Most companies use OpenSource in one way or the other.
Nonetheless, a company like MS has probably already build visualizers purely commercially (see excel) or/and is absolutly able to write it themselfs.
If I release a novel visualization library on github under some open source license I want it to be attributed to me. I don't want some specialized LLM to be lifting and offering the same visualization concepts to unnamed corporates for a hefty fee without me ever even knowing about it and these corporates pretending they don't know where that concept is coming from.
It is you choice whether you think that is a problem and how "novel" it is. Theft after all has a very long history.
Furthermore, it isn't theft to learn from others' work and reproduce similar qualities.
Good to know that the prevailing commercial tech culture now sees plagiarism and stealing ideas without attribution as the modern way of doing business and hopes that dressing things up under some algorithmic veil will hide the act.
I guess the pit of moral decline has no bottom. The consolation is that theft has never been the road to wealth. Once the plundering is over the only thing that is left is a wasteland.
It seems that Microsoft has finally found a way to kill the open source "cancer".
I.e. what is being stolen without attribution? I'm genuinely not getting what you mean in this specific case.
It seems to me that such solutions are soon to be within the set potentially constructed by an LLM.
Let me break it down for you. If I ask for a visualization that squares the circle and there is one repo that has an example of squaring the circle, the LLM will "arrive" at a way of squaring the circle.
If (1) an LLM is able to arrive at solutions in the same class of difficulty as the solution for the target problem and (2) it's not possible to establish the provenance of the solution actually offered by the LLM, then what's the argument for assuming that the solution is based on IP rather than constructive reasoning?
> Low Resource Grammars: ... LIDA depends on the underlying LLM s having some knowledge of visualization grammars as represented in text and code in its training dataset (e.g., examples of Altair, Vega, Vega-Lite, GGPLot, Matplotlib, represented in Github, Stackoverflow, etc.). For visualization grammars not well represented in these datasets (e.g., tools like Tableau, PowerBI, etc., that have graphical user interfaces as opposed to code representations) ), the performance of LIDA may be limited without additional model fine-tuning or translation.
In other words, open source programmatic visualizations are required to feed the LLM, which then can, e.g., be licensed to corporates to accelerate various internal exploratory data analyses. A win-win for corporates and LLM providers.
Spot the loser.