Language Models Don't Understand. They Predict. And That Might Be Enough.
The debate between "genuine understanding" and "stochastic pattern matching" may be a false binary. When a model reasons through a novel problem, what exactly is happening under the hood?
Read entry →The Attention Mechanism Is the Most Important Invention of the Decade. Here's Why.
In 2017, a paper called "Attention Is All You Need" quietly rewrote the rules of machine learning. Eight years later, everything from medicine to law to software runs on its architecture.
AGI Won't Arrive. It'll Accumulate.
We keep imagining AGI as a singular event — a moment when a machine becomes smarter than us. But the evidence suggests something stranger: a slow, uneven diffusion we may not even notice.
Can a Model Be Creative? The Surprising Answer From Cognitive Science.
The Scaling Law That Changed Everything — And Its Limits
Read entry →For years, the AI industry operated on a simple faith: make the model bigger, train it on more data, and it will get smarter. Now, with diminishing returns creeping in, researchers are asking what comes after scale.
The Memory Problem: Why Context Windows Are AI's Achilles Heel
Every transformer model has a hard limit on how much it can hold in mind at once. This isn't a bug to be patched — it's a fundamental architectural constraint with profound implications for what AI can and cannot do.
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