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IT & Data Center

Latest Content

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Japan's Fugaku tops 1.45 exaflops in AI performance - all without a single GPU

By Louis Stone

Arm wrestles away the top spot

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DataStax unveils Vector, an AIOps service for Apache Cassandra

By Max Smolaks

Proprietary monitoring and automation features for the open source database

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Wipro, IBM to collaborate on cloud, AI and IoT

By Chuck Martin

The latest stage in a 20-year relationship

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Juniper pitches AI-based Wi-Fi for contact tracing in the office

By Tanwen Dawn-Hiscox

Suggests that workplaces can be made safer by using smart networking in combination with badges, apps and notification systems

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Research: Cloud will not become the standard for AI

By Chuck Martin

Analyst firm Omdia predicts that by 2025, cloud-based, pay-as-you-go AI software will account for just 20 percent of the $126 billion market

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Microsoft builds fifth fastest cloud supercomputer, exclusive to OpenAI

By Sebastian Moss

A massive system for a company making a massive bet

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The 5 petaflops Nvidia DGX A100 hopes to run your AI workloads

By Sebastian Moss

While HGX will do it for the cloud

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Nvidia unveils powerful Ampere A100 GPU for data centers and cloud

By Sebastian Moss

Signing up all the customers you could want

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Intel Capital invests in 11 AI and automation startups

By Sebastian Moss

$132m for American and Chinese businesses

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Xilinx plans ‘Adaptive Compute Research Clusters’ at universities around the world

By Sebastian Moss

Hardware donation will help explore what's possible with programmable logic devices

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The disruptive influence of FinTech in data centers

By AI Business

AI requires IT workloads unlike anything that financial organizations have seen before

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A closer look at building predictive models: The criticality of data access

By Jelani Harper

What is machine learning doing to your storage?

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IBM donates AI fairness and explainability tools to the Linux Foundation


Three projects move under the wing of the open source organization


Scoping machine learning projects: The six questions each analytics translator has to know


Innovative ML projects can only succeed if they manage to transform a business problem into clear tasks data scientists can work on

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