Amazon Bedrock simplifies generating high-quality categorical ground truth data for ML models, reducing costs and time. Using XML tags, it creates a balanced label dataset, as shown in a real-world example predicting support case categories.
Amazon Bedrock offers cross-Region inference for AI models, but strict access controls can hinder its functionality. Learn how to modify controls to enable seamless cross-Region inference and boost performance with practical examples. This feature optimizes resource utilization and performance by automatically routing traffic across multiple Regions, prioritizing the source Region for minimal l...
EduFi offers low-interest student loans in Pakistan, increasing access to college for many. The company uses AI for credit scoring and 'Study Now, Pay Later' system, easing financial burden.
Article: "Neural Network Quantile Regression Using C#." A unique approach to machine learning regression is quantile regression, particularly useful for scenarios with significant consequences for under-prediction. By utilizing a custom loss function, neural network quantile regression aims to predict values to a specified quantile, offering a promising method for accurate forecasting.
Amazon SageMaker Canvas offers no-code ML workflows, but some projects may require external dependencies. Learn how to incorporate custom scripts and dependencies from Amazon S3 into your SageMaker Canvas workflows for advanced data preparation and model deployment.
Designers Alex (Qian) Wan and Eli Ruoyong Hong discuss the challenges of translating high-context languages like Chinese and Japanese using Gen AI technology. They developed a Gen AI-powered translation browser extension to improve accuracy and context-awareness in translations, addressing the limitations of traditional tools like Google Translate.
ML Uncertainty: a Python package addressing the lack of uncertainty quantification in popular ML software. Designed to estimate uncertainties in predictions with only a few lines of code, making it computationally inexpensive and applicable to real-world scenarios with limited data.
In a powerful dystopian novel longlisted for the Women’s prize, Sara Hussein is jailed for her potential to commit crimes based on an AI security system. Despite being a seemingly ordinary museum archivist, Sara's "risk score" lands her in a women's retention center where her fate lies in the hands of her guards.
Amazon SageMaker JumpStart offers pre-trained models and new capabilities for organizations to create, manage, and fine-tune their ML models securely. Enhanced private hub features enable enterprises to balance standardization and customization for successful AI implementation.
Supply Chain Data Scientist explores LangChain and LangGraph to build AI agents. Leveraging n8n for easy deployment of AI-powered Control Tower in Supply Chain Analytics.
Generative AI, led by Stability AI's SD3.5 Large model, is transforming game environment creation with high-quality, diverse image generation. This innovation accelerates design cycles and empowers users to create immersive virtual worlds, promising a new era of AI-assisted gaming creativity.
The MIT Maritime Consortium aims to reduce greenhouse gas emissions in the maritime shipping industry through innovative technologies and interdisciplinary research. Led by MIT professors Sapsis and Christia, the consortium includes key industry players and focuses on areas such as nuclear technology, autonomous operation, cybersecurity, and 3D printing for onboard manufacturing.
The transition to a standardized approach for AI tool calling, similar to REST APIs, is crucial for order in the industry. Model Context Protocol (MCP) aims to provide context for AI models in a standard way, democratizing tool calling and enhancing system safety.
The Public Accounts Committee warns of risks to government efficiency from outdated technology and digital skills shortages. Over 20 legacy IT systems still lack funding for improvement, hindering AI integration efforts.
AI-based PawMatchAI can identify 124 dog breeds by analyzing structured traits like body proportions and fur texture, inspired by human expert recognition methods. Unlike traditional CNNs, this model separates key characteristics for clearer interpretability, revolutionizing AI-based breed identification.