Probably because your app has an actual database of plants to compare with instead of feeding it into an AI.
Edit: The term AI is getting to be a little useless these days. What I meant to say, was it’s not using image recognition as implemented by a multi-modal LLM. It’s using the more traditional machine learning algorithms that came before “Attention is all you need”
No. AI and, what you’re more likely to be referring to, machine learning has had applications for decades. Basic work was used back into the '60s, mostly for quick things, and 1D data analysis was useful long before images (voice and stuff like biometrics). But there are many more types of AI. Bayesian networks (still in the learned category) were huge breakthroughs and still see a lot of use today. Decision trees, Markov chains, and first order logic are the most common video games AI and usually rely on expert tuning rather than learned results.
AI is a huge field that’s been around longer than you expected, and permeates a lot of tech. Image stuff is just the hot application since it’s deep learning based buff that started around 2009 with a bunch of papers that helped get actual beneficial learning in deeper models (I always thought it started roughly with Deep Boltzmann Machines, but there’s a lot of work in that era that chipped away at the problem). The real revolution was general purpose GPU programming getting to a state where these breakthroughs weren’t just theoretical.
Before that, we already used a lot of computer vision, and other techniques, learned and unlearned, for a lot of applications. Most of them would probably bore you, but there are a lot of safety critical anomaly detectors.
It really depends on what you call AI, but just to put things into context: XKCD 1425 was released in 2014. Compare that to the timeline of AI on Wikipedia.
That’s blatantly untrue. My plant ID app gives multiple suggestions with certainty percentages.
What’s your plant ID app?
inaturalist does this, and also lets other people suggest an ID so you can get a consensus.
PlantNet
My app does this too!
Feeling like half these commenters hating on this feature use one bad program and think the whole concept is bad.
Probably because your app has an actual database of plants to compare with instead of feeding it into an AI.
Edit: The term AI is getting to be a little useless these days. What I meant to say, was it’s not using image recognition as implemented by a multi-modal LLM. It’s using the more traditional machine learning algorithms that came before “Attention is all you need”
Why do you think so, and how do you think the plants are compared without AI?
Image classification/object detection AI (usually) gives you a confidence value for every result. It’s a natural consequence of their architecture.
Weren’t image recognition algorithms like the first types of AI that got good enough to be useful?
No. AI and, what you’re more likely to be referring to, machine learning has had applications for decades. Basic work was used back into the '60s, mostly for quick things, and 1D data analysis was useful long before images (voice and stuff like biometrics). But there are many more types of AI. Bayesian networks (still in the learned category) were huge breakthroughs and still see a lot of use today. Decision trees, Markov chains, and first order logic are the most common video games AI and usually rely on expert tuning rather than learned results.
AI is a huge field that’s been around longer than you expected, and permeates a lot of tech. Image stuff is just the hot application since it’s deep learning based buff that started around 2009 with a bunch of papers that helped get actual beneficial learning in deeper models (I always thought it started roughly with Deep Boltzmann Machines, but there’s a lot of work in that era that chipped away at the problem). The real revolution was general purpose GPU programming getting to a state where these breakthroughs weren’t just theoretical.
Before that, we already used a lot of computer vision, and other techniques, learned and unlearned, for a lot of applications. Most of them would probably bore you, but there are a lot of safety critical anomaly detectors.
It really depends on what you call AI, but just to put things into context: XKCD 1425 was released in 2014. Compare that to the timeline of AI on Wikipedia.