It is important to set our expectations and goals accordingly. I believe it is completely unrealistic for Nx success to be measured as winning data scientists hearts or by matching Python’s ecosystem.

As I mentioned in the podcast, I would already consider a success if we change the answer to “Can Elixir be used for Machine Learning?” from a clear “No” to a “Maybe”. Maybe you are doing a prototype in Elixir and you can quickly hook an existing model instead of having to learn another language and tooling. Or maybe you are integrating ML models with Nerves to run on the edge. I believe those are attainable and it will already be a big success, simply because it was not possible to do any of this a month ago. Once we get to a “Maybe”, we can start discussing if, when, and how to make that a “Yes”.

As we saw on Elixir’s early days, the best way to adopt the language was to try it on small, well-defined problems, and get a feel for it. I don’t think Nx is going to be any different. Try it on small and reasonably well-defined problems, so you can get an initial look and give us feedback, and decide on how to move forward accordingly. Hopefully this will happen enough times for us to continue improving.

It took Elixir going into Machine Learning for my first post here! Kudos to Jose, Sean and everybody else who helped launch this in record time.

The fact that Nx will be like Numpy and have some feature parity will be awesome. Just FYI, Numpy has been around 2006 and it wasnt till 2011 (5 years later) that Numpy could spread its wings and be a productive tool (at least for me) speaks volumes on what Jose and team accomplished in three months!.

On the Thinking Elixir podcast, Jose kept talking about tensors. For math geeks, tensors are really generalized matroids. Matroids are a combinatorial structure that lives at the intersection of Optimization, Linear Algebra, and Graph Theory (Source: Wikipedia). Matroids have many applications in geometry, topology, combinatorial optimization, network theory, coding theory, and machine learning. That’s where I would look for use cases and applications.

Given what Jose was discussing around Softmax classifiers, one could deduce, he is talking about Deep Learning based computer vision use cases like image classification and object detection (e.g. boundary boxes). Computer vision has one of the richest set of use cases in AI.

• Protein structure prediction for medicine and vaccines
• Human Pose Estimation
• Image transformation like Snapchat filters
• Converting 2D images into 3D models.
• Medical Diagnostic Imaging analysis.

I have many more questions around the hardware side:

• How will Nx interface with CPU, GPU and TPU (Tensor Processing units)? I got excited when Jose talked about how there are flags for GPU based operations.

• Will Nx have support for GPU Acceleration using CUDA (proprietary) and OpenCL (open source)?

• What other tensor compilers are supported in the initial release of Nx?

• Today, Keras (higher level syntactic sugar wrapper for TensorFlow) is used mostly as TensorFlow is hard, so how does Nx play with Keras or TensorFlow? or even the next incumbent Pytorch?

• The next question would be around are are we talking about small datasets or large ones?

Also, Interested in seeing a comparison akin to a Python vs Elixir showdown (all things equal) for the same training and inference workloads on the same hardware.

Sorry for the long post. I am so hyped and excited for Nx, it feels like the dam burst for me.

I live in Toronto (the spiritual home of Deep Learning) and the 2019 NBA Champions aka Toronto Raptors.

You have my vote to fork a new topic or category page for Nx in this forum.

Outside of the typical AI and ML space, here some examples of Jax (one of Nx’s inspirations) thriving in a broad number of spaces:

• Molecular Dynamics Simulations
• Differentiable Cosmology
• Differentiable Path Tracing
• Neuroevolution

It seems as well that libraries like Jax are growing increasingly more popular for research. See DeepMind’s Jax Ecosystem

Of course, we are in our early stages, but hopefully we get to a point where people are comfortable adopting Elixir for some of their numerical work rather than shelling out to Python, Julia, R, MatLab, etc.

There are a lot of possibilities, it is only just the beginning!