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Navigating Driver Privacy and Safety of Electric Vehicles, Self-Driving Vehicles

A growing number of connected electric vehicles, as well as the evolution of self driving and automated vehicles are putting a greater demand on processing power. New technologies are advancing rapidly with the introduction of new processing methods, according to experts at BrainChip Holdings Ltd (ASX: BRN), (OTCQX: BRCHF), a leading provider of ultra-low power high performance artificial intelligence technology.

MLOps: Bringing AI to the Tactical Edge—and Making It Work

In this contributed article, Joel Dillon and Eric Syphard of Booz Allen, feel strongly that in order for machine learning to have a profound impact on data sharing for defense and the intelligence community, it’s imperative that data get communicated to warfighters at the tactical edge, where fast decisions are at a premium and compute power and connectivity are often scarce. It is critical that these edge use cases characterize and shape planning for AI and ML-driven investment as digitization continues to accelerate the pace of war.

Surpassing Decentralized Data Management Woes with Data Virtualization

In this contributed article, editorial consultant Jelani Harper discusses how data virtualization enables organizations to surmount obstacles (i.e. data quality, schema, and data integrations that are foundational to data management) and to focus on benefits (i.e. remote collaborations characteristic of working from home, the takeoff of the cloud as the de facto means of deploying applications, and the shift to external sources of unstructured and semi-structured data). Supplementing it with mutable graph data models boosts its applicability to data of all types.

Innovative AI Edge Device Cuts Costs and Delivers Faster Performance

Leopard Imaging has been working to address the need for affordable multiprocessing power in deep learning applications. Using Socionext’s SC2000 image signal processor and the Hailo-8™ M.2 AI acceleration module, Leopard Imaging’s EdgeTuring™ consumes less power, performs at a higher level, and ensures greater reliability for video analytics and privacy at the edge than alternative solutions.

Research Highlights: Attention Condensers

A group of AI researchers from DarwinAI and out of the University of Waterloo, announced an important theoretical development in deep learning around “attention condensers.” The paper describing this important advancement is: “TinySpeech: Attention Condensers for Deep Speech Recognition Neural Networks on Edge Devices,” by Alexander Wong, et al. Wong is DarwinAI’s CTO.

Transform Raw Data to Real Time Actionable Intelligence Using High Performance Computing at the Edge

In this special guest feature, Tim Miller from One Stop Systems discusses the importance of transforming raw data to real time actionable intelligence using HPC at the edge. The imperative now is to move processing closer to where the data is being sourced, and apply high performance computing edge technologies so real time insights can drive business actions.

Expansion of the Edge: The Preeminent Importance of Edge Computing Today

In this contributed article, editorial consultant Jelani Harper discusses how the reliance on edge components and edge processing is becoming more critical to the decentralized big data landscape, especially with the ongoing need to communicate remotely. It’s imperative to ensure edge networks can be remotely provisioned, secured, and available for fringe processing where applicable to continue to support what’s become a burgeoning need for the IoT in general.

On the Edge of Something Big

In this contributed article, Tim Parker, VP of Network Strategy at Flexential, provides the top four reasons your organization needs an edge strategy now. Edge computing enables efficient data processing near the source to minimize latency, reduce bandwidth usage and lower costs while improving compliance, security and resiliency.

The Open Edge Architecture Imperative

In this contributed article, Roman Shapshnik, Co-Founder and VP of Product & Strategy at ZEDEDA, outlines the path forward for edge computing—where regardless of hardware, developers can create applications to run uniformly on the edge. Constricting the architecture to keep control only works in the short term.

NXP Delivers Embedded AI Environment to Edge Processing

NXP Semiconductors N.V. (NASDAQ:NXPI) announced a comprehensive, easy-to-use machine learning (ML) environment for building innovative applications with cutting-edge capabilities. Customers can now easily implement ML functionality on NXP’s breadth of devices from low-cost microcontrollers (MCUs) to breakthrough crossover i.MX RT processors and high-performance application processors.