It’s not just random jitter, it also likely adds context, including the device you’re using, other recent queries, and your relative location (like what state you’re in).
I don’t work for Google, but I am somewhat close to a major AI product, and it’s pretty much the industry standard to give some contextual info to the model in addition to your query. It’s also generally not “one model”, but a set of models run in sequence— with the LLM (think chatGPT) only employed at the end to generate a paragraph from a conclusion and evidence found by a previous model.
Potentially better idea, add a gyroscope to the key fob, and stop broadcasting after the fob is perfectly still for some threshold. That way when you set it down inside it can’t be relayed, but if it’s in your pocket, it won’t remain perfectly still, and will start transmitting. Could also add an IR blaster to detect if you set it down in the car. Battery life would start to become a bigger issue, but I think solutions to these problems could be engineered.
Relaying a key signal 20 ft when you know the key is there isn’t too tricky, like when you’re home. But I would propose that trying to relay a signal across hundreds of feet, like a busy mall or store, when you’re not even sure the owner is there is quite another thing. You can also require that the IR blaster is in the car before starting. There’s also a technology Google has been using for a while now where the device (car) would emit a constant ultrasonic signal for the other device (key) to pick up on to determine if they are close to each other. Something that could be done through clothing, but not easily relayed.
While I agree in principle, one thing I’d like to clarify is that TRAINING is super energy intensive, once the network is trained, it’s more or less static. Actually using the network isn’t dramatically more energy than any other indexed database lookup.
I firmly believe it will slow down significantly. My prediction for the future is that there will be a much bigger focus on a few “base” models that will be tweaked slightly for different roles, rather than “from the ground up” retraining like we see now. The industry is already starting to move in that direction.
Indexing and lookups on datasets as big as companies like Google and Amazon are running also take trillions of operations to complete, especially when you take into account the constant reindexing that needs to be done. In some cases, encoding data into a neural network is actually cheaper than storing the data itself. You can see this in practice with gaussian splatting point cloud capture, where they are training networks to guide points in the cloud at runtime, rather than storing the position of trillions of points over time.
Actually, Windows does allow you to use an alternate “compositor”— a feature which is used quite frequently in the industrial/embedded space. Windows calls them “custom shells”. The default is Explorer, but it can be set to any executable.
I could 100% see them offering user replaceable memory, but with a slower max speed than factory installed. Gotta have something to point to when the regulators come a-knockin.
Autopilot maintains altitude and bearing between waypoints in the sky, and in some (ideal) situations can automatically land the aircraft. In terms of piloting an aircraft, it can handle the middle of the journey entirely autonomously, and even sometimes the end (landing).
Autopilot (the Telsa feature) is not rated to drive the car autonomously, requires constant human supervision, and can automatically disengage at any time. Despite being sold as an “autonomous driver”, it cannot function as one, like autopilot on a plane can. It is clearly using the autopilot feature of an aircraft to imply that the car can pilot itself through at least the middle of the journey without direct supervision (which it can’t). That is misrepresentation.
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Cross posted from: beehaw.org/post/13516787...