The mein reason I'm interested in orcas is because they have 43 billion cortical neurons, whereas the 2 land animals with the most cortical neurons (where we have have optical-fractionator measurements) are humans and chimpanzees with 21 billion and 7.4 billion respectively. See: https://en.wikipedia.org/wiki/List_of_animals_by_number_of_neurons#Forebrain_(cerebrum_or_pallium)_only

Pilot whales is the other species I'd consider for experiments - they have 37.2 billion cortical neurons.

For sperm whales we don't have data on neuron densities (though they do have the biggest brains). I'd guess they are not quite as smart though because they can dive long and they AFAIK don't use very collaborative hunting techniques.

Cool, thanks!

Cool, thanks, that was useful.

(I'm creating a language for communicating with orcas, so the phonemes will be relatively unpractical for humans. Otherwise the main criteria are simple parsing structure and easy learnability. (It doesn't need to be super perfect - the perhaps bigger challenge is to figure out how to teach abstract concepts without being able to bootstrap from an existing language.) Maybe I'll eventually create a great rationalist language for thinking effectively, but not right now.)

Is there some resource where I can quickly learn the basics of the Esperanto composition system? Somewhere I can see the main base dimensions/concepts?

I'd also be interested in anything you think was implemented particularly well in a (con)language.

(Also happy to learn from you rambling. Feel free to book a call: https://calendly.com/simon-skade/30min )

Thanks!

But most likely, this will all be irrelevant for orcas. Their languages may be regular or irregular, with fixed or random word order, or maybe with some categories that do not exist in human languages.

Yeah I was not asking because of decoding orca language but because I want inspiration for how to create the grammar for the language I'll construct. Esparanto/Ido also because I'm interested about how well word-compositonality is structured there and whether it is a decent attempt at outlining the basic concepts where other concepts are composites of.

Currently we basically don't have any datasets where it's labelled what orca says what. When I listen to recordings, I cannot distinguish voices, though idk it's possible that people who listened a lot more can. I think just unsupervised voice clustering would probably not work very accurately. I'd guess it's probably possible to get data on who said what by using an array of hydrophones to infer the location of the sound, but we need very accurate position inference because different orcas are often just 1-10m distance from each other, and for this we might need to get/infer decent estimates of how water temperature varies by depth, and generally there have not yet been attempts to get high precision through this method. (It's definitely harder in water than in air.)

Yeah basically I initially also had rough thoughts into this direction, but I think the create-and-teach language way is probably a lot faster.

I think the earth species project is trying to use AI to decode animal communication, though they don't focus on orcas in particular, but many species including e.g. beluga whales. Didn't look into it a lot but seems possible I could do sth like this in a smarter and more promising way, but probably still would take long.

Thanks for your thoughts!

I don't know what you'd consider enough recordings, and I don't know how much decent data we have.

I think the biggest datasets for orca vocalizations are the orchive and the orcasound archive. I think they each are multiple terabytes big (from audio recordings) but I think most of it (80-99.9% (?)) is probably crap where there might just be a brief very faint mammal vocalization in the distance.
We also don't have a way to see which orca said what.

Also orcas from different regions have different languages, and orcas from different pods different dialects.

I currently think the decoding path would be slower, and yeah the decoding part would involve AI but I feel like people just try to use AI somehow without a clear plan, but perhaps not you.
What approach did you imagine?

In case you're interested in few high-quality data (but still without annotations): https://orcasound.net/data/product/biophony/SRKW/bouts/

Thanks.

I think LTFF would take way too long to get back to me though. (Also they might be too busy to engage deeply enough to get past the "seems crazy" barrier and see it's at least worth trying.)

Also btw I mostly included this in case someone with significant amounts of money reads this, not because I want to scrap it together from small donations. I expect higher chances of getting funding come from me reaching out to 2-3 people I know (after I know more about how much money I need), but this is also decently likely to fail. If this fails I'll maybe try Manifund, but would guess I don't have good chances there either, but idk.

Actually out of curiosity, why 4x? (And what exactly do you mean by "2x larger"?) (And is this for a naive algorithm which can be improved upon or a tight constraint?)

Thanks for pointing that out! I will tell my friends to make sure they actually get good data for the metabolic cost and not just use cortical neuron count as proxy if they cannot find something good.

(Or is there also another point you wanted to make?) And yeah it's actually also an argument for why orcas might be less intelligent (if they sorta use their neurons less often). Thanks. 

My guess is that there probably aren't a lot of simple mutations which just increase intelligence without increasing cortical neuron count. (Though probably simple mutations can shift the balance between different sub-dimensions of intelligence as constrained through cortical neuron count.) (Also of course any particular species has a lot of deleterious mutations going around and getting rid of those may often just increase intelligence, but I'm talking about intelligence-increasing changes to the base genome.)

But there could be complex adaptations that are very important for abstract reasoning. Metacognition and language are the main ones that come to mind.

So even if the experiment my friends to will show that the number of cortical neurons is a strong indicator, it could still be that humans were just one of the rare cases which evolved a relevant complex adaptation. But it would be significant evidence for orcas being smarter.