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Run:ai Launches Full-Stack Solution for Hyper-Optimized Enterprise AI Built on NVIDIA DGX Systems

Run:ai, a leader in compute orchestration for AI workloads, announced the launch of the Run:ai MLOps Compute Platform (MCP) powered by NVIDIA DGX™  Systems, a complete, full-stack AI solution for enterprises. Built on NVIDIA DGX systems and using Run:ai Atlas software, Run:ai MCP is an end-to-end AI infrastructure platform that seamlessly orchestrates the hardware and software complexities of AI development and deployment into a single solution, accelerating a company’s ROI from artificial intelligence. 

Podcast Highlights: “Intel on AI”: Learning with AI

With over 15 years of experience in machine learning, neural networks, and computer vision, Microsoft’s Milena Marinova knows a thing or two about using AI for professional growth. In this podcast episode of “Intel on AI,” Milena shares lessons she’s learned during her career, including the challenges of developing AI products and the importance of data policy by design.

Video Highlights: Google Engineer on His Sentient AI Claim

Google Engineer Blake Lemoine (who worked for the company’s Responsible AI unit) joins Emily Chang of Bloomberg Technology in the video below to talk about some of the experiments he conducted that led him to believe that LaMDA (a Large Language Model) was a sentient AI, and to explain why he was placed on administrative leave and ultimately fired.

CoreWeave Among First Cloud Providers to Offer NVIDIA HGX H100 Supercomputers Set to Transform AI Landscape

CoreWeave, a specialized cloud provider built for large-scale GPU-accelerated workloads, announced it is among the first to offer cloud instances with NVIDIA HGX H100 supercomputing. CoreWeave, Amazon, Google, Microsoft and Oracle are the first cloud providers included in the launch of this groundbreaking AI platform.

AI Hiring Experts on President Biden’s AI Bill of Rights

A recent interdisciplinary study from NYU Tandon researchers explores the issue of accountable AI. The study reveals how resume format, LinkedIn URLs and other unexpected factors can influence AI personality prediction and affect hiring.

The Move Toward Green Machine Learning

A new study suggests tactics for machine learning engineers to cut their carbon emissions. Led by David Patterson, researchers at Google and UC Berkeley found that AI developers can shrink a model’s carbon footprint a thousand-fold by streamlining architecture, upgrading hardware, and using efficient data centers. 

ACM Global Technology Policy Council Releases Joint Statement on Principles for Responsible Algorithmic Systems by US and Europe Policy Committees 

The Association for Computing Machinery’s global Technology Policy Council (TPC) has released a new Statement on Principles for Responsible Algorithmic Systems authored jointly by its US (USTPC) and Europe Technology Policy Committees (Europe TPC). Recognizing that algorithmic systems are increasingly used by governments and companies to make or recommend decisions that have far-reaching effects on individuals, organizations and society, the ACM Statement lays out nine instrumental principles intended to foster fair, accurate, and beneficial algorithmic decision-making.

Video Highlights: Modernize your IBM Mainframe & Netezza With Databricks Lakehouse

In the video presentation below, learn from experts how to architect modern data pipelines to consolidate data from multiple IBM data sources into Databricks Lakehouse, using the state-of-the-art replication technique—Change Data Capture (CDC).

AWS Celebrates 5 Years of Innovation with Amazon SageMaker

In just 5 years, tens of thousands of customers have tapped Amazon SageMaker to create millions of models, train models with billions of parameters, and generate hundreds of billions of monthly predictions. The seeds of a machine learning (ML) paradigm shift were there for decades, but with the ready availability of virtually infinite compute capacity, […]

TruEra Launches First Automated Test Harness for ML Models with TruEra Diagnostics 2.0 Release

TruEra, which provides a suite of AI Quality management solutions for managing model performance, explainability, and societal impact, launched TruEra Diagnostics 2.0, a major update to its TruEra Diagnostics solution, incorporating the first-ever automated test harness for AI models that includes root cause analysis. The new systematic testing features in TruEra Diagnostics 2.0 help enterprises to get models into production faster by providing comprehensive model evaluation that promotes quality and transparency, accelerating model development and approval.