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Also, since Yann LeCun said yesterday that Deep Learning needs a new language, this has become a major topic of interest.

https://venturebeat.com/2019/02/18/facebooks-chief-ai-scient...

Of course, our view is that Julia is one such language that people should consider seriously. The talk linked here is a peek under the hood and shows that differentiable programming in Julia is not a special add-on, but something that fits naturally within the language.



A long time ago, Yann LeCun wrote Lush, which was a numerically-focused Lisp dialect, with a focus on C interop. It might be one of Julia's closest sibling.

> Lush is an object-oriented programming language designed for researchers, experimenters, and engineers interested in large-scale numerical and graphic applications. Lush is designed to be used in situations where one would want to combine the flexibility of a high-level, weakly-typed interpreted language, with the efficiency of a strongly-typed, natively-compiled language, and with the easy integration of code written in C, C++, or other languages.

http://lush.sourceforge.net/index.html

Is there more detail about his proposal? It must be well thought-out.


A Lisp with expressive macro support is at least what is needed. Nothing I know of nothing else that has the affordances to support internal DSLs and creative control flow.


Julia as hygienic macros. From their documentation [1]: "The strongest legacy of Lisp in the Julia language is its metaprogramming support. Like Lisp, Julia represents its own code as a data structure of the language itself."

Parts of Julia are even implemented in Lisp [2], although they tend to be ported to Julia now IIUC. But it's clear the Julia developers are well aware of Lisp and its strong points.

[1] https://docs.julialang.org/en/v1/manual/metaprogramming/ [2] https://discourse.julialang.org/t/the-role-of-femtolisp-in-j...


Julia also feels a bit like Dylan, another Lisp offspring with Algol-like syntax.


That is super cool, I didn't know that.




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