gigantum

You just always start with the base case. It could be recursion is just so simple that your mistake is thinking there must be something complicated.

Also, if the algorithm is not tail recursive, you may be missing that there is an implicit call stack where intermediary results are pushed to.

Like some of the other ML/AI posts that made it to the top page today, this research too does not give any clear way to reproduce the results. I looked through the pre-print page as well as the full manuscript itself.

Without reproducibility and transparency in the code and data, the impact of this research is ultimately limited. No one else can recreate, iterate, and refine the results, nor can anyone rigorously evaluate the methodology used (besides giving a guess after reading a manuscript).

The year is 2019, many are finally realizing it's time to back up your results with code, data, and some kind of specification of the computing environment you're using. Science is about sharing your work for others in the research community to build upon. Leave the manuscript for the pretty formality.

Makes sense. The founding team came out of large scale brain imaging research, but the goal is that anything you'd do in a notebook / web UI environment (like Jupyter, RStudio, etc.) you should be able to do with the Gigantum Client.

The difference from the standard approach for those tools is that we automate some command line operations (Git, Docker, etc.), and provide UI for the rest. We provide a stable foundation for how to organize data using Git LFS, along with an optimized S3 storage back-end if you need to cherry pick large datasets.

Our main goal is to improve the quality of "academic" science, but we're open to anything that fits!

At Gigantum (https://gigantum.com), we've been working with brain imaging researchers on enabling exploratory analyses that are easy to share or reproduce. For now, our collaborators have focused on large public datasets like the Healthy Brain Network (http://fcon_1000.projects.nitrc.org/indi/cmi_healthy_brain_n...) and OpenNeuro (https://openneuro.org/).

I'm curious to know how you're managing the AI engineering side of things - I know there's nothing close to "the right answer" yet in terms of pipelines for brain images. And of course I'd be interested how folks could collaborate on developing better algorithms for understanding these images (with Gigantum and otherwise).

Certainly, if you have a collaborative project and would like to try Gigantum for coordinating code, data, and computational environments, we'd be happy to support that! We provide a one-click solution to publish a project so that someone else can pick up exactly where you left off.

Re: Type system -- at Gigantum we very aggressively enforce all classes and methods in the core libraries must be fully typed using mypy. The depth and expressiveness of mypy rivals that of any other strongly typed language.

Interesting, however there is no indication from the publisher or researchers how this result can be reproduced. It's nice that they put in some of the training data, but imagine how much more impactful to the community this could be if those interested could reproduce - and iterate - on this...

At Gigantum (https://github.com/gigantum/gigantum-client) this is literally our raison d'être to make this process as simple as possible.