NVIDIA is building a desktop supercomputer. At the company's GTC conference today, CEO Jensen Huang announced DGX Spark and DGX Station. We got a first look at the former during CES earlier this year when Huang and company revealed Project Digits. Now known as DGX Spark, NVIDIA is billing the 3,000 device as the world's smallest AI supercomputer. It features a GB10 Grace Blackwell Superchip NVIDIA has shrunk down to fit inside an enclosure about the size of the previous generation Mac mini.

In vitro fertilization, a common treatment for infertility, is a lengthy undertaking for prospective parents, involving ultrasounds, blood tests and injections of fertility medications. If the process doesn't end up in a successful pregnancy -- which is often the case -- it can be a major emotional and financial blow. Sydney-based healthcare startup Harrison.ai is using deep learning to improve the odds of success for thousands of IVF patients. Its AI model, IVY, is used by Virtus Health, a global provider of assisted reproductive services, to help doctors evaluate which embryo candidate has the best chance of implantation into the patient. Founded by brothers Aengus and Dimitry Tran in 2017, Harrison.ai

A new report estimates the cost of waste in the U.S. healthcare system alone ranges as high as $935 billion a year, about 25 percent of total healthcare spending. A growing army of startups and established practitioners sees the inefficiencies as a trillion-dollar opportunity to apply AI. The U.S. spends about 18 percent of its gross domestic product on healthcare, more than any other country. A report published online by the Journal of the American Medical Association surveyed 54 studies to estimate annual waste figures in six broad categories, including failures from choosing ineffective treatments (up to $166 billion), failures from coordinating multiple treatments ($78 billion), fraud and abuse ($84 billion) and administrative complexity ($266 billion). "Implementation of effective measures to eliminate waste represents an opportunity to reduce the continued increases in U.S. health care expenditures," the report concluded.

Michael Balint is a senior manager of applied solutions engineering at NVIDIA. Previously, Michael was a White House Presidential Innovation Fellow, where he brought his technical expertise to projects like Vice President Biden's Cancer Moonshot program and Code.gov. Michael has had the good fortune of applying software engineering and data science to many interesting problems throughout his career, including tailoring genetic algorithms to optimize air traffic, harnessing NLP to summarize product reviews, and automating the detection of melanoma via machine learning.

More than two billion images are shared daily in social networks alone. Research shows that it would take a person ten years to look at all the photos shared on Snapchat in the last hour! Media buyers and providers experience difficulty organizing relevant content in groups, parsing components of images/videos, and defining the return on investment from generated content in an efficient way. NVIDIA has many customers and ecosystem partners tackling that problem, using NVIDIA DGX as their preferred platform for deep learning (DL) powered image recognition. One of the notable names among the ecosystem is Imagga, a pioneer in offering a deep learning powered image recognition and image processing solution, built on NVIDIA DGX Station, the world's first personal AI supercomputer.

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.

In this vastly competitive AI market, many companies are overwhelmed with the relentless pace of innovation. Time to market is one key competitive differentiator, and having readily accessible supercomputing power within arm's reach quicken innovation from months to days. That's why we built the DGX Station, a powerful and purpose-built AI supercomputer workstation. It fits neatly under the desk and is ideal for researchers and developer teams. Let's explore how the DGX Station has benefited some of the leading AI companies and their research teams: The Data Center Comes to Your Desk.

The billowing compute and data demands that spurred the oil and gas industry to be the largest commercial users of high-performance computing are now propelling the competitive sector to deploy the latest AI technologies. Beyond the requirement for accurate and speedy seismic and reservoir simulation, oil and gas operations face torrents of sensor, geolocation, weather, drilling and seismic data. Just the sensor data alone from one off-shore rig can accrue to hundreds of terabytes of data annually, however most of this remains unanalyzed, dark data.

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.

However, for our current projects we need a compute server that we have exclusive access to." Access to a deep learning workstation will increase the speed of innovation and improve security." RESEARCHER "I felt I won the software stack lottery as NVIDIA- docker was already installed. I immediately pulled a container and started work on a CNTK NCCL project, the next day pulled another container to work on a TF biomedical project. I haven't looked back at how to reimage because felt too productive." It feels right for this work to allow fast iteration.