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Cake day: June 16th, 2023

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  • There was a lot going on. The final count used had bush up by 537 votes out of 5.8 million cast. The close margin triggered a recount and Bush dropped to 327 vote lead.

    Nadar probably cost the democrats more votes then republicans by greater then that 327. But there were other things that hurt Gore. Some intentional some random.

    There were ballot design issues. In areas where the butterfly ballot was used Buchanan (who was also a 3rd party candidate) got way more votes than elsewhere. So if you wanted Gore saw him under Bush and selected the dot below you voted for Buchanan. See below.

    Bush. O

    / O Buchanan

    Gore. O

    In another democratic area the ballot had the presidential race split on the front and back page. 21,000 votes were invalidated because they had multiple selections for president.

    There was a large purge of mostly black felon voters. 15% weren’t felons.

    Then there were lawsuits trying to stop and start recounts in both state and federal court. The state supreme court ordered recounts while they decided if the recount should be used. Then they decided the recount should be used and set a date it was du. Then the US supreme court stopped the recount. Several days later they decided there wasn’t time for a recount and ordered the Bush ahead by 537 count to be used.

    So honestly it probably took all the above to swing the final count to Bush from Gore. I’m guessing if any one had not happened Gore would have been president.

    A personal note I live in Florida and that was the first election I voted in. My vote for president has never be closer to making a difference in who was president. It’s shaped my views on elections and voting.










  • There were similar debates about photographs and copyright. It was decided photographs can be copyrighted even though the camera does most of the work.

    Even when you have copyright on something you don’t have protection from fair use. Creativity and being transformative are the two biggest things that give a work greater copyright protection from fair use. They at are also what can give you the greatest protection when claiming fair use.

    See the Obama hope poster vs the photograph it was based on. It’s to bad they came to an settlement on that one. I’d have loved to see the courts decision.

    As far as training data that is clearly a question of fair use. There are a ton of lawsuits about this right now so we will start to see how the courts decide things in the coming years.

    I think what is clear is some amount of training and the resulting models fall under fair use. There is also some level of training that probably exceeds fair use.

    To determine fair use 4 things are considered. https://www.copyright.gov/fair-use/

    1 Purpose and character of the use, including whether the use is of a commercial nature or is for nonprofit educational purposes.

    This is going to vary a lot from training model to training model.

    Nature of the copyrighted work.

    Creative works have more protection. So training on a data set of a broad set of photographs is more likely to be fair use than training on a collection of paintings. Factual information is completly protected.

    -> Amount and substantiality of the portion used in relation to the copyrighted work as a whole.

    I think ai training is safe here. Once trained the ai data set usually doesn’t contain the copyrighted works or reproduce them.

    Effect of the use upon the potential market for or value of the copyrighted work.

    Here is where ai training presumably has the weakest fair use argument.

    Courts have to look at all 4 factors and decide on the balance between them. It’s going to take years for this to be decided.

    Even without ai there are still lots of questions about what is and isn’t fair use.



  • The solution for this is usually counter training. Granted my experience is on the opposite end training ai vision systems to id real objects.

    So you train up your detector ai on hand tagged images. When it gets good you use it to train a generator ai until the generator is good at fooling the detector.

    Then you train the detector on new tagged real data and the new ai generated data. Once it’s good at detection again you train the generator ai on the new detector.

    Repeate several times and you usually get a solid detector and a good generator as a side effect.

    The thing is you need new real human tagged data for each new generation. None of the companies want to generate new human tagged data sets as it’s expensive.