AI

ISG report recognises Atos IOT capabilites

Atos today announces that it has been recognised as the overall global leader in Artificial Intelligence on the Edge in ISG’s Provider Lens ‘Internet of Things – Solutions & Services’ Quadrant Report 2021, ranking highest from all global companies evaluated both in terms of Portfolio Attractiveness and Competitive Strength. Atos is also named a global Leader across the other two segments analysed: IoT Endpoint Security, and IT/OT Tech Data Convergence.

The report recognises Atos’ strengths as:

Credit: Quardia/Shutterstock

Nvidia and Google Cloud to create industry’s first AI-on-5G lab

Nvidia has announced that it is partnering with Google Cloud to establish the industry’s first AI-on-5G Innovation Lab, enabling network infrastructure players and AI software partners to develop, test and adopt solutions that will help accelerate the creation of advanced 5G and AI applications

Sittipong Phokawattana/Shutterstock

HPE acquires Determined AI

Hewlett Packard Enterprise today announced that it has acquired Determined AI, a San Francisco-based startup that delivers a software stack to train AI models faster, at any scale, using its open-source machine learning (ML) platform

Evolving AI

With the growth of AI and DL comes new opportunities for emerging applications, finds Robert Roe

LIVE WEBINAR: GPU Accelerated Scientific Machine learning with Large Data Sets

Tuesday 15 June @ 2pm (UK)

The time to publication of Data-Driven Research can be decreased through GPU-Accelerated Data Science.  Large proportion of tools used for scientific analysis and machine learning (e.g. Pandas, SciKit Learn, NetworkX or Spark) have GPU-accelerated variants. We'll discuss how, by changing just a few lines of code, research teams can accelerate their experiments by several orders of magnitude, significantly reducing hardware-associated cost or data processing/training times.

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On Demand Webinar: GPU Accelerated Scientific Machine learning with Large Data Sets

The time to publication of Data-Driven Research can be decreased through GPU-Accelerated Data Science.  Large proportion of tools used for scientific analysis and machine learning (e.g. Pandas, SciKit Learn, NetworkX or Spark) have GPU-accelerated variants. We'll discuss how, by changing just a few lines of code, research teams can accelerate their experiments by several orders of magnitude, significantly reducing hardware-associated cost or data processing/training times.

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