riotnrrd

I just had my hole inspected and all the preparation was with over-the-counter supplies. My prep drink was gatorade with some flavorless powder mixed in. It made no change in the taste or texture of this drink. Having the squirts for a day was no fun, but other than that it was a breeze.

It's a tech demo.

When you post on a public forum defending child pornography, it's maybe a good time to take a step back and evaluate your life.

What are saying? Spell it out for us.

We'll worry about that when the Presidency and both houses of Congress are controlled by the Communist Party

It takes an act of Congress to change the name. Trump just made one of his dictatorial proclamations.

NVIDIA has been selling automotive-specific silicon for a decade.

We experimented with a rig with more cameras on it (four, in a square) but the baseline of the cameras on the drones we were using could be measured in centimeters, so the vertical stereo pairs didn't provide much better results. Further, more cameras means more power, more weight, and much more expensive on-board processing (which also will require more power).

All cables? Everywhere in the entire country? Accurate to the centimeter level and updated on the hour?

Edit: This was flippant, but the real issues are: any map you get will be incomplete and obsolete almost immediately and cables move and sway in the breeze.

Statistics are not theft. Judges have written over and over again that training a neural network (which is just fitting a high-dimensional function to a dataset) is transformative and therefore fair use. Putting it another way, me summarizing a MLB baseball game by saying the Cubs lost 7-0 does not infringe on MLB's ownership of the copyright of the filmed game.

People claiming that backpropagation "steals" your material don't understand math or copyright.

You can hate generative tools all you want -- opinions are free -- but you're fundamentally wrong about the legality or morality at play.

I used to work in perception for autonomous aerial vehicles and horizontal wires were the hardest common object to avoid. Traditional stereo won't help you localize them -- wires are thin so even mere detection can be hard, and one portion of a wire looks much like another so feature matching fails resulting in bad or no depth estimates -- and LIDAR sacrifices resolution for weight and power consumption (which both have to be optimizied for drones). It's been years since I've worked in this field, and Amazon has many smart people thinking about it but I'm not surprised it's still a difficult problem.