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0 pointsTeMPOraL6y ago0 comments

Yes, I'm talking about this paper exactly. All I can see is photogrammetry and regular image processing stuff. There's no DNNs or other hot stuff. I guess the part where they're estimating parameters for the equations from photos counts as ML, but people have stopped calling this stuff "AI" quite a while ago.

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darksaints6y ago· 2 in thread

> I guess the part where they're estimating parameters for the equations from photos counts as ML, but people have stopped calling this stuff "AI" quite a while ago.

That's the AI effect.

https://en.m.wikipedia.org/wiki/AI_effect

wgjordan6y ago

> That's the AI effect.

Indeed- as that coined term suggests, "AI" has such a philosophically-tied definition with moving goalposts, that in practice it gets slapped far too liberally on anything 'magical' having to do with computation. (As long as it's still trendy to do so, at least.)

'Machine Learning' is at least a little more specific of a term (if still rather general).

6gvONxR4sf7o6y ago

People point at this all the time, but there's also another AI effect. People use traditional techniques to solve modern problems and label it "AI." Calling it a traditional technique despite the modern application doesn't mean it's moving the goalposts.

Acinyx6y ago

I think it is more that this correction allows more generic algorithms like object classification to work. Of course you could do object classification without color, but that would be throwing away information. It is just that the back scattering currently is overwhelming the colors in such a variable way that it is hard to use color inclusive object classification without doing stuff like requiring the exact same distance between camera and object for each picture. And likely that doesn't even work yet.

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