Yes, indeed. One almost feels sorry for the poor old AI/CV faced with untangling these. Not surprisingly, seaweeds, fungi, and lichens seem over-represented. As I have experienced, having wrongly identified something as a Red Algae and having it corrected to Brown Algae (or was it vice versa?) resulted in it being ‘elevated’ to ‘Life’.
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Thanks Jane. It would take a very clever algorithm and a lot more than pixels in a photo to replicate the power of the human brain!
Are there any measures for how the accuracy of the AI/CV ID’ing is improving over time as more images are added to the data base?
It has been documented that the accuracy is high (80-ish %), but there are relatively few taxa that get uploaded a lot (e.g., mallards, honeybees) and thousands of obscure species that will probably never have enough observations to be included in the model.
A personal tale on CV accuracy. Last year, I reviewed Penstemon strictus pictures on iNat. At the time, it was overwhelmingly the first suggestion out of the purple-flowering beardtongues. From the stats in the “Similar Species” tab, 1/3 of observations that used to say P strictus were wrong. Most of those species-level IDs were inspired by CV (users contribute too, but that’s been discussed to death).
also anecdotal evidence. When Cape Town did its first Bioblitz AI / CV couldn’t even recognise our king protea!
https://www.inaturalist.org/taxa/132848-Protea-cynaroides
Year by year I see steady and appreciated improvement. And motivation to get more obs and IDs for the … Pending to be included next time …
And also motivation to - flag for curation - please add this (African) species. The human curators’ work also gratefully appreciated each time.
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If you are referring to this post, the range mentioned is 60-80% but those numbers are very misleading in my opinion for the reasons detailed here. …so, I would take them with a pinch of salt.
I don’t believe there are any meaningful broader stats about AI/CV accuracy shared on the forum at present. But the number of species included in the model is growing fast and that is a solid metric for improvement! It increased from 25000-38000 in the update last summer - more details are visible on this blog post.
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There is a check box that lets you move a picture to the first position in the iPhone, and then that picture is used for suggestions. The check box is only available to the person who made the observation.
Also remember that local species may not be in CV and thus may not appear in the suggestions. Other more broadly distributed species that are visually similar and overlap the range may appear instead.
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The perceptron is cool for explaining because you can literally work out a small one by hand (I know, because I had to do this on a computational neuroscience exam in college) but rudimentary, which is why CV barely worked for most problems even 10 years ago, even though it was invented over 60 years ago. The CV that makes inat work is much more sophisticated and probably more similar internally to how the human brain works.
Its also debatable if the training is that much different from how you would teach a human in that you cannot see how your brain actually does the training of your neurons.
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