim to share later this year some of their techniques in hopes of nudging the broader industry forward. The new tools are being applied to scorecards that date back to the 1950s when calculations were made with paper and pencil.

Royal Bank of Canada built a DGX-powered cloud and tied it to a strategic investment in AI. Despite headwinds from a global pandemic, it will further enable RBC to transform client experiences. The voyage started in the fall of 2017. That's when RBC, Canada's largest bank with 17 million clients in 36 countries, created its dedicated research institute, Borealis AI. The institute is headquartered next to Toronto's MaRS Discovery District, a global hub for machine-learning experts.

Core Scientific, Inc. provides accelerated AI computing optimized to the demands of AI and deep learning. The company's Cloud for Data Scientists also integrates NetApp ONTAP AI, built on a verified architecture that combines Nvidia DGX systems for compute and NetApp AFF systems for storage. NetApp, Inc.'s architecture extends the company's solution for AI and ML and is included in the AiLab for customers to train large models. The firm is a provider in AI and blockchain technologies, delivering infrastructure and software solutions. In an increasingly distributed and connected world, the company believes that AI and blockchain are changing the way information is processed, shared and stored across a range of industries.

HPC and AI in a phone booth: ScaleMatrix and Nvidia announced today at the SC19 conference in Denver a joint offering that puts up to 13 petaflops of Nvidia DGX-1 compute power in an air conditioned, water-cooled ScaleMatrix Dynamic Density Control (DDC) "clean room" cabinet. Built for modular deployments and designed for high-demand AI workloads, ScaleMatrix said its ruggedized cabinet can be erected "anywhere power and a roof exist," and it includes biometric security and fire suppression. At the high end of the product line is a composable SKU comprised of the Nvidia DGX-1 system, a single rack running at 42kW, containing 13 DGX-1 units and delivering 13 Pflops of throughput. Other configurations come with a DGX POD deployment, four DGX-2s, run at 43kW and deliver 8 Pflops of compute, the companies said. The units will be sold with storage and networking following DGX POD reference architecture designs, such as NetApp's ONTAP AI solution.

Pure Storage, has announced a host of new and improved AI solutions that provide enterprise customers with the features and functionality needed to execute increasingly complex AI initiatives through any phase or scale.Built on Pure's industry-leading file and object system, FlashBlade, and its joint AI-Ready Infrastructure (AIRI) offering with NVIDIA, customers can develop and deploy AI rapidly to keep pace with modern business. "Enterprise organizations that have existed and done business one way for decades now find themselves working hard to build a business for the future. To truly compete going forward will require large-scale, multi-phase AI initiatives, and Pure has innovated with that particular set of challenges in mind, said Amy Fowler, VP of Strategy and Solutions for FlashBlade at Pure Storage. Organizations today are stuck with a siloed, traditional analytics infrastructure. AI Data Hub extends traditional analytics and provides more performance and security at a lower cost.

In a clear demonstration of why AI leadership demands the best compute capabilities, NVIDIA today unveiled the world's 22nd fastest supercomputer -- DGX SuperPOD -- which provides AI infrastructure that meets the massive demands of the company's autonomous-vehicle deployment program. The system was built in just three weeks with 96 NVIDIA DGX-2H supercomputers and Mellanox interconnect technology. Delivering 9.4 petaflops of processing capability, it has the muscle for training the vast number of deep neural networks required for safe self-driving vehicles. Customers can buy this system in whole or in part from any DGX-2 partner based on our DGX SuperPOD design. AI training of self-driving cars is the ultimate compute-intensive challenge.

Europe is home to 5 of the top 10 universities for computer science in the world. It comes as no surprise, then, that Europe is a hub for ground-breaking AI research. Leading institutes are increasingly tackling real-world challenges using AI. And the trend is not limited to those schools traditionally focused on computer science; business-oriented schools are also recognizing the benefits AI can bring. Those institutes at the cutting-edge of AI research are turning to NVIDIA's DGX Systems, the world's first portfolio of purpose-built AI supercomputers, to provide the computing power they need.

In a competitive marketplace, insights from recorded call center audio can help companies improve employee training, enhance lead qualification, increase sales, improve customer satisfaction, and reduce churn and employee turnover. But because call scoring is labor-intensive and time-consuming, most call centers only listen to approximately 2% of their recorded calls. Deepgram, a deep learning enabled voice-to-text technology company, is helping solve this challenge for enterprises by unlocking a wealth of information buried within these call recordings. Related: DeepGram's Dr. Scott Stephenson presents "From Dark Matter to Deep Learning in the Enterprise" at GPU Technology Conference 2018. They also have deep expertise in other industries, including medical, legal, media, and emergency services. One of Deepgram's early customers saw a 3% increase in annual revenue using the solution.

Trace3 will play a key role in delivering NVIDIA-based artificial intelligence, machine learning, and deep learning solutions to enterprises worldwide. The NVIDIA partner program will include initiatives to help partners expand capabilities for the integration and deployment of NVIDIA GPU computing solutions, including NVIDIA DGX systems. The initial phase will focus on providing services for deep learning and neural network development for image analysis, natural language processing, and time-series analysis. Trace3 has been providing big data services to enterprise clients since the launch of their Data Intelligence practice in March 2014. Today, the company has new comprehensive Artificial Intelligence engagements (Server Hardware and Services) in the works at companies in the manufacturing, financial services, and banking industries as well as several other clients in the planning stages for 2018.

Jumpstart and accelerate your deep learning deployments with our exclusive deal on the NVIDIA DGX Station. We are offering a special promotional price of $49,900 for your first DGX Station purchase now through April 29, 2018. Built on the same software stack powering all NVIDIA DGX Systems, DGX Station gives researchers the fastest start in deep learning and data science. Simply plug it in and power it up for immediate productivity that lets you experiment at your desk and extend your work across all DGX Systems and the cloud. The NVIDIA DGX Station packs 480 TeraFLOPS of performance, with the first and only workstation built on four NVIDIA Tesla V100 accelerators, including innovations like next generation NVLink and new Tensor Core architecture.