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So, that raises the question: did Python win, after all? ... In many of these "Python circles", it seems like programming is not at all an art. Creating a beautiful piece of code is not important in such circles. Self-promoting is.

My personal opinion is that Perl, Python and Ruby are essentially equivalent. At least I enjoy coding in all three ... not so much in Javascript, and especially not PHP.

As noted in the root node, the Why Perl Didn't Win essay argues (convincingly IMHO) that to remain popular over time, a programming language must be compelling for new projects ... while further noting this is not solely a technical concern; it's a concern of the language community and ecosystem.

Curiously, this theory of programming language adoption reminds me of Planck's Principle:

An important scientific innovation rarely makes its way by gradually winning over and converting its opponents: it rarely happens that Saul becomes Paul. What does happen is that its opponents gradually die out, and that the growing generation is familiarized with the ideas from the beginning: another instance of the fact that the future lies with the youth.

-- Planck's Principle (Scientific autobiography, 1950, p.33,97)

BioPerl and PDL

Illustrating community and ecosystem trumping language, notice what a superb job Lincoln Stein did twenty years ago in evangelising Perl and developing quality Perl libraries in the Bioinformatics space. Perl remains compelling in this domain today as indicated by:

For a long list of PDL References see:

See also:

On CPAN:

AI and Machine Learning

Sadly, Perl is way behind in the newer domain of AI and Machine Learning. Googling for Perl books on this topic looks barren ... I further noticed that the Perl books about AI PM question did not receive a single reply.

Googling for Python books on AI and Machine Learning is a totally different story with many recent books available, such as:

and many more ... so if I was embarking on an AI and Machine Learning career today, I'd choose Python, not Perl. This is a specific example of why Perl is losing market share.

BTW, I used to work with a PhD AI researcher (and novice computer programmer) and she did most of her research using Python AI libraries utilising GPGPU.

AI References:

  • AI::MXNet and other modules in the CPAN AI namespace.

Array Processing/HPC/GPGPU References

Mathematical

Embedded

Other

I originally missed other Perl strengths in the Scientific Computing domain:

Science Perl Committee

Some Perl Monks interested in using Perl in Science

See Also

Updated: Many references added long after the original reply was made. Dec 2022: moved some PDL links to Re: first stumbling steps in PDL (PDL References). Dec 2023: Added Planck's Principle analogy.


In reply to Re^2: Organizational Culture (Part II): Meta Process (BioPerl/PDL/AI/Embedded/Data Science References) by eyepopslikeamosquito
in thread Organizational Culture (Part II): Meta Process by eyepopslikeamosquito

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