| I haven't looked into it myself, but I'll try to understand what they do better, thanks for letting us know. I will say that: 1. It's not open source, so hard for us to compare other than running black-box experiments. 2. Oracle, so presumably that comes with all the Oracle-ecosystem buy-ins that implies, which might not be ideal for many people. As a purely personal opinion: I guess it's good to know that other people are thinking in the same direction as us, but at the same time I personally would like for widely-used ML libraries to be open-source. If these models are going to be used as generator of important decision making algorithms, ideally both the model and the algorithm should be open source. The later is up to whoever is building the algorithm, but I think if we can get the zeitgeist to move towards the later being open source as the norm that can alleviate a lot of potential harm and has little downside. I.e. Do you feel comfortable with the NHS off-sourcing important decision making to algorithms that are proprietary black boxes? Considering that it's funded by the tax paying public and it's supposed to service that public. "Secret" laws used to be a norm in the past e.g. in large civilziations like the Roman empire, where the norm evolved to be that only "schooled" men could understand the law due to complexity, or in most of medieval Europe where the bible was foundational for morality but closed off to a small subset of the population that knew Greek or Latin and could get their hands on it. But in general that seems to have caused more harm than good. It seems reasonable to ask that, if algorithms are going to be used by governments in decision making, those should be entirely open. Ideally the ones used by corporations should be open to whatever degree is possible, to avoid run-off harm from buggy or unaligned systems. |