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One reason to keep an eye on Julia if you are envisioning a long and varied career in the broader computational / data science world is that its not at all clea

One reason to keep an eye on Julia if you are envisioning a long and varied career in the broader computational / data science world is that its not at all clear which will be the leading platform going forward (Python, R, Julia or something else altogether). This ambivalence might sound absurd on the face of the exponential recent growth of Python (which apparently enticed even some people with serious mojo to get into the act) but take two steps back with me and look at the big (if still hazy) picture: We are going through a remarkable period where complex algorithmic applications left academia and research labs and diffuse into mainstream society and the economy like never before. This process carries enormous risks and opportunities, which are currently basically... ignored (well, the risk side). Despite its undeniable strengths and loveability, Python is actually a poster child of the move-fast-and-break-things phase. It is not necessarily best placed for the next phase. The next phase will invariably see a re-examination of all aspects of the stack and qualities that will be prized will be those that eliminate the frictions and risks associated with the large scale deployment of algorithms. The stakes are high, which means there will be plenty of resources seeking to create reliable platforms. The future need not look like the past. None of the usual suspects ticks all the boxes. In fact we don't even know all the boxes yet. Depends how fast and how seriously models and algorithms get deployed at scale. Python, Julia and R have been propelled forward by circumstances as the main algorithm-centric platforms, and they have each their various warts and blessings but the near and mid-term future will test how well they can deliver on aspects they may have not be designed for.

dS0rrow 3y ago
> society and the economy like never before. This process carries enormous risks and opportunities, which are currently basically... ignored (well, the risk side) What risks are you talking about here?
- nologic01 3y ago
  take the entire stack (including all dependencies, toolchains etc.) and think about scenarios of accidental or malicious malfunction, but also reproducibility, auditability of outcomes, that sort of stuff. The overall ability to provide locked-down, performant, safe, secure deployments of high-quality, validated algorithms without breaking the bank. In other words the risks (but also the frictions / costs) in the "productionising" of algorithms.
jstx1 3y ago
> its not at all clear which will be the leading platform going forward (Python, R, Julia or something else altogether). This ambivalence might sound absurd on the face of the exponential recent growth of Python It sounds absurd because trends don't reverse overnight. You can be fairly confident that Python will be the top language in this space for a while and that R will never be the top choice for most applications.
- nologic01 3y ago
  I think so too in the short term (1-2 years at least) Python will gently move into the last stage of its adoption curve (even doing nothing). But now is a time where at various high places people will say: "Ok you got my attention. What is this snake language you are talking about and explain why I should bet the house on it". And the answer is not simple.
  - jstx1 3y ago
    What's your argument - that people who don't understand the space at all will dictate rewrites in other languages? It seems very unlikely.
    - nologic01 3y ago
      you obviously have never worked in the corporate world
      - jstx1 3y ago
        The corporate world will want to do $x because $x is in the news, they won't be making nuanced arguments about tradeoffs in a domain they don't understand. Least of all arguments that go entirely against the trends in the industry.
- npalli 3y ago
  Irrespective of whether Julia ends up the winner of the shift or if the shift happens, it is quite possible for trends to reverse very fast. See Perl or Objective-C for that matter. https://www.tiobe.com/tiobe-index/perl/ https://www.tiobe.com/tiobe-index/perl/ https://www.tiobe.com/tiobe-index/objective-C/ https://www.tiobe.com/tiobe-index/objective-C/ In 2006 (for Perl) and 2014 (for Objective-C) it was clear they had the momentum for their particular space however their limitations were well known and as soon as a better language came along the momentum flipped in an equally dramatic manner. Python is much more widespread so it will remain strong in some areas but you could see the flip in ML/DS given challenges productionizing across broad capabilities (not just doing NN's). As the joke goes -- python is the second best language for everything, if you know only two languages. With ML expanding beyond narrow big tech domains there will be need for specialized languages like Julia (and others perhaps like Mojo etc..)

dS0rrow 3y ago

> society and the economy like never before. This process carries enormous risks and opportunities, which are currently basically... ignored (well, the risk side) What risks are you talking about here?

nologic01 3y ago
take the entire stack (including all dependencies, toolchains etc.) and think about scenarios of accidental or malicious malfunction, but also reproducibility, auditability of outcomes, that sort of stuff. The overall ability to provide locked-down, performant, safe, secure deployments of high-quality, validated algorithms without breaking the bank. In other words the risks (but also the frictions / costs) in the "productionising" of algorithms.

jstx1 3y ago

> its not at all clear which will be the leading platform going forward (Python, R, Julia or something else altogether). This ambivalence might sound absurd on the face of the exponential recent growth of Python It sounds absurd because trends don't reverse overnight. You can be fairly confident that Python will be the top language in this space for a while and that R will never be the top choice for most applications.

nologic01 3y ago
I think so too in the short term (1-2 years at least) Python will gently move into the last stage of its adoption curve (even doing nothing). But now is a time where at various high places people will say: "Ok you got my attention. What is this snake language you are talking about and explain why I should bet the house on it". And the answer is not simple.
- jstx1 3y ago
  What's your argument - that people who don't understand the space at all will dictate rewrites in other languages? It seems very unlikely.
  - nologic01 3y ago
    you obviously have never worked in the corporate world
    - jstx1 3y ago
      The corporate world will want to do $x because $x is in the news, they won't be making nuanced arguments about tradeoffs in a domain they don't understand. Least of all arguments that go entirely against the trends in the industry.
npalli 3y ago
Irrespective of whether Julia ends up the winner of the shift or if the shift happens, it is quite possible for trends to reverse very fast. See Perl or Objective-C for that matter. https://www.tiobe.com/tiobe-index/perl/ https://www.tiobe.com/tiobe-index/perl/ https://www.tiobe.com/tiobe-index/objective-C/ https://www.tiobe.com/tiobe-index/objective-C/ In 2006 (for Perl) and 2014 (for Objective-C) it was clear they had the momentum for their particular space however their limitations were well known and as soon as a better language came along the momentum flipped in an equally dramatic manner. Python is much more widespread so it will remain strong in some areas but you could see the flip in ML/DS given challenges productionizing across broad capabilities (not just doing NN's). As the joke goes -- python is the second best language for everything, if you know only two languages. With ML expanding beyond narrow big tech domains there will be need for specialized languages like Julia (and others perhaps like Mojo etc..)