Idk if you've seen this already but Taalas does this interesting thing where they embed the model directly onto the chip, this leads to super-fast speeds (https://chatjimmy.ai) but the model they're using is an old small Llama model so the quality is pretty bad. But they say that it can scale, so if that's really true that'd be pretty insane and unlock the inference you're talking about.
Robotics/control systems is exactly what came to mind when I saw this release! What struck me is the possibility of look ahead search in real time, a bit like alphazero's mcts.
It's a fascinating proposition and no doubt they'll get bigger models in there, and likely be able to cluster multiple models for mega MOE. One thing that would really be great is if they could take the power requirements down -- the chip requires 2.5KW, which is modest in terms of what the big boys use but would be an issue on a battery powered robot.
Pangram is probably the best known example of a detector with low false positives, they have a research paper here: https://arxiv.org/pdf/2402.14873. They do have an API but not sure if you need to request access for it.
For humans I think it just comes down to interacting with LLMs enough to realize their quirks, but that's not really fool-proof.
Pangram has time after time been shown as the only detector that mostly works. And that paper is pretty old now! There are recent papers from academics independently bench-marking and studying detectors e.g. https://arxiv.org/abs/2501.15654
Some good examples there. The octopus one is at an angle - can't really call that one pass (unless the goal is "VISIBLE" tentacles).
Other than the five-leaf clover, most of the images (dog, spider, person's hands) all required a human in the loop to invoke the "Image-to-Image" capabilities of NB Pro after it got them wrong. That's a bit different since you're actively correcting them.
