{"version":"1.0","type":"rich","provider_name":"Acast","provider_url":"https://acast.com","height":250,"width":700,"html":"<iframe src=\"https://embed.acast.com/$/68ab182de2f63983a7587241/6954d9084833761f1d2213a5?\" frameBorder=\"0\" width=\"700\" height=\"250\"></iframe>","title":"E95 - Supercharging LLMs: The MIT Breakthrough","thumbnail_width":200,"thumbnail_height":200,"thumbnail_url":"https://open-images.acast.com/shows/68ab182de2f63983a7587241/1767167482777-1efe3f08-daea-45a1-a990-26f8e2799b82.jpeg?height=200","description":"<h3>Is \"bigger\" always \"better\" in the world of AI? MIT researchers say: Not necessarily. </h3><h3><br></h3><h3>For years, the race has been about adding more parameters and burning more GPU power. But a new paper released this week introduces a novel way to significantly increase the capabilities of Large Language Models (LLMs) without just throwing more compute at the problem.</h3><h3><br></h3><h3>In this episode, we unpack this game-changing approach.</h3><h3><br></h3><h3>🎙️ We cover: 🔹 The limitations of current scaling laws. 🔹 The \"Secret Sauce\" behind MIT's new capability boost. 🔹 What this means for the next generation of AI applications.</h3><h3><br></h3><h3>Tune in to understand the future of efficient AI. </h3><p><br></p><p>source: https://news.mit.edu/2025/new-way-to-increase-large-language-model-capabilities-1217</p><p><br></p><p>#LargeLanguageModels #GenerativeAI #MITNews #TechInnovation #AIResearch #FutureOfWork #MachineLearning #NLP #TechPodcast</p>","author_name":"Farhad Fatehi"}