ShinyProxy is the solution to deploy data science apps (#Shiny, #Dash, #Streamlit, #Jupyter and more). v3.2.1 sees improvements to the ECS backend. Also, it contains multiple fixes for XSS vulnerabilities. Check out the release notes at oa.eu/laE4lS #opensource

#opensource #rstats pipeline and #shiny app to build high-quality molecular databases and lay the data foundation for #ai models in medicinal chemistry. 🔗 Explore the article here: oa.eu/mk67B7 💻 Try out the prototype of the customized app: oa.eu/9xzXlS

RDepot is enterprise-grade #opensource software to manage #rstats and #Python repositories. In v2.5.1 packages built for a specific Linux distribution, architecture and R series can now be stored for blazingly fast installation. More goodies at oa.eu/x1xJTo

Today is #ilovefs Day All our services and free software products stand on the shoulders of giants. Thank you #rstats, #Python and #Julia. Thank you #Rust, #Kotlin, #C++ and #Java. Thank you #Kubernetes, #Singularity and #Docker. Learn more: oa.eu/v78aFW

Crane is a new #opensource product to host #datascience artifacts: reports, documentation sites, or packages and libraries. Part of our FOSS suite to build data science platforms and playing well with ShinyProxy and RDepot. Read more at oa.eu/aPxUcm #rstats #python

The 'tiny subset' I mention does not refer to the harmful content (to be addressed through a data commons with contributor agreements), but rather to a subset of the data types that are used in ML. If you predict response to drugs based on omics, you use omics data (not a corpus)

Indeed. Collective and intentional management as a shared resource is possible for this type of data: no need to scrape the ocean floor to catch fish (and throw all else away). Also, this type of data is a tiny subset of all data types one can train ML models on; AI > ML > LLMs.

Many thanks for the interesting reference. Illegal content should not be in anyone's possession and therefore should not end up in a training dataset. This seems a problem to be solved upstream of Open Source AI. It does mean you cannot take shortcuts by using unguided crawling.

What do you mean with problematic content in training data? Can you give a concrete example?

We mentioned being practitioners of open source data science (navigating the challenges of both open source and ML) since you mentioned being an open source advocate ('trust me, I am an open source expert'), but happy to go over these challenges 1 by 1.

There is nothing ideological about it. There is a clear definition of open source that refers to 4 freedoms. If you apply these 4 freedoms to AI, then you need the training data to be able to meaningfully understand and modify (2 of the 4 freedoms) the resulting models.

We agree, of course, but not having access to the training data severely reduces the freedom to understand and to modify. You can not reasonably call something 'open source AI' if it does not respect the basic four freedoms that define the idea of open source.

Start from the four freedoms (a.o. freedom to study and to modify) and you can only end up with needing the training data. Otherwise both of these (modify and study) are severely limited. 1/2

The OSI currently indicates it can do with a 'description' of the data. What is the next step? That you provide a 'description' of your code? 2/2

ShinyProxy 3.1.0. Instant access to your apps through container pre-initialization. Container sharing for massive deployments. #AWS ECS backend and more. Blog post at oa.eu/Uod85E and release notes at oa.eu/9OO2AV. #opensource #datascience #python #rstats

A selection of our athletes running for #UNICHIR. BENISUR - UNICHIR provides high-quality surgical and obstetric care to almost two million people in Beni, Eastern #Congo. If you want to learn more or make a donation: oa.eu/hOgm1U

We hosted the Belgian #punkrock band PARKS for the shooting of the music video for their song Big Frog. oa.eu/ap2g2A Happy #WorldFrogDay !

The expiry dates on pharmaceutical products are the result of extensive research. We just published a Bayesian methodology to predict shelf life in a more robust and interpretable way. Here's the paper: oa.eu/soBMjH #Bayesian #Statistics #Pharmaceutical