NEWS IN BRIEF: AI/ML FRESH UPDATES

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Media Concerns: OpenAI's Content Deals Stir Controversy

OpenAI signs deals with The Atlantic and Vox Media to license editorial content for ChatGPT training, sparking backlash from writers and unions. Unions express alarm and concern over lack of transparency and potential impact on members' work and ethical considerations.

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.

Divergent AI Applications

Choosing the right AI use case is crucial for success. AI can be valuable even with moderate performance, offering unique solutions. Examples include Sensor Fusion and Generative AI in everyday products.

Unlocking the Power of Evolutionary Algorithms

Evolutionary Algorithms (EAs) have limited math foundation, leading to lower prestige and limited research topics compared to classical algorithms. EAs face barriers due to simplicity, resulting in fewer rigorous studies and less exploration potential.

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.

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.

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.