Há alguns dias, me deparei com um interessante vídeo sobre os chamados “fósseis vivos”. O vídeo focou mais nos problemas de usá-los como argumentos contra a teoria da evolução, e aproveitei a oportunidade para falar mais sobre essas linhagens longevas.
‘Fóssil vivo‘ é um termo usado para descrever linhagens que acredita-se terem se originado há muito tempo e que mantêm características que se assemelham a seus parentes fósseis. Alguns exemplos bem conhecidos dessas linhagens são os Tuatara da Nova Zelândia (Sphenodon punctatus) e as árvores Gingkos (Gingko biloba).
A couple of days ago I came across a nice video (in Portuguese only, sorry) about so-called “living fossils”. The video focused on the problems of using them as arguments against evolution. But I’d like to take the opportunity to talk more about these long-lived lineages.
‘Living fossil’ is a term used to describe lineages that are thought to have been around for a very long time and retain characteristics that resemble of their fossil relatives. A couple of well-known examples of these lineages are the Tuatara of New Zealand (Sphenodon punctatus) and the Gingko tree (Gingko biloba).
Artificial intelligence (or AI) is an enormously hot topic, regularly hitting the news with the latest milestone where computers matching or exceeding the capacity of humans at a particular task. For ecologists, one of the most exciting and promising uses of artificial intelligence is the automatic identification of species. If this could be reliably cracked, the streams of real-time species distribution data that could be unlocked worldwide would be phenomenal.
Despite the hype and rapid improvements, we’re not quite there yet. Although AI naturalists have had some successes, they can also often make basic mistakes. But we shouldn’t be too harsh on the computers, since identifying the correct species just from a picture can be really hard. Ask an experienced naturalist and they’ll often need to know where and when the photo was taken. This information can be crucial for ruling out alternatives. There’s a reason why field guides include range maps!
Currently, most AI identification tools only use an image. So, we set out to see if a computer can be taught to think more like a human, and make use of this extra information. Continue reading →