Had they chosen the language based on actual suitability and not because one of them was Chris Lattners pet-project, they could have automatically been significantly further ahead in far less time and energy.
God, please not another one with new tools, new libraries, etc.
Julia — odd language
R — not a general purpose programming language
Most of the data scientist and ml engineers I work with want a new language. So yeah it’s not a bad idea
What a profound criticism. If data scientists can't use Julia then they're not great data scientists - you have RCall and PyCall, Flux, Knet, MLJ, plotting libraries, etc. It has built-in and user-defined types, threads, multiple dispatch, ...
"Please don't think that Julia is only useful for 1. custom units 2. custom GPU kernels 3. Custom array types 4. custom bayesian priors. 5. AD through custom types 6. Task based parallelism 7. symbolic gradients with modeling toolkit 8. Agent based modeling 9. physics informed neural networks 10. abstract tables types ..."
I really tried to like Julia, wrote it for months, but it misses the mark. I've been working in analytics for 10 years and of everyone I know who's tried it I only know a couple people who like Julia.
Thats a problem, and you can see it in the adoption numbers. The Julia community keeps wanting to bury their head in the sand and pretend its not which is fun.
Anyone familiar with Python is unlikely to struggle with the syntax of Julia or with reading straightforward Julia code. In fact, I'd argue that Julia is easier to learn than Python in many aspects.
Python is now a large language, not the small, simple language of years past. Of course, in both languages (Julia and Python) you can ignore complex features.
But what Python also gives you is abundant examples and solutions to problems that are easily found on the Web.
In contrast, new(ish) programming languages suffer from a lack of libraries, examples and tutorials that slows down momentum.
~ the one end Ashton Tate delivering a shallow learning curve declarative language for semi referential data that SOHO novices can create applications with, and at the other, Stonebraker wanting to put the neatness that pure client computing enabled (dedicated cpu time and architecture leading to multimedia inexpensively) together with large system semantics [0] long before capabilities, a curse I believe was academic grant seeking behaviour in origin.
I seriously keep seeing the results of driving forces individually uniquely capable in project conditions which are very difficult to reconcile with the ultimate purpose software is used for. like, before Jim Keller quit Intel, he gave a talk explaining how Tesla was founded on a unique case where reductions to silicon were optimal for CAFÉ and that's a rare case, and how he couldn't see the 22 people in the audience he knew can tune the full performance out of modern silicon software instructions. (probably that should have called out his imminent departure considering the proliferating instruction set at Intel and his unusual call out to a success he thought worthy of mention). it used to be academia thinking about small systems applications and development, eg the Edinburgh AI group up to the 80s and Carl Hewitt thinking about how to make "next use unknown" a good thing in PLANNER. But certainly lately academic prowess has adhered to scale and the apparent needs of the largest computing users. If there's a impedance mismatch here I think it has evolved from putting the solo developer in the employ of hyperscalers without any thought about the effects of that situation.
[0] personally I remember the talk preceded the walk by some 20 years or so) and simultaneously take on the big iron boys at IBM. Oracle drove it's ugly polluting truck thru the middle and created a empire that we're still far from superannuating (not replacing, in the view of this guy seriously targeting openVMS 9.3 with the ability to have workstations join the cluster and run transactions across the system and db against the same native transaction manager and define time series objects using FORTRAN and make the cursor over the resulting blobs addressable from DEC BASIC inside user applications including Excel soon to be patched to the VMS TM via SRV-IO via a switch on the same NIC. All very much old functionality becoming the new. if I'm allowed to write about it, I will probably emphasise just how much innovative design has been enabled by expensive licensing and recount my memories of how nothing ever cropped up to address comparable needs in the FOSS world despite the foundations resting upon some of the most well documented CS and being wonderfully unencumbered by IP inhibitors due to the passing of time.
I agree it needs to be as simple as python on the high level though, or the data science end of the spectrum will just never adopt it.
before getting distracted by dot net and mono, I feel I have to lay the blame (and my nineties) at the feet of CORBA and the Object Management Group. Seriously though, what was the OMG but a UNIX consortium trying to break the hold of DEC and VMS over the most complex critical systems..
I stand with my "pitched by Google" :)
We have the example of "Swift for Tensorflow", which was pitched by Google as the "next-generation platform for machine learning" (that's from their own website) and didn't result in anything meaningful and is now dead as far as I understand. I'm not that convinced that would work better with Julia.