Artificial IntelliTools

  How one graduate student spent weeks fine-tuning a RAG system to bring MIT-level education to students in Resource constraints—and the surprising lessons learned along the way. The Challenge: Bringing World-Class Education Where the Internet Doesn't Reach Picture this: You're a high school student in rural Uganda. The nearest university is hours away, internet connectivity is sparse at best, and data costs more than your family can afford. Yet somewhere on the internet, MIT has published thousands of hours of world-class lectures covering everything from calculus to computer science—completely free. The problem? You can't access them. This is the gap that Emmanuel, a graduate student at Yeshiva University's Katz School, set out to bridge with AXAM—an AI-powered educational platform designed to work entirely offline. Think of it as having a knowledgeable teaching assistant in your pocket, one that can answer questions about complex academic topics without needing ...

  How one graduate student turned 7,600 educational videos into an intelligent, offline learning companion for resource-constrained schools The Dream That Started With a Question Emmanuel sat in his data analytics class at Yeshiva University's Katz School, watching his professor explain neural networks. As President of the Katz African Students Association, he couldn't help but think about students back home in Uganda and Rwanda—brilliant minds with limited access to quality educational resources. "What if," he wondered, "we could package MIT's entire course library into something that works without internet, runs on basic computers, and answers student questions like a patient teaching assistant?" That question launched a month-long technical odyssey that would teach him more about AI, education, and real-world constraints than any textbook ever could. The Raw Material: 7,600 Hours of MIT's Best Emmanuel's starting point was remarkable: ...

  Can artificial intelligence predict what will go viral? I built three neural networks to find out—and discovered something surprising about the limits of machine learning. The Challenge: Finding the Next Viral Hit Imagine you're an editor at a major online publication. You publish 50 articles today. Some will get 500 shares. A few might explode to 50,000. Most will land somewhere in between. The million-dollar question: Can you predict which articles will go viral before you invest your marketing budget? This isn't just an intellectual exercise. For publishers like Mashable, BuzzFeed, or Medium, getting this right means: Promoting the right content at the right time Maximizing return on advertising spend Understanding what resonates with audiences I set out to answer this question using neural networks and real data from 39,644 Mashable articles. What I discovered challenges everything you might assume about AI and prediction. The Data: 39,644 Articles, 60 Feat...