101–110 of 339 posts
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FWIW those type of traffic cameras are in common use. https://www.milesight.com/company/blog/types-of-traffic-came...
If I read the web page, they don't actually use that as a solution to shortening a red - IMHO that has a very high safety bar compared to the more common uses. But I'd be happy to hear this is something that Just Works in the Real World with a reasonable false positive and false negative rate.
doesn't seem like it makes sense to train AI around human user interfaces which aren't really efficient. It is like building a mechanical horse.
This is just like the comments suggesting we need sensors and signs specifically for self-driving cars for them to work. It'll never happen, so companies need to deal with the reality we have.
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It does, because it's hard to infer where each element will end up in the render. So a checkbox may be set up in a shitty way such that the corresponding text label is not properly placed in the DOM, so it's hard to tell what the checkbox controls just based on the DOM tree. You have to take into account the styling and placement pixel stuff, ie render it properly and look at it. That's just one obvious example, but…
Spatial continuity has nothing to do w/ how neural networks interpret an array of numbers. In fact, there is nothing about the topology of the input that is any way relevant to what calculations are done by the network. You are imposing an anthropomorphic structure that does not exist anywhere in the algorithm & how it processes information. Here is an example to demonstrate my point: https://x.com/s_scardapane/statu…
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None of that matters to neural networks.
It does, because it's hard to infer where each element will end up in the render. So a checkbox may be set up in a shitty way such that the corresponding text label is not properly placed in the DOM, so it's hard to tell what the checkbox controls just based on the DOM tree. You have to take into account the styling and placement pixel stuff, ie render it properly and look at it. That's just one obvious example, but…
Earlier quoted context omitted.
It does, because it's hard to infer where each element will end up in the render. So a checkbox may be set up in a shitty way such that the corresponding text label is not properly placed in the DOM, so it's hard to tell what the checkbox controls just based on the DOM tree. You have to take into account the styling and placement pixel stuff, ie render it properly and look at it. That's just one obvious example, but…
Spatial continuity has nothing to do w/ how neural networks interpret an array of numbers. In fact, there is nothing about the topology of the input that is any way relevant to what calculations are done by the network. You are imposing an anthropomorphic structure that does not exist anywhere in the algorithm & how it processes information. Here is an example to demonstrate my point: https://x.com/s_scardapane/statu…
doesn't seem like it makes sense to train AI around human user interfaces which aren't really efficient. It is like building a mechanical horse.
Earlier quoted context omitted.
Spatial continuity has nothing to do w/ how neural networks interpret an array of numbers. In fact, there is nothing about the topology of the input that is any way relevant to what calculations are done by the network. You are imposing an anthropomorphic structure that does not exist anywhere in the algorithm & how it processes information. Here is an example to demonstrate my point: https://x.com/s_scardapane/statu…
Why are you talking about image processing ? The guy you’re talking to isn’t
Earlier quoted context omitted.
Spatial continuity has nothing to do w/ how neural networks interpret an array of numbers. In fact, there is nothing about the topology of the input that is any way relevant to what calculations are done by the network. You are imposing an anthropomorphic structure that does not exist anywhere in the algorithm & how it processes information. Here is an example to demonstrate my point: https://x.com/s_scardapane/statu…
It would have to implicitly render the HTML+CSS to know which two elements visually end up next to each other, if the markup is spaghetti and badly done.