Monetizing in a global market is challenging. Many organizations hope that by the time an opportunity arises, they'll have a plan in place to handle both the risk and opportunity of monetizing any new market by using data science for risk management Prabhu Sadasivam's 2019 talk for ODSC's Accelerate AI, "Data Science for Risk Management in a Global Market," seeks to define strategies for using data science in global risk management as companies expand to new markets and predict up and coming ones. He offers practical strategies, and some pitfalls organizations must avoid in order to have a reliable strategy. Let's learn from his expertise. When you move into new markets, you must create a master file identifying the unique list of businesses or customers.

AI is hungry, hyperscale AI ravenous. As AI models rapidly get larger and more complex (an estimated 10x a year), a recent MIT study warns that computational challenges, especially in deep learning, will continue to grow. Service providers, large enterprises and others also face unrelenting pressures to speed up innovation, performance, and rollouts of neural networks and other low-latency, data-intensive applications, often involving exascale cloud and High-Performance Computing (HPC). These dueling demands are driving technology advances and adoption of a growing universe of Field Programmable Gate Arrays (FPGAs). In the early days of exascale computing and AI, these customer-configurable integrated circuits played a key role.

A startup that specialises in healthcare analytics and AI has launched a new platform that aims to help healthcare providers accelerate their digital journeys to make a return on their investment (ROI). KenSci's new AI platform, built on Microsoft Azure, aims to helps healthcare organisations accelerate their data transformation, enabling AI and analytics workloads at scale. The startup, which is headquartered in Seattle, has launched their re-imagined platform with the latest predictive analytics technology, to enable health organisations to develop business intelligence (BI) and AI-based workloads in an easy, agile way. The platform has been built specifically for the healthcare sector, designed to provide AI-based insights on operational and clinical workflows. Users can customise it to suit their needs or use the solutions already built into the software.

A new reinforcement-learning algorithm has learned to optimize the placement of components on a computer chip to make it more efficient and less power-hungry. It requires the careful configuration of hundreds, sometimes thousands, of components across multiple layers in a constrained area. Traditionally, engineers will manually design configurations that minimize the amount of wire used between components as a proxy for efficiency. They then use electronic design automation software to simulate and verify their performance, which can take up to 30 hours for a single floor plan. Time lag: Because of the time investment put into each chip design, chips are traditionally supposed to last between two and five years.

WIRE)--WekaIO (Weka), the innovation leader in high-performance, scalable file storage for data-intensive applications, today announced that Innoviz, a leading manufacturer of high-performance, solid-state Light Detection and Ranging (LiDAR) sensors and Perception Software that enables the mass-production of autonomous vehicles, has selected the Weka File System (WekaFS) to accelerate its Artificial Intelligence (AI) and deep learning workflows. WekaFS has been chosen by Innoviz to improve application performance at scale and deliver high bandwidth I/O to its GPU cluster. Innoviz's solid-state LiDAR sensors are key to the future of autonomous cars. The sensors and Perception Software, which identifies, classifies, segments, and tracks objects to give autonomous vehicles a better understanding of the 3D driving scene, rely heavily on AI. Having recently closed its Series C funding round with $170M secured, Innoviz is choosing and developing the right technologies to empower it to realize its expansion plans and enhance its manufacturing capabilities.

Munich – 17 September 2019 – Artificial intelligence (AI) and neural networks are becoming a key factor in developing safer, smart and eco-friendly cars. In order to support AI-driven solutions with its future automotive microcontrollers, Infineon Technologies has started a collaboration with Synopsys, Inc. Next generation AURIX microcontrollers from Infineon will integrate a new high-performance AI accelerator called Parallel Processing Unit (PPU) that will employ Synopsys' DesignWare ARC EV Processor IP. AI and neural networks are fundamental building blocks for future automated driving applications such as object classification, target tracking, or path planning. Furthermore, they play an important role in optimizing many other automotive applications, helping to reduce the cost of ECU systems, improving their performance and accelerating time-to-market.

The News: NVIDIA and VMware today at VMworld announced their intent to deliver accelerated GPU services for VMware Cloud on AWS to power modern enterprise applications, including AI, machine learning and data analytics workflows. These services will enable customers to seamlessly migrate VMware vSphere-based applications and containers to the cloud, unchanged, where they can be modernized to take advantage of high-performance computing, machine learning, data analytics and video processing applications. Analyst Take: This week at VMworld, I expect the big theme to continue to be multi-cloud and Hybrid IT and for VMware it is all about building solutions that create the greatest level of seamlessness between on-prem and cloud. This has been the driving factor of vSphere as IT has sought for ways to build applications and workloads that can work on-prem or in the cloud consistently for both administrators and users. To date, AI Training can be accomplished effectively on-prem or in the Cloud, but there has been some limitations on leveraging the benefits of hybrid IT to modernize applications using GPUs.

MWC19 will be all about 5G: mobile service providers will officially announce commercial deployments; handset manufacturers will showcase 5G smartphones and the rest of the supply chain industry will announce new products and services to support these first network deployments. There will be many concepts and technologies developing in parallel to 5G at MWC19, including AR/VR, rich media, robotics, drones, and several other use cases across many end markets. Most of these will likely be proof of concepts or ideas rather than actual live implementations. The majority of 5G developments focus on mobile broadband now, and the US has a significant lead. Several mobile service providers, including AT&T have discussed that AI will be a critical technology to accelerate 5G deployments.

Artificial Intelligence (AI) holds major potential for healthcare, from predicting the patient length of stays to diagnostic imaging, anti-fraud, and many more use cases. To be successful in using AI, healthcare needs solutions, not projects. Learn how you can close the gap to your AI in healthcare solution by accelerating your initiative using Microsoft Azure blueprints. To rapidly acquire new capabilities and implement new solutions, healthcare IT and developers can now take advantage of industry-specific Azure Blueprints. Blueprints include resources such as example code, test data, security, and compliance support.

Deep learning is the technology driving today's artificial intelligence revolution. Dimensionality reduction is one of the most crucial tools in a data scientists' toolbox, and modern tools can yield truly magical results. ODSC Europe 2017 is a unique collection of over 70 insightful presentations on data science modeling, tools, and languages, and topics delivered by top experts in the field. Topics include deep learning, quant finance and AI for business and more. Data visualisation offers a brilliant way of bringing the raw numbers to life.