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Looks like they have samples on this page: https://cartesia.ai/sonic


Interesting to compare with a time-reversal mirror for water waves that uses a high-acceleration "jolt" to disrupt the wave propagation at all points in space simultaneously: https://pubs.aip.org/physicstoday/Online/11398/Reversing-tim...


I suspect this is more of a collaborative scientific effort rather than a commercial enterprise. However, they do mention in a tweet that they are supported with funding from the Flatiron Institute (Simons Foundation):

https://twitter.com/PolymathicAI/status/1711389556623450177

The press release on the Simons Foundation website might be a bit more informative: https://www.simonsfoundation.org/2023/10/09/scientists-begin...


Yes, it was announced on the (relatively brief) live stream.


Thanks, is there a better link then that would let someone not up to speed understand the announcement and significance?



For context, Rich Sutton is one of the founding fathers of reinforcement learning. He is also known for his 2019 essay, "The Bitter Lesson": http://incompleteideas.net/IncIdeas/BitterLesson.html



Thanks. Interpolating between map projections that have the same clip region is relatively straightforward. When projecting geometries from the surface of a sphere to a 2D plane, you're always going to have a discontinuity somewhere on the sphere, i.e. the clip region. The projections supported so far all have the same discontinuity along the antimeridian (the meridian at longitude ±180°).

Extending the interpolation to work for projections with different clip regions should be feasible but there are several ways to interpolate between arbitrary shapes in 2D, so I'd have to give it some thought.

Another way to transition between "polyhedral" projections like the Dymaxion map and the Waterman butterfly is to fold and unfold the maps in 3D to and from a closed polyhedral globe.


Really love your previous work on ocean-centered maps like the Spilhaus. Keep up the good work.


This is probably the most frequently asked question! The data is from here: http://ourairports.com/data/

I had to limit the number of airports displayed for performance reasons. I filtered the airport data so that only those airports with scheduled services and which are denoted "large" or "medium" are included (according to OurAirports), bringing the number down to 2,980.


Suppose that'll explain why there are at least three airports included in Ireland that have no scheduled services at all: the data's from nearly a decade ago


I am amused how airports like Lands End (which was grass runways only before 2014), and Chester(!!) made the cut. Plymouth closed in 2011

However that's all nitpicking, this is a pretty cool map, I'd never encountered Voronoi diagrams before.


Interesting. The data is from 2014, which is when I created the visualisation. Perhaps I should auto-update it every so often!


Most recent item on "recent changes" was five years old in 2014. Galway Airport's certainly been shut for a lot longer than that.


St Helena airport is open and technically operational. It's certainly one of the most remote airports on the planet (this happened post 2015)


It would be nice if there was a slider for N. I like to think my computer is recent, but evidently it is not!


It's probably to avoid dead-code elimination, which would defeat the purpose of the benchmark. The rust benchmarks use test::black_box [0], which does the same thing, so there's no unfair advantage there.

[0]: http://doc.rust-lang.org/test/fn.black_box.html


But it appears that the Rust code is avoiding optimization over f() whereas for C code `asm("");` is inside iteration loop.


I've just reduced the resampling slightly, using precision(.5) instead of precision(.1), not sure if that helps much.


The files used are from Mike Bostock’s World Atlas TopoJSON project: https://github.com/mbostock/world-atlas

They are in turn derived from Natural Earth data: http://www.naturalearthdata.com/downloads/110m-cultural-vect...


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