Small but effective visual cues, smooth and carefully chromatic.

I am struck by the conceptual framework of classification tasks so snappily rendering clear categories from such fuzziness.

Lottie: https://lottiefiles.com/

ExAI feels like a better shortening, both for clarity and given that xAI is a company already.

This article seemed really basic, no insight other than "it learns the high dimensional manifold on which cat images lie, thus separating cats from non-cats" (not that simple explanations are bad, but Quanta articles seem to be getting more watered down over time).

The real question is whether we can get some insight as to how exactly it's able to do this. For convolution neural networks it turns out that you can isolate and study the behavior of individual circuits and try to understand what "traditional image processing" function they perform, and that gives some decent intuition: https://distill.pub/2020/circuits/ - CNNs become less mysterious when you break them down as being decomposed into "edge detectors, curve detectors, shape classifiers, etc."

For LLMs it's a bit harder, but anthropic did some research in this vein.

I wrote the CatBench vector search playground toy app exactly for this reason! [1] ("cat-similarity search for recommendation engines and cat-fraud detection"). I built it both for learning & fun, but also it's useful for demoing vector search functionality, plugged in to regular RDBMS application schemas in business context. I used cats & dogs as it's something everyone understands, instead of diving deep into some narrow industry vertical specific use case.

[1]: https://tanelpoder.com/posts/catbench-vector-search-query-th...

I imagine when they run out of other sensors to add to our phones, they’ll add chip readers so you can just scan for the implanted microchip on a cat you encounter. (said semi-sarcastically since the tech requires close proximity between animal and reader which most cats you encounter on the street will not countenance)

Yes, I've worked in this space for dogs (for re-identifying animals that have been vaccinated for rabies). It's a very difficult problem, but mostly because getting/scraping good training data is difficult. You really want lots of paired images of the same animal and that's hard compared to searching for "cat". Plus the usual challenges: animals don't like to stay still so getting good pictures is hard and users must have good guidance for lighting/pose to get the best results. Human facial recognition benefits from strong commercial interest and the most robust methods rely on extras like 3D scanning.

Tricks include facial alignment + cropping and very strong constraints on orientation to make sure you have a good frontal image (apps will give users photo alignment markers). Otherwise it's a standard visual seatch. Run a face extraction model to get the crop, warp to standard key points, compute the crop embedding, store in a database and do a nearest neighbour lookup.

There are a few startups doing this. Also look at PetFace which was a benchmark released a year or so ago. Not a huge amount of work in this area compared to humans, but it's of interest to people like cattle farmers as well.

https://github.com/mapooon/PetFace

Came to say Apple also did a great job on tagging my bois who are both grey-ish cats, even in pictures they faced backward, no idea how they did that

The same with Google photos, it groups similar cats as just one. Fun fact does the same for human twins

Take a look at SureFlap and OnlyCat. They use RFID chips in the cats.