NEWS IN BRIEF: AI/ML FRESH UPDATES

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Grace Hopper Superchip Boosts Murex MX.3 Analytics

Murex, a Paris-based trading software company, is testing NVIDIA's Grace Hopper Superchip for faster and more energy-efficient risk calculations. Grace Hopper offers significant performance improvements, including a 4x reduction in energy consumption and a 7x increase in speed for XVA calculations.

Enhancing Genomic Language Models with AWS HealthOmics and SageMaker

Genomic language models like HyenaDNA use transformer architecture to interpret DNA language for insights in genomics, healthcare, and agriculture. AWS HealthOmics storage and Amazon Sagemaker enable cost-effective training and deployment of these models, driving innovation in precision medicine and biotechnology.

Win $10m by Talking to Animals!

AI may enable real interspecies communication, as Tel Aviv University joins $10m Coller Dolittle Challenge. Scientists invited to create two-way conversations with animals in groundbreaking competition.

AI Powerhouse Alliance Takes on Nvidia

Major tech companies like Google, Microsoft, and Meta form UALink group to develop new AI accelerator chip interconnect standard, challenging Nvidia's NVLink dominance. UALink aims to create open standard for AI hardware advancements, enabling collaboration and breaking free from proprietary ecosystems like Nvidia's.

Salesforce's AI Competition Concerns

Salesforce faces potential $48bn market value loss amid concerns over low revenue growth forecast and competition from rival AI offerings. Shares dropped 18% after disappointing quarterly results below expectations for the first time in 15 years.

AI Image Goes Viral: The Rafah Phenomenon

An AI-generated graphic depicting refugee tents in Rafah becomes viral during Israel-Gaza war, with over 45m shares on Instagram. The image also gains traction on TikTok and Twitter, reaching millions of views and retweets.

Decoding the Secrets of Large Language Models

Anthropic's recent paper delves into Mechanistic Interpretability of Large Language Models, revealing how neural networks represent meaningful concepts via directions in activation space. The study provides evidence that interpretable features correlate with specific directions, impacting the output of the model.

Optimizing LightGBM for Target Variable Intervals

A LightGBM regression model predicts income accuracy within intervals, demonstrating the model's effectiveness with synthetic data. The model showcases accuracy for various income ranges, highlighting the importance of specifying target value proximity for correct predictions.