What’s the Deal with Nim and Is There a Place for It in Data Science and A.I.?

Recently I attended JuliaCon 2018, a conference about the Julia language. There people talked about the various cool things the language has to offer and how it benefits the world (not just the scientific world but the other parts of the world too). Yet, as it often happens to open-minded conferences like this one, there are some unusual ideas and insights that float around during the more relaxed parts of the conference. One such thing was the Nim language (formerly known as Nimrod language, a very promising alternative to Julia), since one Julia user spoke very highly of it.

As I’m by no means married to this technology, I always explore alternatives to it, since my commitment is to science, not the tools for it. So, even though Julia was at an all-time high in terms of popularity that week, I found myself investigating the merits of Nim, partly out of curiosity and partly because it seemed like a more powerful language than the tools that dominate the data science scene these days.

I’m still investigating this language but so far I’ve found out various things about it that I believe they are worth sharing. First of all, Nim is like C but friendlier, so it’s basically a high-level language (much like Julia) that exhibits low-level language performance. This high performance stems from the fact that Nim code compiles to C, something unique for a high-level language.

Since I didn’t know about Nim before then, I thought that it was a Julia clone or something, but then I discovered that it was actually older than Julia (about 4 years, to be exact). So, how come few people have heard about it? Well, unlike Julia, Nim doesn’t have a large user community, nor is it backed up by a company. Therefore, progress in its code base is somewhat slower. Also, unlike Julia, it’s still in version 0.x (with x being 18 at the time of this writing). In other words, it’s not considered production ready.

Who cares though? If Nim is as powerful as it is shown to be, it could still be useful in data science and A.I., right? Well, theoretically yes, but I don’t see it happening soon. The reason is three-fold. First of all, there are not many libraries in that language and as data scientists love libraries, it’s hard for the language to be anyone’s favorite. Also, there isn’t a REPL yet, so for a Nim script to run you need to compile it first. Finally, Nim doesn’t integrate with popular IDEs such as Jupyter and Atom, and as data scientists love their IDEs, it’s quite difficult for Nim to win many professionals in our field without IDE integration.

Beyond these reasons, there are several more that make Nim an interesting but not particularly viable option for a data science / A.I. practitioner. Nevertheless, the language holds a lot of promise for various other applications and the fact that it’s been around for so long (esp. considering that it exists without a company to support its development) is quite commendable. What’s more, there is at least one book out there on the language, so there must be a market for it, albeit a quite niche one.

So, should you try Nim? Sure. After all, the latest release of it seems quite stable. Should you use it for data science or A.I. though? Well, unless you are really fond of developing data science / A.I. libraries from scratch, you may want to wait a bit.