Category: AI

AI institutions develop heterogeneous models for specific tasks but face data scarcity challenges during training. Traditional Federated Learning (FL) supports only homogeneous model collaboration, which needs identical…

Daniel Rausch, Amazon’s vice president of Alexa and Echo, is in the midst of a major transition. More than a decade beyond the launch of Amazon’s Alexa, he’s been tasked with creating a new version of the marquee voice assistant, one…

In this tutorial, we walk you through building an enhanced web scraping tool that leverages BrightData’s powerful proxy network alongside Google’s Gemini API for intelligent data extraction. You’ll see how to structure your…

The Shift in Agentic AI System Needs

LLMs are widely admired for their human-like capabilities and conversational skills. However, with the rapid growth of agentic AI systems, LLMs are increasingly being utilized for repetitive,…

Modern large language models (LLMs) might write beautiful sonnets and elegant code, but they lack even a rudimentary ability to learn from experience.

Researchers at Massachusetts Institute of Technology (MIT) have now devised a way for LLMs to…

Godschild, who penned the fantasy novel The Hunter and The Hunted, says she’s been writing since childhood and goes through a lengthy process—plotting her manuscript years before putting pen to paper. A few days after seeing Aveyard’s…

Autoencoders and the Latent Space

Neural networks are designed to learn compressed representations of high-dimensional data, and autoencoders (AEs) are a widely-used example of such models. These systems employ an encoder-decoder…

Introduction: The Need for Efficient RL in LRMs

Reinforcement Learning RL is increasingly used to enhance LLMs, especially for reasoning tasks. These models, known as Large Reasoning Models (LRMs), generate intermediate…