NEWS IN BRIEF: AI/ML FRESH UPDATES

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Mastering Logistic Regression in C#

Article: "Logistic Regression with Batch SGD Training and Weight Decay Using C#". It explains how logistic regression is easy to implement, works well with small and large datasets, and provides highly interpretable results. The demo program uses stochastic gradient descent with batch training and weight decay for accurate predictions.

AI vs. Art Critic: The Unreplaceable Eye

The AI version of Brian Sewell's review lacks his authentic voice, disappointing readers. Sewell's posh voice and unique style are sorely missed in the London Standard's attempt to recreate his writing.

Deepfake Scam Targets US Senator

FBI probes AI deepfake impersonating Dmytro Kuleba questioning Ben Cardin in suspected election interference attempt. Senator Cardin raises suspicions during Zoom call with imposter posing as Ukraine's former foreign minister.

Embracing the Circular Fashion Economy

Fashion retailer explores circular rental model using data analytics to reduce environmental footprint and improve resource efficiency. Data scientist assesses operational challenges and metrics crucial for transitioning to a circular economy, aiding sustainability and logistics teams in building a solid business case for top management approval.

Save Your Money: A Guide to Dutch Exam Benchmarking

A machine learning engineer and PhD researcher conducted Dutch-specific benchmarking of LLMs, comparing models like o1-preview and GPT-4o on real Dutch exam questions. The study highlights the importance of validating AI models for Dutch-language tasks and offers valuable insights for companies targeting the Dutch market.

AI Turbocharges Data Science Workflows

NVIDIA's RAPIDS cuDF library accelerates pandas by up to 100x on RTX hardware, improving data processing speed for data scientists. Data scientists can now use their preferred code base without sacrificing efficiency, thanks to RAPIDS cuDF's GPU-accelerated Python libraries.

Mastering LLM Fine-tuning: Your FAQs Answered

LLM Fine-Tuning FAQs: Understand the nuances of fine-tuning large language models and when to use it effectively for AI projects. Fine-tuning can lower inference costs and adapt model outputs through prompt engineering, but its effectiveness depends on the use case and data volume.

Unlocking the Potential: Meta's Llama Vision Models

Llama 3.2 models with vision capabilities are now available in Amazon SageMaker JumpStart and Amazon Bedrock, expanding their traditional text-only applications. These state-of-the-art generative AI models offer improved performance, multilingual support, and are suitable for a wide range of vision-based use cases.