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.

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.

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 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.

Unleashing Vision Language Models

VLMs combine text and visual inputs for tasks like VQA and Image Captioning, bridging the gap between textual and visual data. Techniques for prompting VLMs include zero-shot, few-shot, and object detection guided prompting, enhancing models' understanding of tasks.

Enhancing AI Performance in Uncertain Conditions

Researchers from MIT and others discovered the indoor training effect: AI agents trained in less noisy environments outperformed those trained in noisy ones, challenging conventional wisdom. The study, presented at the AAAI Conference, suggests new approaches to training AI agents for better performance.