[badminton1]

Access to AI/ML is already very democratic. Takes little time to set run Tensorflow from a docker image, or learn from Jupyter notebooks, etc... with fully open source projects where you can consult the source code and see how an algorithm is implemented.

In contrast this involves running a proprietary operating system, IDE, closed source, etc... Quite the contrary to anything that could be considered democratic.

Then, ML is all about volume. Open a spreadsheet with more than 10000 rows in Excel and see it squirm in pain.

[pscsbs]

Takes little time to set run Tensorflow from a docker image, or learn from Jupyter notebooks, etc... with fully open source projects where you can consult the source code and see how an algorithm is implemented.

This is exactly the problem -- what you're describing is not easy for anyone outside of tech to do. If you want to, say, run a simple text classification task and have thousands of labels, this is way overkill. Machine learning has the opportunity to become a common place utility for automating repetitive tasks, and the barrier to entry does not need to be learning Tensorflow, Docker, and Jupyter.