I assume they're both built around deep neural networks, and it wouldn't surprise me if some parts of the pothole-detectors use the same Transformer architecture (the T in "GPT") as LLMs - it was used as part of the pipeline that decoded the Herculaneum scrolls recently. You can't draw a hard-and-fast line between LLMs and other ML techniques.
I assume they're both built around deep neural networks, and it wouldn't surprise me if some parts of the pothole-detectors use the same Transformer architecture (the T in "GPT") as LLMs - it was used as part of the pipeline that decoded the Herculaneum scrolls recently. You can't draw a hard-and-fast line between LLMs and other ML techniques.
The difference is more around the size and complexity of model required, it's the first L in LLM that tends to make current AI problematic.
Image classification models have been around for well over a decade at this point (Google famously got into hot water over their model back int 2015).
It appears the model is simple enough to be run on-device. A small fleet of (hopefully electric) vehicles with locally processed "AI" images probably isn't going to be classed as planet burning
I assume they're both built around deep neural networks, and it wouldn't surprise me if some parts of the pothole-detectors use the same Transformer architecture (the T in "GPT") as LLMs - it was used as part of the pipeline that decoded the Herculaneum scrolls recently. You can't draw a hard-and-fast line between LLMs and other ML techniques.
The difference is more around the size and complexity of model required, it's the first L in LLM that tends to make current AI problematic.
Image classification models have been around for well over a decade at this point (Google famously got into hot water over their model back int 2015).
It appears the model is simple enough to be run on-device. A small fleet of (hopefully electric) vehicles with locally processed "AI" images probably isn't going to be classed as planet burning