| > We believe JavaScript, instead of Python, could act as an ideal language for expressing mathematical ideas if the proper infrastructure existed Outside of "everyone uses js", why do we believe this? What makes JS "ideal". I would think for machine learning one would want: - generally default to non-mutating, functional representation of ideas, with convenient escape hatches - treats the GPU as a concurrent (or better yet, distributed) resource - can seamlessly run ML code as either immediate or an optimized graph. - could swap out compute resources as asynchronous rpc, like, flex out to something more powerful if available and needed Certainly most of these are possible in JS, but I would hardly call it "ideal" for these bullet points, not to mention other general concerns like a highly questionable dependency management ecosystem. |
When you use a jupyter notebook the kernel wastes a lot of resources on visualization and the support for interactive visualization isn't ideal (copy all your data to json, pass that to a JS library which has to do the work).
Take a look at ObservableHQ for an idea of what an interface might look like, then imagine numpy semantics and webGPU on top.
As a bonus: run on server or in browser locally would reduce cloud bills because you don't need a whole server to do an exploratory analysis.