NEWS IN BRIEF: AI/ML FRESH UPDATES

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Unlocking Keir Starmer's Economic Potential: The Stephen Collins Cartoon

Tesla's new self-driving technology, Full Self-Driving (FSD) beta, has been met with mixed reviews, with some testers reporting significant improvements in performance and others highlighting safety concerns. The FSD beta represents a significant step forward in autonomous driving technology, but questions remain about its reliability and potential risks.

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.

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.

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.

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.

Unleashing Hidden Patient Insights with AI and Amazon Bedrock

Aetion leverages real-world data to uncover hidden insights with Smart Subgroups and generative AI, transforming patient journeys into evidence. Aetion's use of Amazon Bedrock and Anthropic's Claude 3 LLMs enables users to interact with Smart Subgroups using natural language queries, accelerating hypothesis generation and evidence creation.

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.