The compression of the bills under their own weight might account for the excessive margin - a lone $100 bundle, even compressed by hand before measuring, probably takes up more vertical space than the ones in the cube.
No, this performs the same task as CoTracker or TAPIR, but intended for running at a higher resolution. Point tracking is useful both for keeping track of the position of a target and for "inside-out" positioning of the camera.
YOLO is mostly concerned with detecting objects of certain classes in a single image, and SAM is concerned with essentially classifying pixels as belonging to an object or not.
As clejack said, "Org silos, security, and permissions" - this is usually the largest single time sink on any project that needs production data.
Related to this is obtaining data in bulk - teams (understandably) are usually not willing to hand out direct read access to their databases and would prefer you use their API, and they've usually built APIs intended for accessing single records at a relatively slow rate. It often takes some convincing (DoSing their API) to get a more appropriate bulk solution.
I'd imagine 4th of July is unlikely to shift to majority drones (at least in the near term) due to the economics of it - there are a lot of fireworks shows all concentrated on one evening, and the corresponding number of drone fleets would be sitting mostly idle for the rest of the year. (And I agree that Americans like explosives and would definitely object if you took them away.)
If the price of the drones comes down further, or fireworks become more regulated, then maybe.
> and the corresponding number of drone fleets would be sitting mostly idle for the rest of the year
Would they, or would cities find new occasions to use them because they are already bought and paid for? Maybe Memorial and Labor day drone shows, or "Saturday In The Park" drone shows, or Christmas and New Years drone shows. If they already have them in house, why not use them more? I know I would love that.
There's some discussion of time in the paper; they compare to Blender Cycles (path tracing) and at least for their <= 4k triangle scenes the neural approach is much faster. I suspect it doesn't scale as well though (they mention their attention runtime is quadratic with number of tris).
I wonder if it would be practical to use the neural approach (with simplified geometry) only for indirect lighting - use a conventional rasterizer and then glue the GI on top.
Easily the best store I've ever been to. Unfortunately the nearest one is ~an hour drive, so I only visit once every couple of years, but it's always a good afternoon.
The tokenizer might lump the last digit together with some preceding digits though. I know o200k_base (OpenAI -o models) tends to give groups of three (900001 for example is 900-001).
Anyway, I wouldn't be surprised if a non-finetuned model made some mistakes.
All the big providers offer no-training/retention guarantees (either by default, or as a toggle, or upon request). For many high security environments though I'd expect everything to be hosted on-prem or at minimum on company-controlled instances, which does limit your model options somewhat.
My employer has such contracts for some use cases, but actually forbids use of code completion/generation due to IP concerns.
An excellent video from Scott Manley about last year's fix of the Voyager 1 computer. Not related to the recent thruster fix.
Original title "The First Interstellar Software Update - The Insane Hack That Saved Voyager 1"; this combined with the [video] tag was too long, so I removed the "number + adjective" in accordance with the HN guidelines.
Because the MCP neuron is taken as common knowledge and people do not feel the need to explicitly reference it (and haven't for some time), and the pace of publishing has increased in recent years.
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