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Thinking beyond localized activations

Every network you’ve trained almost certainly leans on the same handful of activation functions — ReLU, GELU, SiLU, tanh. They differ in shape, but they share one quiet assumption: the nonlinearity itself is fixed. You pick one function, wide enough to cover the whole range of inputs a neuron will ever see, bolt it on after every layer, and let ...

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வேதாந்தக் குறிப்புகள் — குருமுகமாகக் கேட்டறிந்தவை

गुरुर्ब्रह्मा गुरुर्विष्णुः गुरुर्देवो महेश्वरः । गुरुः साक्षात् परं ब्रह्म तस्मै श्रीगुरवे नमः ॥ guru-r-brahmā guru-r-viṣṇuḥ guru-r-devo maheśvaraḥ, guruḥ sākṣāt paraṁ brahma tasmai śrī-gurave namaḥ. குரு பிரம்மா; குரு விஷ்ணு; குரு தேவனாகிய மகேஸ்வரன்; குருவே சாட்சாத் பரப்பிரம்மம்; அத்தகைய குருவுக்கு நமஸ்காரம். ஸ்ரீ சங்கரானந்தா இக்கட...

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More Is Different

When we scale a neural network to billions of parameters, new capabilities emerge. But we still lack a complete explanation of why certain capabilities emerge or how they arise. Well, this is not unique to neural networks! I recently came across a beautiful essay, “More Is Different,” by Philip W. Anderson. Anderson argued that as systems becom...

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Open Agents for Building Interactive Simulations

Recently, I built a set of interactive simulations for teaching math and science concepts to kids and students. However, I have used Claude to generate such simulations. I wondered, can we build such applets using open-weight models? It turns out that we CAN. We do not need very big models with hundreds of billions of parameters. With an agentic...

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Building Interactive Simulations for My Daughter

My 6-year-old daughter asked me about the phases of the moon. I tried explaining it using pen and paper, hand-waving in the air — she got a bit of it. Like every kid, she watches space cartoons and videos on YouTube. Suddenly I thought of building an interactive simulation like in PhET. I searched PhET, and I didn’t find any app for explaining m...

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