> Lisp programmers tend to be ignorant of applied mathematics.
Please don't get your knowledge from some more or less useful bloggers.
Raymond a Lisp user? What had he done in Lisp? Yegge? Mostly an Emacs user with Emacs Lisp usage. Unfortunately his Lisp knowledge mostly ends with Emacs Lisp. Yegge would not recognize a Lisp, even if you hit him with Lisp Machine Manual over the head. Only Graham was and is a real Lisp user and hacker. OTOH there are too many Lisp and mathematics users.
Actually if there is a language with Mathematics background, then it is Lisp.
People like McCarthy, Minsky, Sussman are/were mathematicians.
The MIT Lisp hacker culture had lots of mathematicians from Joel Moses to Bill Gosper.
Lisp spawned symbolic mathematics which has been used in all kinds of areas with thousands of applications.
Macsyma, Axiom, Reduce, Derive and several other Lisp written maths applications have been used in many applications. The whole AI domain is based on all kinds of diverse mathematics written in Lisp: logic, statistics, probability theory, neural networks, signal processing, ...
Even students who are reading SICP are usually complaining that most of the examples are math related.
Looks like he was wrong on the Lisp front then. But actually all your examples kind of strengthen one of the points of the article (for me). That is that of we want to get things done, make a difference, then we should be focusing on applied maths and not the lambda calculus and category theory. Only a very few people are needed to focus on those things. For the rest of us a focus on those things means taking our eye off the ball to the detriment of what we work on (as an industry).
I like your point about the blog posts. If you want to find out how to really make things they're often not where it's at.
I couldn't disagree more strongly. I think systems built on the lambda calculus are building nothing less than a new form of constructive mathematics, taking it squarely out of philosophy and into the realm of practicality. Theorem provers will only get stronger, and they will change a lot about how mathematics is done.
I do finite element methods myself, and Rust has helped a lot in writing allocation-free code (meaning allocation-up-front really), and it has benefited a lot from functional/induction-centric languages.
Having better programming tools is a useful goal. Writing applications is another one. Not all applications need advanced maths, though. In many Lisp applications many different kinds of maths have been necessary and used. All kinds of symbolic and numeric mathematics have been used.
But Lisp has been also a topic of study and research - as it is also an application of symbolic mathematics. See for example Chaitin, The Limits of Mathematics. https://www.cs.auckland.ac.nz/~chaitin/lm.html or the research on Scheme: http://library.readscheme.org .
Please don't get your knowledge from some more or less useful bloggers.
Raymond a Lisp user? What had he done in Lisp? Yegge? Mostly an Emacs user with Emacs Lisp usage. Unfortunately his Lisp knowledge mostly ends with Emacs Lisp. Yegge would not recognize a Lisp, even if you hit him with Lisp Machine Manual over the head. Only Graham was and is a real Lisp user and hacker. OTOH there are too many Lisp and mathematics users.
Actually if there is a language with Mathematics background, then it is Lisp.
People like McCarthy, Minsky, Sussman are/were mathematicians.
The MIT Lisp hacker culture had lots of mathematicians from Joel Moses to Bill Gosper.
Lisp spawned symbolic mathematics which has been used in all kinds of areas with thousands of applications.
Macsyma, Axiom, Reduce, Derive and several other Lisp written maths applications have been used in many applications. The whole AI domain is based on all kinds of diverse mathematics written in Lisp: logic, statistics, probability theory, neural networks, signal processing, ...
Even students who are reading SICP are usually complaining that most of the examples are math related.
Let's see some actual Lisp applications:
* http://www.lispworks.com/success-stories/netfonds-primetrade... Real Time Stock trading. Applied maths.
* http://www.lispworks.com/success-stories/inspiredata.html Data visualization. Applied maths.
* http://www.lispworks.com/success-stories/brs-mle.html Machine Learning. Applied Maths
* http://www.lispworks.com/success-stories/memetrics-xos.html Marketing Analysis: Applied Maths.
* http://www.lispworks.com/success-stories/raytheon-siglab.htm... Signal processing: Applied Maths.
* http://www.franz.com/success/customer_apps/mechanical_cad/mt...
* http://www.franz.com/success/customer_apps/data_mining/pepit... and many many others.It's just that these Lisp users tend to publish their works not as blog posts, but in domain-specific journals and/or as applications.