Lots of non-chatbot uses in property management. Auditing leases vs. payment ledgers. Classifying maintenance work orders. Creating work orders from inspections (photos + text). Scheduling vendors to fix these issues. Etc.
They say "thus, on average, about two-thirds of cats preferred to sleep on the left side of their body with their left shoulder down", and their image for leftward lateral bias shows this. So I guess leftward means "lying on their left side", not "curling left".
But, they suggest this is because "Upon awakening, a leftward sleeping position would provide a fast left visual field view of objects", which seems suspect. When my cats sleep on their left, it's their left eye that's obscured by their paw, and their right eye that has a better field of view!
> Here, I don't think it's even useful to look at this problem in electronic terms
I always thought this problem was a funny choice for the comic, because it’s not that esoteric! It’s equivalent to asking about a 2d simple random walk on a lattice, which is fairly common. And in general the electrical network <-> random walk correspondence is a useful perspective too
This seems unusually shallow for the hedgehog review. I thought we'd largely moved on from this sort of sentimental, "I can't get good outputs therefore nobody can" style essay -- not to mention the water use argument! They've published far better writing on LLMs too: see "Language Machinery" from fall 23 [1]
Johnson-lindenstrauss lemma [1] for anyone curious. But you can only map to k>8(\ln N)/\varepsilon ^{2}} if you want to preserve distances within a factor of \varepsilon with a JL-transform. This is tight up to a constant factor too.
I always wondered: if we want to preserve distances between a billion points within 10%, that would mean we need ~18k dimensions. 1% would be 1.8m. Is there a stronger version of the lemma for points that are well spread out? Or are embeddings really just fine with low precision for the distance?
https://planetscale.com/benchmarks/aurora
Seems a bit better, but they benchmarked on a kind of small db (500gb db / db.r8g.xlarge)