NEWS IN BRIEF: AI/ML FRESH UPDATES

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The Hidden Dangers of Blindly A/B Testing Everything

Leading voices in experimentation suggest that you test everything, but inconvenient truths about A/B testing reveal its shortcomings. Companies like Google, Amazon, and Netflix have successfully implemented A/B testing, but blindly following their rules may lead to confusion and disaster for other businesses.

The Rise of Gaussian Splatting: Revolutionizing the Avatar Space

The recent surge in Gaussian Splatting papers, including GaussianAvatars and MonoGaussianAvatar, is revolutionizing the field of digital humans. Gaussian splatting offers visually stunning quality, high frame rates, and easy editability, making it a powerful method for representing 3D scenes.

Streamlining ML Operations at Scale with PwC's Machine Learning Ops Accelerator

PwC Australia's Machine Learning Ops Accelerator, built on AWS native services, streamlines the process of taking ML models from development to production deployment at scale. The accelerator includes seven key integrated capabilities to enable continuous integration, continuous delivery, continuous training, and continuous monitoring of ML use cases.

The AI Chronicles: Unraveling the Hype and Impact of 2023

Generative AI has taken the tech industry by storm in 2023, dominating headlines and sparking debates. Amidst the emergence of AI-related figures, confusion arises for non-technical individuals on whom to trust, which AI products to use, and whether AI poses a threat to their lives and jobs. Additionally, the relentless pace of machine learning research continues to bewilder experts, prompting ...

ISO 42001: Elevating Responsible AI for Global Trust

AWS highlights the importance of responsible AI practices and announces its commitment to adopting ISO 42001, an international standard for managing AI systems within organizations, to earn public trust. AWS is actively collaborating with international stakeholders to improve AI standards and encourages organizations to demonstrate their commitment to excellence in responsibly developing and de...

Unveiling the Anomalies: A Comparative Analysis of Outlier Detection Methods

This article explores outlier detection algorithms in machine learning and their application to Major League Baseball's 2023 batting statistics. The four algorithms compared are Elliptic Envelope, Local Outlier Factor, One-Class Support Vector Machine with Stochastic Gradient Descent, and Isolation Forest. The goal is to gain insight into their behavior and limitations in order to determine whi...

Optimizing Rust Compiler Settings for Maximum Performance

This article explains how to benchmark using the criterion crate and how to benchmark across different compiler settings, providing insights on performance effects and comparisons across CPUs. The range-set-blaze crate is used as an example to measure SIMD settings, optimization levels, and various input lengths.