Organizations have a wealth of unstructured data that most AI models can’t yet read. Preparing and contextualizing this data is essential for moving from AI experiments to measurable results.

In a sense, it sounds like that’s another facet of computational thinking that’s more relevant in the age of AI—the abstractions of statistics and probability in addition to algorithms and data ...

With Claude Code using Next.js and Superbase you can ship faster and quickly deploy for easy sharing and monitization ...

Abstract: Graph neural networks (GNNs), as a cutting-edge technology in deep learning, perform particularly well in various tasks that process graph structure data. However, their foundation on ...

New research reveals why even state-of-the-art large language models stumble on seemingly easy tasks—and what it takes to fix it ...

This tool has been developed using both LM Studio and Ollama as LLM providers. The idea behind using a local LLM, like Google's Gemma-3 1B, is data privacy and low cost. In addition, with a good LLM a ...

Abstract: To partition samples into distinct clusters, Fuzzy C-Means (FCM) calculates the membership degrees of samples to cluster centers and provides soft labels, gaining significant attention in ...

Developers are navigating confusing gaps between expectation and reality. So are the rest of us. Depending who you ask, AI-powered coding is either giving software developers an unprecedented ...

Artificial intelligence (AI) agents are a breeze to create using Microsoft Copilot Studio, and almost just as easy to manipulate into divulging sensitive corporate data. Despite broad security ...