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In this blog, we’ll be looking at one of the “sharp bits” of JAX: pseudorandomness, and see if we can’t smooth it out a little. JAX is an accelerated linear algebra library that forms the backbone of some modern python based scientific and machine learning libraries (things like Google’s Gemini were built on JAX). Personally I’m a fan.
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I was recently experimenting with the linear separability of embeddings, and showed a graph which indicated the embeddings were substantially more linearly separable than the quantized embeddings. One person commented that this didn’t make sense, and the quantized vectors should be as separable as the non-quantized vectors. That didn’t seem right to me, but on the spot I couldn’t think of a trivial counter example. In this blog, I present two.
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As the AI hype train has progressed and consumed upwards of 35% of the US economy (which surely is not a problem), a growing sentiment has emerged: that this is a bubble. This is probably now even the dominant opinion: that given the scale of investment, the large valuations, the lack of returns, and the slow progress, this constitutes a bubble. And bubbles always pop.