NEWS IN BRIEF: AI/ML FRESH UPDATES

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AI vs Software Engineering: Unveiling the Key Differences

AI projects differ from traditional software development in their iterative approach, emphasizing discovery and adaptation. The AI development lifecycle includes problem definition, data preparation, model development, evaluation, deployment, and monitoring.

Revolutionizing Supply Chains with Amazon Bedrock AI

Amazon Bedrock utilizes generative AI to create intelligent supply chain solutions, mitigating risks and improving agility. Its visual workflow builder connects data sources and AWS services for end-to-end solutions, ensuring resilience in the face of disruptions.

Efficient Email Classification with Amazon Bedrock

Foundation models (FMs) are surpassing supervised learning in text classification tasks, with benefits like rapid development and extensibility using Amazon Bedrock. Travelers and GenAIIC collaborated to build an FM-based classifier for automating service request emails, saving thousands of hours with 91% accuracy.

Unveiling E-commerce Inequality

A 6-year Shopify case study reveals the delicate balance between product focus and diversification for optimal business success. Learn how understanding concentration in your product portfolio impacts crucial decisions, with practical strategies and interactive visualizations provided.

Unveiling RAG: Revolutionizing Content Generation

Retrieval-augmented generation (RAG) enhances generative AI with specific data sources, improving accuracy and trustworthiness. RAG helps models provide authoritative answers, clear ambiguity, and prevent incorrect responses, revolutionizing user trust.

Mastering Gradient Boosting Regression in C#

Article discusses Gradient Boosting Regression Using C# in Microsoft Visual Studio Magazine, presenting a demo of a simple version compared to XGBoost, LightGBM, and CatBoost. The demo showcases the step-by-step process of predicting values with gradient boosting regression.

Maximizing Accuracy: Pruning MNIST Data for 99%

Data-centric AI can create efficient models; using just 10% of data achieved over 98% accuracy in MNIST experiments. Pruning with "furthest-from-centroid" selection strategy improved model accuracy by selecting unique, diverse examples.

Maximizing Marketing Impact: Contextual Bandit Simulation

Bandit algorithm vs A/B test: When A/B tests fail due to multiple variants or one-off campaigns, bandit algorithms offer a more efficient solution by focusing budget on the best performing ad variant in real-time. Bandit algorithms maximize rewards by serving the ad variant with the highest KPI, making them ideal for campaigns with numerous treatments or special events.

SoftBank Eyes $25bn Investment in OpenAI

SoftBank in talks to invest up to $25bn in OpenAI, becoming largest financial backer of ChatGPT startup. Potential $15-25bn deal with San Francisco-based company reported by Financial Times.