How Can AI Id a Cat?

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

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.

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

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

If you want something mostly premade,go get an autoslide. If you want to do it completely from scratch:

1. RFID/bluetooth proximity is much easier to work with than camera + rpi + AI. For the usecase you are talking about, AI is not just overkill, but will make it actively harder to achieve your goal

2. Locking is pretty easy depending on motor mechanism - either a cheap relay'd magnetic lock, or simply a motor that can't be backdriven easily.

Motor wise, you can either use the rack and pinion style that autoslide does, or a simple linear motor if you don't want to deal with gear tracks.

Overall, i went the autoslide route and had it all set up and working in an hour or two.

The article glosses over activation functions, which - if non-polynomial - give the entire neural networks non-linearity. A major inflection point was proving that neural networks architectures even with very few layers (as small as one) can approximate any continuous function.

https://en.m.wikipedia.org/wiki/Universal_approximation_theo...

So, common ground with a lot of Hacker News audience?

Don't take me too seriously here, and not to excuse anything but what would these people be doing if they weren't data labeling? How would they be treated differently?

Presumably, they'd be working for some other multinational, because overall their quality of living is better than working at whatever other local industry exists?

The data labeling job itself strikes me as something dystopian. As if we're the work mules for our AI overlords.