My Views on AI Generated Content

I had a bit of time this morning so wanted to do a few more responses quick here.

No actually, I dont want to go to far into it here so I would encourage you to bring it back up in the 5th post or feel free to DM me if you want to chat about it more, but my larger concerns is the EU’s copyright directive (mainly how this effects the one mulligan we get) and general civil liability if we let something like a malicious exe slip through the cracks as an example.

These are more just questions I need to look into and tbh with how I am planning it it may not matter anyway. But there is a difference between moderation and curation and generally speaking the more curation that is done the more responsibility you have to take for what you are curating.

Fair enough. While I may not agree with your premise, I cant really argue with that. I think it does highlight or differing viewpoints very well though. Your arguments feel very deontological in nature where I am looking more from a utilitarian perspective. Neither is right or wrong per se, but due to their differing viewpoints lead to different perspectives on how to approach the problem.

Sorry, I think my example muddied the waters of what my intended point was. I was more meaning to draw your attention to the Overton Window and how forced adoption within the work place could lead to further normalization of AIGC over time which would make it harder for you fight back against it becoming a standard in the broader sense. If your intent was focusing more on just this specific community that is fair but I thought you where talking in the more broader sense so if I misread that sorry about that.

More or less though my intent was to bring that up as a factor for you to add to your consideration.

Quick warning, going to be showing my train nerd side here a bit.

Generally speaking there a 3 reasons these companies make the tools freely available and available to consumers in general.

  1. Training - When some one uses an AI tool you always see that little thumbs up or down. That is used to communicate back to the company what is considered good or bad output. This raw data is then used both directly in another training pass and indirectly to create another LLM that is specialized to only judge the quality of the output which can be used for synthetic training. Supervised learning models are very data hungry so data is always a massive motivating issue.
  2. Advertising - Its kind of like how freight rail in the US used to use their passenger rail service to advertise how quick and reliable their freight service was even though passenger rail usually lost them money. These tools are made available like that to more try to show perspective clients how much more advance their models are compared to their competitors as well as to drum up more investor hype by making it a public display.
  3. Supplemental Revenue - Since they are burning cash anything to help stem the flow does help even if its not by much. Its kind of like how I am with ads right now with Weight Gaming. They are making so little I kind of want to remove them, but since we run at a loss anyway $100 is still better then no dollars even if I dont feel like its worth it.

I dont think this was what @someoneoutthere was arguing. I could be wrong, but I think they where making a similar point to what I was saying that if we do want to affect any actual change it would have to be done collectively, not on an individual scale. Also, its not an unfair assertion that framing its use as a moral failing could make any collective action more difficult as it could accidentally alienate possible supporters (especially those more on the edge) by making them feel like they are “the bad guys” even if that is not the intent.

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