Cars use stronger LIDAR lasers than the phones. The bigger range and faster response time requires it.
Natanael
joined 6 months ago
I've heard stories of clients giving gifts getting pissed when the wrong person claims them, so it's risky for not just legal reasons
The only viable competition to LIDAR is structured light (see Leap Motion, there's equivalent sensors for cars), which uses an IR source with patterned light and multiple high frame rate cameras to calculate depth from the reflections. In theory light field photography with special lenses is possible too, but far more computationally heavy for real-time use IIRC
There's some safety issues with LIDAR at close range (it's a laser! it can damage cameras, etc), which is basically the main reason to not use it. But Tesla are dumb enough to try to replace them with cameras alone, and not even using proper multi-camera techniques to calculate depth
Cars impose a ton of other societal costs too, busses still win. Expanding public transit usually saves money. (it helps if you can move some traffic to rail)
You need an exercise bike to produce something like a few hundred watts at most, if you can keep up an intense session. Continous stable power generation will be lower.
And everything which isn't a bike will have much lower peak power generation capacity, and will be less efficient too
Swede here.
Some American candy, mostly bad chocolate
Same thing with early studies on prime numbers
This assumes both have the same amount of heat capacity * mass. A hand with heat insulating gloves would also significantly reduce heat loss.
Better do it in a vacuum though, you'll lose energy to air resistance
The judge explicitly did not allow piracy here. Only legally acquired media can be used for training.
This case didn't cover the copyright status of outputs. The ruling so far is just about the process of training itself.
IMHO the generative ML companies should be required to build a process tracking the influence of distinct samples on the outputs, and inform users of potential licensing status
Division of liability / licensing responsibility should depend on who contributes what to the prompt / generation. The less it takes for the user to trigger the model to generate an output clearly derived from a protected work, the more liability lies on the model operator. If the user couldn't have known, they shouldn't be liable. If the user deliberately used jailbreaks, etc, the user is clearly liable.
But you get a weird edge case when users unknowingly copy prompts containing jailbreaks, though
Appears to me meant to handle much larger numbers of users efficiently