I don't know how this is emerging. If I take a stab at it, BEAM/OTP/Erlang/Elixir is very good at coordinating among many concurrent processes as well as handling failures that come with it. There really isn't any other language platform that does that as well, including Python. (Although there is interest in recreating those advantages in Rust). The distributed computing is built on top of those concurrency primitives. So when one thinks about adding numerical computing into the mix, I think of:
- Broadway (an Elixir library) that handles workflows from unreliable data sources. It is already in production use to handle massive, distributed web scraping operations, so embedding a data transform in there with numeric computing will have good synergies
- IoT applications, both at the edge with Nerves, and with the command/control for IoT. Each of the IoT devices are unreliable (power and network), and OTP already has great facility for that. Being able to do numeric computing at the edge is a thing.
We are not trying to make Elixir the new AI language, but one possible language for AI. @hosh covered some of the use cases in his reply and I can add two more insights:
* Lately we have seen functional ideas brought into Python through projects like JAX and Thinc.ai and I think exploring those concepts within a functional language is both interesting and exciting (and we are not the only ones doing so!)
* Besides IoT and data pipelines, there is potential in mixing some of the current ML trends, such as Distributed and Federated Learning, with the capabilities of the platform
We likely aren't much attractive to Python developers right now, unless you are interested in contributing/designing ML tooling (for learning, hobby, or professional reasons) or you have a use case that suits one of the mentioned strengths of the platform.
* pandas - explorer
* numpy - nx
* statsmodel - Not sure this exists yet
The article answers this pretty well.
However, our goals are also to:
* make Elixir a suitable platform for new Machine Learning developments
* fully leverage the power provided by the platform Elixir runs on, the Erlang VM
* provide consistency and stability, especially when working on a domain that is still actively evolving
For those reasons, we chose to invest on Nx as its own foundation, agnostic to any particular framework. The road is definitely longer but we also believe the pay-off will be higher too!
Give elixir a shot. I used to use python (though not as much as I used ruby) and I will never go back to python.
After learning other languages though, I cannot see the point behind using Python aside from broad adoption. Couldn't imagine choosing it for any problem I needed to solve.
I was actually really looking forward to it after hearing for years about how Python would have "one way to do everything" but there's a gazillion different ways just to run Python on your machine, different versions of it to choose from.
Should I be running the local system version, PyPy, Stackless, Anaconda, Miniconda? Is PyEnv the weapon of choice for multiple Python codebases or is it one of the many others? The Python 2 vs 3 migration stuff I expected to be a little wonky but all of the stuff on top of it was just weird for a community that had so long been advertised as having agreed on solutions.
I did finally get why a lot of experienced developers call it "The Okayest" language.