NEWS IN BRIEF: AI/ML FRESH UPDATES

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Mastering GitHub Actions for Data Workflow Automation

GitHub Actions, a CI/CD tool, is not just for software - it automates data workflows, from setting up environments to deploying ML models. Free and easy to use, it offers pre-built actions and community support for automating tasks within repositories.

Adapting Graph Neural Networks: GraphSAGE in Action

Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs) have limitations with large graphs and changing structures. GraphSAGE offers a solution by sampling neighbors and using aggregation functions for faster and scalable training.

Thinktank warns UK on AI laws

Tony Blair Institute advises UK to relax copyright laws for AI innovation, warns of strain on US relations and potential tariffs. Enforcing stricter licensing rules may threaten national security interests, says thinktank.

Centralizing AI Model Inference: The Key to Efficiency

AI models are replacing traditional algorithms in algorithmic pipelines due to their higher resource requirements. Centralized inference servers may improve efficiency in processing large-scale inputs through deep learning models, as shown in a toy experiment using a ResNet-152 image classifier on 1,000 images.

Optimizing with PSO and EO

Algorithm combining PSO with EO, EPSO, performs similarly to PSO and EO, not significantly better. Slow for practical use, but shows promise in training a KRR prediction system.

Building Attention Mechanism from Scratch

Attention mechanism, crucial in Machine Translation, helps RNNs overcome challenges, leading to the rise of Transformers. Self-attention in Transformers involves key, value, and query vectors to focus on important elements within a sequence.

Crafting Your Supply Chain Analytics Portfolio

Supply Chain Analytics is crucial in navigating disruptions and uncertainties in supply chains. Samir Saci shares insights and practical case studies in his comprehensive Supply Chain Analytics Cheat Sheet to help improve profitability and optimize operations.

Evolutionary Optimization Failure in Support Vector Regression

Support Vector Regression (SVR) and Support Vector Machine (SVM) were popular in the 1990s but have limitations. SVR's complexity and scalability issues are addressed by the kernel trick, with the radial basis function being a common choice. Training SVR models requires specialized algorithms like sequential minimal optimization (SMO), and attempts to use evolutionary optimization have not been...