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Comment by iambenm | Hacker News Reader

Hmm. The project uses BirdNET-Go, which does inference locally on a Raspberry Pi. Then it looks up the species against a set of already existing illustrations. So in this case it seems like the "ultra expensive, data-center-driven AI" is not really involved in the core loop of this project.

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The local inference is a product that came out of the data-center-driven AI.

What a shame that we get a bird ckassifier by the samle technology we use to recognize cancer and other illnesses.

What a shame that you use that excuse to offset all other evils of AI, without even having a hint of how many patients were saved by AI. Just like how oil salesmen and nukes makers say that they solve some great problem of the world.

> came out of the data-center-driven

should we tell them how hacker news gets onto their computer?

You assume that I assume that HN doesn't require data centers?

This is incorrect; the local model is a small dense neural network called BirdNET that would not have been referred to "AI" when it was published in 2021. The model outputs a bird species probability distribution and the web application displays an existing image file of the most probable bird on the screen. This is a lovely example of simple, offline, and fun project using a straightforward machine learning model.

Ok, so why didn't this small dense model come up, say, 10 years back? Oh it needed all the AI evolution and concepts that were powered and evolved inside the data centers. But we still want to claim that it has nothing to do with the Big AI.

Incorrect. As others have pointed out, BirdNet is a “traditional” neural network. The amount of compute needed to train something like this is many orders of magnitude less than an LLM. Something like this could be trained on local hardware with enough juice, or by renting a handful of GPU’s

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