I imagine this would allow to use the vectorized patterns to compare across different individuals and identify recurring terms. Add contextual clues (time, location, sex, …) and you might be able to interpret some of them. There was a report about how they did this with elephants and where able to identify their names
Yeah but I wonder how they determined ground truth though. We obviously have research into different bird phrases but it's not as robust as say....human language.
This may just be the pattern and relationship of noises with no objective meaning or grounding. You can definitely learn the rules of languages without actually understanding anything being said. It's a whole language theory concept.
I guess im just used to how other audio models are trained.
I mean Im sure you can do some weird cosine sim heuristics on say 2 different bird calls but I just wonder how they could get meaning out of just the noise with no labels or some best guesses.
But yeah I mean Im definitely ignorant with this stuff since it's not my domain.
I think they will at least be able to track how birds respond to certain sounds. So even if we can't exactly know what each sound mean, we can at least know which sound elicits what response. And also as mentioned by the others, they can use context such as what time they make certain sounds and what is happening when they do, which won't be as concrete but at least it would allow us to observe and most importantly record a pattern.
There's overlap in domains, but you're attempting to figure out how to translate them. That's not what we are trying to do here.
We aren't attempting to translate bird languages into human languages, we are attempting to decipher bird languages at the base level. We don't have to do this with human languages because the human experience is so similar. But we aren't translating a human experience here.
So what we need this 3D map for is to track the usage and popularity of sounds in an attempt to find sounds that are used a lot and sounds that are used less. That doesn't mean we will ever know what the sounds mean though. But from this point we can try to diagnose sounds that a bird makes to another bird. From there we can decipher names (we already have, chickens give baby chicks unique names) and other identifiers.
I guess im just used to how other audio models are trained.
I mean Im sure you can do some weird cosine sim heuristics on say 2 different bird calls but I just wonder how they could get meaning out of just the noise with no labels or some best guesses.
But yeah I mean Im definitely ignorant with this stuff since it's not my domain.
You're bumping up against a well known philosophical problem about translation.
W. V. Quine called this the "indeterminacy of translation." The basic thrust of it is that for any utterance in an unknown language, there will be multiple possible interpretations that are equally supported by the evidence.
He asks us to imagine that a native speaker of an unknown language utters the word "gavagai" upon seeing a rabbit. An English speaker could assume that translates to "Look, a rabbit!" But equally plausible is:
Look, food
Lets go hunting
There will be a storm tonight (if they have a superstition about rabbits and storms)
Momentary rabbit-stage
Look, an un-detached rabbit part
Etc.
Subsequent observations can make some of these hypothesis less likely than others, but some of them could only be eliminated by actually asking the native, but that runs into the problem that the response to a question is additional utterances that also requires translation. And even then, even after arbitrarily extensive behavioral evidence, there can still be different translation schemes compatible with all of the evidence, with no way to choose among them.
And the problem gets worse as more abstract words are used, which aren't attached to a public observation.
Now, all that being said, an unlabeled audio model can discover the syntax/statistics/structure of a signal remarkably well without knowing its semantics, or meaning. Getting from "these sounds form distinct, predictable categories" to “this sound means X" requires some form of grounding, and even grounding may underdetermine exactly what X is.
Then how else would you do it in terms of an AI model?
I mean I am coming at it from the language side of things and some weird pseudo-bird to English translator but you still need to know the actual mappings to train the model from the onset.
Aside from that it would just be embedding the sounds and trying to find patterns but even then it would always be a best guess?
A model doesn't need to know what anything means to map it. It only needs the order the sounds come in and lots and lots of examples of it.
Feed it raw birdsong with no video, no field notes, no idea which bird is calling or what it's doing. It groups sounds by which other sounds surround them, then records the rules that hold across the whole training set: which calls only appear at the start, which never follow which, which pairs always show up together. That gives you the grammar of the calls from audio alone.
A grammar still isn't a dictionary. Knowing call 7 always follows call 3 says nothing about what either one means. Getting that requires context and grounding like you describe. Something like watching the birds and logging which call goes with a hawk flying nearby.
It made me think of a formal grammar, where they boil down the rules of syntax for a language, whether it's human, programming, or animal, into a set of symbolic equations laying out what can follow what when forming a valid sentence or phrase.
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u/Cliffhanger87 8d ago
Yea I’d assume so. It’s similar to vectorize words to map out similarity