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PaperclipPerfector  1yr ago (text post)   31957 thread views

Culture War Roundup for the week of May 22, 2023

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Every worry is unlike every other. In terms of qualitative danger, AI is more like other worries than commonly believed, because its unique property of reactiveness, which it shares with us, loads on both ends of the scale. Sometimes it's about optimizing for the exact same criterion, just with outputs going to different people. Cases in point:

and so on. Importantly, this isn't the case where the defense has to crush every single attack to be successful whereas attackers need to only triumph once, like doomers often say. Successful attacks will not be existentially threatening (unless the attacker does have a tremendous advantage in technical capability, but that's trivial and a good reason to commoditize the technology, if anything). Attacks still have cost, their perpetrators still work with limited resources, leave a footprint and are vulnerable to discovery, and while it is not a given that attackers learn effectively from each other, the next iteration of defense is better-informed; until attacks run into fundamental constraints.

All that information will be banned, just as facial recognition software has been banned for police

US cities are already reversing facial recognition bans, New Orleans did just a few months ago iirc. If it works, it will happen.

I know this is a hobby horse, but once AI is trained on gait recognition and body language of labelled examples of millions of hours of countless criminals’ movements recorded by CCTV, tiny little telltale patterns might well allow for effective pre-crime in the case of almost all premeditated criminal activity. People show their nerves, everyone has a tell, etc.

I have trouble believing there's enough information content present in CCTV streams to uniquely identify individuals confidently. I see how it maybe could work, but it's not something I'd focus on directly. Are human gaits really that different as to be identifiable from distant security cameras? Are they even consistent for a single person day-to-day?

The longer I think about it, I've also started thinking that AI likely scales sub-linearly (logarithmic?) with the size of the training dataset. "But the AI can viably consider a larger dataset than human experts" may be true, but may not generate hugely better results.

Interesting! Any good papers or summary articles you'd recommend?

Sure, there's a decades-long history of forensic gait analysis (long predating AI of course) in criminology. A nice overview is here. It's actively employed in China integrated with AI, although not widely in the West. In the West, gait analysis by experts has been a feature of trials for a long time - even before CCTV, it was used (and still is) on footprints left at crime scenes to identify suspects.

The challenge, of course, is that for now the applications of current forensic gait analysis are highly limited. The lack of comprehensive gait libraries for the wider population means that, unlike DNA (at least in recent memory) it's generally only used to support attempts to prove a suspect on trial was or was not someone in video footage. The real benefit is in scanning a library of millions or billions of hours of video taken from a network of surveillance cameras (which have ideally already been used to build up a 'library' of the entire population) to find possible 'matches' (the search space can be narrowed by geography and other quantitative or qualitative information recorded by police) in the general public, just like police DNA databases and Ancestry.com data are today.

In the West, research has been slow for a while. It's generally focused on identifying diseases like Parkinsons, the racing industry uses it for analysing horses etc, so a lot of Western research uses Lidar and multiple cameras; these achieve extremely high accuracy (often over 90%), but obviously aren't hugely helpful when the footage is actually blurry black-and-white CCTV at night.

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