Robert Moss: Julia and the Next Generation Airborne Collision Avoidance System

A detailed discussion is on Julia discourse: [link] Bugs are everywhere - including Python and R libraries. You would not design a safety critical system in Python using libraries and codepaths that are not heavily tested. Even then, you still have to carefully design testsuites that give you confidence that what you are computing is what is expected. See these talks on how a collision avoidance system was designed with Julia. [link] ▶ this video

in “Why I still recommend Julia” · View on HN

This is true, but even engineers see the advantages of Julia. My engineering school has went from almost pure Matlab usage to many key engineering courses switching to Julia due to its simplicity and friendliness. It's also SOTA for many engineering applications, particularly for acausal modelling and scientific machine learning (see [link] ), which has led to big companies like Pfizer adopting it [1]. And for engineers writing novel libraries, it clearly has a strong edge. See for example the work by NASA's JPL [2, 3], the FAA [4] or the CliMa project [5]. [1]: [link] (see also [link] ) [2]: [link] [3]: [link] [4]: ▶ this video [5]: [link]

in “Calculus with Julia” · View on HN

While many of Julia Computing's customers already use Julia in production, JuliaCon has had many talks as well. Here are a few: A Big Data application: [link] A finance application: [link] An avionics application: ▶ this video

in “An Introduction to Machine Learning in Julia” · View on HN

In case you are interested, here are slides ( [link] ) and a presentation ( ▶ this video ) about TCAS (and its successor, ACAS X)

in “Static, Ahead of Time Compiled Julia” · View on HN