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I think you're describing technology that has existed for 15+ years and is already pretty accurate. It's not even necessarily "AI"/ML. For example, I think Open
by scottlamb 11mo ago
I think you're describing technology that has existed for 15+ years and is already pretty accurate. It's not even necessarily "AI"/ML. For example, I think OpenALPR (automated license plate recognition) is all "classical" computer vision. The most accurate facial/gait/etc. recognition is most likely ML-based with a state-of-the-art model, admittedly, and perhaps the threshold of accuracy for large-scale usefulness was only crossed recently.
The guard rails IMHO are not technological but who owns the cameras/video storage backend, when/if a warrant is needed, and the criteria for granting one.
- CuriouslyC 11mo agoThe difference is that AI makes annotating/combing through all that data much more feasible.
- deleted 11mo ago[deleted]
- scottlamb 11mo agoCan you explain what you mean? The queries in jrochkind1 are not something I'd expect AI (LLMs, I assume) to be necessary for. Too simple and factual. (Maybe just the last one would be where interpretation kicks in—knowing what to emphasize in a summary, describing actions.) Did you have something else in mind?
- CuriouslyC 11mo agoIf you have a bunch of surveillance footage, the bottleneck is your analysts' ability to comb through it. You can sit LLMs on top of faster object detection/identification algorithms to create narratives across your surveillance net that are easy to query, can be overlaid on timelines, etc.
- scottlamb 11mo agoThat's fair, but I think it's a significant step beyond the queries jrochkind1 was describing. (I also don't trust LLMs to do it accurately but maybe that part will change.)