The principles are the four essential freedoms of free software, and the one job the OSI had in implementation was to work out what had to be open to protect them.
Hint: it’s the data.
Like many of the objectors, I’ve been in Open Source since before there was Open Source or the OSI, and am now doing a masters specialising in ML while coding an AI OS. This is condescending BS from talking heads who don’t even claim to have AI expertise.
Meanwhile their chosen “co-design” process is literally the “Do Your Own Research (DYOR)” of technical standards:
“We believe that everyone is an expert based on their own lived experience, and that we all have unique and brilliant contributions to bring to a design process.”
What a clown show. How do we get off this train?
> "The reality is that if only a handful of companies and a handful of governments have the resources" to rebuild models, it is not a practical goal for open-source AI.
Any chance one could build a (working) system for training in a distributed way?
Because e.g. Folding@home was able to accumulate quite some computational power this way:
> Folding@home is one of the world's fastest computing systems. With heightened interest in the project as a result of the COVID-19 pandemic,[8] the system achieved a speed of approximately 1.22 exaflops by late March 2020 and reached 2.43 exaflops by April 12, 2020,[9] making it the world's first exaflop computing system. This level of performance from its large-scale computing network has allowed researchers to run computationally costly atomic-level simulations of protein folding thousands of times longer than formerly achieved.
