• 2 Posts
  • 41 Comments
Joined 1 year ago
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Cake day: June 14th, 2023

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  • Tu as des subsides pour la LAMAL. Pour vivre à 1300 tu es obligé de les utiliser.

    Je confirme 1300 c’est obligatoirement une colocation ou en couple et tu ne vas jamais au restaurant.

    Mais à 4000 tu n’as plus toutes ces contraintes. Quand j’ai touché 3600 CHF pour la première fois j’avais vraiment l’impression d’être riche. Je pouvais aller au restaurant tous les jours, partir à l’autre bout de l’Europe sur un coup de tête etc…








  • Akisamb@programming.devtoFrance@jlai.luJérôme Peyrat
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    4 months ago

    Depuis janvier 2021, il est conseiller politique auprès de la délégation générale de La République en marche. Jérôme Peyrat est condamné en 2020 pour violences conjugales sur son ancienne compagne, ce qui le conduit à renoncer à sa candidature aux élections législatives de 2022.

    Il finit par se représenter aux élections législatives anticipées de 2024 malgré les polémiques que sa candidature lève

    Le petit filou, il a attendu qu’on l’oublie. Je ne comprends pas pourquoi les partis politiques tiennent autant à garder ces éléments.








  • I’m afraid that would not be sufficient.

    These instructions are a small part of what makes a model answer like it does. Much more important is the training data. If you want to make a racist model, training it on racist text is sufficient.

    Great care is put in the training data of these models by AI companies, to ensure that their biases are socially acceptable. If you train an LLM on the internet without care, a user will easily be able to prompt them into saying racist text.

    Gab is forced to use this prompt because they’re unable to train a model, but as other comments show it’s pretty weak way to force a bias.

    The ideal solution for transparency would be public sharing of the training data.




  • It’s absolutely amazing, but it is also literally and technologically impossible for that to spontaneously coelesce into reason/logic/sentience.

    This is not true. If you train these models on game of Othello, they’ll keep a state of the world internally and use that to predict the next move played (1). To execute addition and multiplication they are executing an algorithm on which they were not explicitly trained (although the gpt family is surprisingly bad at it, due to a badly designed tokenizer).

    These models are still pretty bad at most reasoning tasks. But training on predicting the next word is a perfectly valid strategy, after all the best way to predict what comes after the “=” in 1432 + 212 = is to do the addition.