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Cake day: May 29th, 2024

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  • So, keep in mind that single photon sensors have been around for awhile, in the form of avalanche photodiodes and photomultiplier tubes. And avalanche photodiodes are pretty commonly used in LiDAR systems already.

    The ones talked about in the article I linked collect about 50 points per square meter at a horizontal resolution of about 23 cm. Obviously that’s way worse than what’s presented in the phys.org article, but that’s also measuring from 3km away while covering an area of 700 square km per hour (because these systems are used for wide area terrain scanning from airplanes). With the way LiDAR works the system in the phys.org article could be scanning with a very narrow beam to get way more datapoints per square meter.

    Now, this doesn’t mean that the system is useless crap or whatever. It could be that the superconducting nanowire sensor they’re using lets them measure the arrival time much more precisely than normal LiDAR systems, which would give them much better depth resolution. Or it could be that the sensor has much less noise (false photon detections) than the commonly used avalanche diodes. I didn’t read the actual paper, and honestly I don’t know enough about LiDAR and photon detectors to really be able to compare those stats.

    But I do know enough to say that the range and single-photon capability of this system aren’t really the special parts of it, if it’s special at all.




  • I think there’s a sort of perfect storm that can happen. Suppose there are two types of YouTube users (I think there are other types too, but for the sake of this discussion we’ll just consider these two groups):

    • Type A watches a lot of niche content of which there’s not a lot on YouTube. The channels they’re subscribed to might only upload once a month to once a year or less.

    • Type B tends to watch one kind of content, of which there’s hundreds of hours of it from hundreds of different channels. And they tend to watch a lot of it.

    If a person from group A happens to click on a video that people from group B tend to watch that person’s homepage will then be flooded with more of that type of video, blocking out all of the stuff they’d normally be interested in.

    IMO YouTube’s algorithm has vacillated wildly over the years in terms of quality. At one point in time if you were a type A user it didn’t know what to do with you at all, and your homepage would consist exclusively of live streams with 3 viewers and family guy funny moments compilation #39.





  • drosophila@lemmy.blahaj.zonetoAutism Memes@lemmy.zipWhat are these "rules?"
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    9 days ago

    NTs when you say you wish social rules were either explained more explicitly or else your worth as a human being wasn’t tied to correctly following them:

    Sorry that’s literally impossible. Also its not that bad, you just need to try harder. I have no idea why so many of you kill yourselves.

    NTs after a few months of living in another country where the rules are different:

    Depression, panic attacks, sometimes even full on mental breakdowns.



  • At to end of the day it comes down to this:

    Is it cheaper to store steel stock in a warehouse or terrawatt-hours of electricity in a battery farm?

    Is it cheaper to perform maintainance on 2 or 3x the number of smelters or is it cheaper to maintain millions of battery or pumped hydro facilities?

    I’m sure production companies would love it if governments or electrical companies bore the costs of evening out fluctuations in production, just like I’m sure farmers would love it if money got teleported into their bank account for free and they never had to worry about growing seasons. But I’m not sure that’s the best situation for society as a whole.

    EDIT: I guess there’s a third factor which is transmission. We could build transmission cables between the northern and southern hemispheres. So, is it cheaper to build and maintain enormous HVDC (or even superconducting) cables than it is to do either of the two things above? And how do governments feel about being made so dependent on each other?

    We can do a combination of all three of course, picking and choosing the optimal strategy for each situation, but like I said above I tend to think that one of those strategies will be disproportionately favorable over the others.





  • Specifically they are completely incapable of unifying information into a self consistent model.

    To use an analogy you see a shadow and know its being cast by some object with a definite shape, even if you can’t be sure what that shape is. An LLM sees a shadow and its idea of what’s casting it is as fuzzy and mutable as the shadow itself.

    Funnily enough old school AI from the 70s, like logic engines, possessed a super-human ability for logical self consistancy. A human can hold contradictory beliefs without realizing it, a logic engine is incapable of self-contradiction once all of the facts in its database have been collated. (This is where the SciFi idea of robots like HAL-9000 and Data from Star Trek come from.) However this perfect reasoning ability left logic engines completely unable to deal with contradictory or ambiguous information, as well as logical paradoxes. They were also severely limited by the fact that practically everything they knew had to be explicitly programmed into them. So if you wanted one to be able to hold a conversion in plain English you would have to enter all kinds of information that we know implicitly, like the fact that water makes things wet or that most, but not all, people have two legs. A basically impossible task.

    With the rise of machine learning and large artificial neural networks we solved the problem of dealing with implicit, ambiguous, and paradoxical information but in the process completely removed the ability to logically reason.