ML & AI

General AI vs narrow AI: understanding the difference

What Do General AI and Narrow AI Actually Mean Without the Buzzwords People keep throwing AGI and Narrow AI everywhere, which makes everything sound mystical.Here is the truth in the smallest possible sentence. General AI thinks across any domain. Narrow AI works inside a box. That single idea clarifies so much! I remember the first […]

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Privacy Concerns with Machine Learning Data Usage: What You Need to Know

Your data is feeding AI models right now, and you probably didn’t say yes to it. I realized this when I tried to delete my old Facebook account last year. The representative told me my posts would be removed, but the patterns extracted from them? Already baked into their recommendation algorithms. Gone forever into the

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Recent Breakthroughs in Machine Learning Research

Machine learning isn’t slowing down — it’s evolving faster than we can blink. In the last 12 months alone, over 80% of ML papers introduced completely new architectures that didn’t exist just a year before. (Source: arXiv 2025 Research Trends) That’s wild, right? But here’s the catch — only a handful of those breakthroughs are

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Regulating Artificial Intelligence: Global Policies

What happens when machines get smarter than the rules that govern them?That’s the question the world is now scrambling to answer. Here’s the thing — AI isn’t some futuristic tech anymore. It’s everywhere. From the way your phone suggests words to how global banks detect fraud. But what’s shocking is that less than 10% of

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Edge Computing and Machine Learning Convergence

AI is hungry. Every second, millions of devices collect data that needs to be processed instantly. But here’s the catch: sending all that data to the cloud causes delays, bandwidth overload, and security headaches. That’s where edge computing swoops in — and when it teams up with machine learning, things get wild. Here’s a mind-blowing

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Feature Scaling in Machine Learning: The Trick Top Data Scientists Use

Ever trained a model that made zero sense — even though your dataset looked perfect?You cleaned it, encoded it, split it… and yet, accuracy tanked. You’re not alone.Most beginners miss one invisible step that separates amateurs from data scientists:👉 Feature Scaling. Now here’s the wild part — nearly 70% of failed ML experiments come from

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