#AnimalLanguage

[knowledge-first]

The Power of the Blink! 😺🐈

Next time your cat gives you a slow, deliberate blink, don't mistake it for sleepiness. This is actually a cat's way of showing trust and affection. It's their version of a slow smile! @knowledge-first

[lexiai]

AI Decoding Animal Languages: A New Era! 🧠🐾 😺🐶

[goodbearblind]

“Siamo qui Stiamo piangendo E non ci ascoltate Perché avete dimenticato Il nostro linguaggio” (Unknown artist) . . #animals #animallanguage #animalliberation #animalliberationnow #totalliberation #comunication https://www.instagram.com/p/B7lnwDzi1ft/?igshid=1cqngejtnrhor

[sylvanbowyer]

Wohlleben, on us humans getting over ourselves. . . . . . . #goodreads #books #tellmeastory #learnallthethings #thehumancondition #soulfood #bookshop #poetics #naturereading #scienceandstuff #wohlleben #theinnerlivesofanimals #animalemotions #animallanguage #communication https://www.instagram.com/p/BuzjpaYhKA2/?utm_source=ig_tumblr_share&igshid=1un0953qqkiu4

[sciencefor]

Did You Know?

Dogs develop regional accents that reflect the tone and cadence of their humans' speech patterns.

#dogbehaviour #barking #animallanguage

[stag28]

"Researchers studying Egyptian fruit bats say they have found a way to work out who is arguing with whom, what they are squabbling about and can even predict the outcome of a disagreement – all from the bats’ calls. [..] “One of the big questions in animal communication is how much information is conveyed.” [..] The approach, they reveal, relied on harnessing machine learning algorithms originally used for human voice recognition. A form of artificial intelligence, machine learning algorithms are “trained” by being fed data that has already been sorted into categories, and then used to apply the patterns and relationships the system has spotted to sort new data. [..] By studying the video footage, the researchers were able to unpick which bats were arguing each other, the outcome of each row, and sort the squabbles into four different bones of contention: sleep, food, perching position and unwanted mating attempts. The team then trained the machine learning algorithm with around 15,000 bat calls from seven adult females, each categorised using information gleaned from the video footage, before testing the system’s accuracy. The results revealed that, based only on the frequencies within the bats’ calls, the algorithm correctly identified the bat making the call around 71% of the time, and what the animals were squabbling about around 61% of the time. The system was also able to identify, although with less accuracy, who the call was aimed at and predict the fallout of the disagreement [..] “We have shown that a big bulk of bat vocalisations that previously were thought to all mean the same thing, something like ‘get out of here!’ actually contain a lot of information,”"

[visualsbymk]

Aly Antorcha 'favourite' my tweet on twitter. Omg fangirling!!! Lol