Students at the Indianapolis-based medical school, one of the nation's largest, learn stethoscope skills but also get training in hand-held ultrasound in a program launched there last year by Dr. Paul Wallach, an executive associate dean. He created a similar program five years ago at the Medical College of Georgia and predicts that within the next decade, hand-held ultrasound devices will become part of the routine physical exam, just like the reflex hammer.

We're looking for a talented Data Scientist to join Snap, Inc! As part of the Data Insights and Governance team you will work closely with the engineering, legal, and growth teams. You will implement as well as operate inventive, scalable, and reproducible solutions to difficult business problems at a global scale. You will broadly impact analytic thinking across other teams in a measurable way delighting our community and partners. Working from our Los Angeles, CA, headquarters, you'll be an indispensable part of a small but highly-leveraged team.

In the workplace of the not-too-distant future, employees will need to go beyond being tech-savvy to being able to comfortably work alongside digital colleagues. Artificial intelligence (AI), machine learning and intelligent bots will be automatically making decisions to streamline business processes and empower efficient automation. The widespread adoption of machines to do much of the "heavy lifting" will change some jobs from the inside out, making individual workers far more productive and less bogged down with repetitive tasks. Smart chatbots can already handle first- and even second-level customer service calls, and AI is powering everything from manufacturing lines to automated vehicles. For example, BHP is rolling out automated trucks at its iron ore and coal mines across Australia over the next 5 years, following the success of its Jimblebar mine trial program, which saw a 90 per cent reduction in the number of dangerous incidents.

Artificial intelligence programs are extremely good at finding subtle patterns in enormous amounts of data, but don't understand the meaning of anything. Whether you are searching the Internet on Google, browsing your news feed on Facebook, or finding the quickest route on a traffic app like Waze, an algorithm is at the root of it. Algorithms have permeated our daily lives; they help to simplify, distill, process, and provide insights from massive amounts of data. According to Ernest Davis, a professor of computer science at New York University's Courant Institute of Mathematical Sciences whose research centers on the automation of common-sense reasoning, the technologies that currently exist for artificial intelligence (AI) programs are extremely good at finding subtle patterns in enormous amounts of data. "One way or another," he says, "that is how they work."

Designers that spend their days creating new electronic chips push descriptions of their design through an elaborate flow of over 20 tools in order to get a verified product fabricated. Along the way, high-level concepts are captured in English-like programming descriptions that are transformed to lower and lower level abstractions until finally, they brush against the very limits of physics at almost the molecular level. For example, a graphics processor chip in a gaming computer can contain over 50 million transistors, yet its size is only 12 by 12 millimeters. Three grains of table salt stacked together are about 1 millimeter across. At each step in the flow, designers apply verification techniques to ensure that the design works as expected.

Supervised learning is the crown jewel of Machine Learning. Supervised learning is the machine learning task or process of producing a function that predicts output variables. It has been adopted widely in the industry. For example, banks apply supervised models to detect credit card fraud. Quantitative traders make purchase decisions based on ML model predictions.

At RMA we focus on both development and application of advanced computational models and engineering software to support complex decision making for water resources systems. We develop desktop, client-server, and mobile applications for data acquisition, visualization, and modeling of large scale river-reservoir systems. Through our collaboration with the U.S. Army Corps of Engineers and National Weather Service, our code supports emergency management operations across the country. Driven by increasing scale of water management issues and consequences, RMA is bringing distributed computing and machine learning techniques to strategic planning and real-time response.

NVIDIA today announced the NVIDIA EGX Edge Supercomputing Platform – a high-performance, cloud-native platform that lets organizations harness rapidly streaming data from factory floors, manufacturing inspection lines and city streets to securely deliver next-generation AI, IoT and 5G-based services at scale, with low latency. Early adopters of the platform – which combines NVIDIA CUDA-X software with NVIDIA-certified GPU servers and devices – include Walmart, BMW, Procter & Gamble, Samsung Electronics and NTT East, as well as the cities of San Francisco and Las Vegas. "We've entered a new era, where billions of always-on IoT sensors will be connected by 5G and processed by AI," said Jensen Huang, NVIDIA founder and CEO, at a keynote at the start of MWC Los Angeles. "Its foundation requires a new class of highly secure, networked computers operated with ease from far away. "We've created the NVIDIA EGX Edge Supercomputing Platform for this world, where computing moves beyond personal and beyond the cloud to operate at planetary scale," he said.

At Mobile World Congress Los Angeles, NVIDIA Co-founder and CEO Jensen Huang announced in his keynote – NVIDIA Jarvis – an SDK for building and deploying AI applications that fuse vision, speech, and other sensors into one system. NVIDIA Jarvis offers a comprehensive workflow to build, train and deploy AI systems that uses speech and visual cues such as gestures and gaze in context. For example, lip movement can be fused with speech input to identify the active speaker. Gaze can be used to understand if the speaker is engaging the AI agent or other people in the scene. This enables simultaneous multi-user, multi-context conversations with the AI system that need a deeper understanding of the context.

NVIDIA and Ericsson today announced they are collaborating on technologies that can allow telco operators to build high-performing, efficient and completely virtualized 5G radio access networks (RAN). These virtualized networks can enable faster and more flexible introduction of new AI and IoT services. The collaboration, announced by NVIDIA founder and CEO Jensen Huang at a keynote ahead of the start of MWC Los Angeles, brings together Ericsson's expertise in RAN technology with NVIDIA's leadership in GPU-powered accelerated computing platforms, as well as AI and supercomputing. Telcos are exploring alternative technologies and RAN architectures amid growing interest for virtualization, while securing the best possible user experience. A major industry challenge is how to virtualize the complete RAN solution in a cost-, size- and energy-efficient way, comparable with traditionally built RAN networks.