NEWS IN BRIEF: AI/ML FRESH UPDATES

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AI Friend: Can Zuckerberg Solve Loneliness?

Mark Zuckerberg promotes AI for friendships, envisioning a future where people befriend systems instead of humans. Online discussions about relationships with AI therapists are becoming more common, blurring the line between real and artificial connections.

Supercharge Your Models: The Power of Ensembling

Bagging and boosting are essential ensemble techniques in machine learning, improving model stability and reducing bias in weak learners. Ensembling combines predictions from multiple models to create powerful models, with bagging reducing variance and boosting iteratively improving on errors.

Decoding AI Transformers: A Layman's Guide

An article on Pure AI simplifies AI Large Language Model Transformers using a factory analogy, making it accessible for non-engineers and business professionals. The analogy breaks down the process into steps like Loading Dock Input, Material Sorters, and Final Assemblers, offering a clear understanding of how Transformers work.

AI learns to talk like humans

Study finds AI agents can develop human-like social norms when communicating in groups, like humans. Research by City St George’s, University of London and IT University of Copenhagen.

GOP Pushes for 10-Year Ban on State AI Regulations

US Republicans seek to block state laws regulating AI for 10 years in budget bill, aiming to prevent guardrails on automated decision-making systems. Proposed provision in House bill would restrict any state or local regulation of AI models or systems unless to facilitate deployment.

Demystifying the Parquet File Format

Apache Parquet is a game-changer in data storage, offering data compression, columnar storage, language flexibility, open-source format, and support for complex data types. Unlike traditional row-based storage, Parquet's column-based approach allows for faster data read operations, optimizing analytics workloads.

HyperPod recipes for customizing DeepSeek-R1 671b model

DeepSeek AI's DeepSeek-R1 model with 671 billion parameters showcases strong few-shot learning capabilities, prompting customization for various business applications. SageMaker HyperPod recipes streamline the fine-tuning process, offering optimized solutions for organizations seeking to enhance model performance and adaptability.