NEWS IN BRIEF: AI/ML FRESH UPDATES

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AI-driven Digital Twins Enhance Urban Tree Monitoring

MIT, Google, and Purdue University develop Tree-D Fusion, merging AI and tree-growth models to create 3D urban tree models. Predictive capabilities could revolutionize urban forest management with proactive planning for climate change adaptation.

AI Jesus: The Future of Faith?

Peter’s chapel in Lucerne replaces priest with AI Jesus speaking 100 languages. Theologian Marco Schmid calls it an experiment to gauge public interest and reactions.

Ethical AI: Amazon Bedrock Batch Inference

Amazon Bedrock offers high-performing AI models from top companies like AI21 Labs and Meta through a single API. Batch inference in Amazon Bedrock enables cost-effective processing of large data volumes with ethical AI guardrails.

Effortless k-NN Regression in C#

Summary: Microsoft Visual Studio Magazine's November 2024 edition features a demo of k-NN regression using C#, known for simplicity and interpretability. The technique predicts numeric values based on closest training data, with a demo showcasing accuracy and prediction process.

Nvidia: Unstoppable Growth in AI Chips

Nvidia, the AI chipmaker, impresses investors with $35bn revenue in latest quarterly results, projecting 70% growth ahead. Profits more than double year-over-year, with a 94% surge in revenue from the previous year.

AI Pets: A Fluffy Replacement?

Casio's Moflin AI companion robot builds relationships without needing food or a litter tray. Priced at £300, it aims to comfort or alienate its owners.

Mastering Data-Driven Customer Management

Building a CBM System can optimize pricing, predict future revenue, and enhance decision-making through ELT, Churn Modelling, and Dashboards. Advanced modules can further boost value generation, giving your company a competitive edge.

Dreaming Robots: A Path to Learning?

MIT CSAIL researchers developed LucidSim, using generative AI and physics simulators to train robots in diverse virtual environments, bridging the sim-to-real gap in robot learning. The idea sparked outside a Cambridge taqueria, leading to a breakthrough in creating expert-level robot performance without real-world data.