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Joined 1 year ago
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Cake day: July 22nd, 2024

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  • Helping a genocide is very much illegal. If you sell precursor chemicals for poison gas to a death camp operator, you are also complicit.

    After Oct. 7 the usage by the IDF exploded. In early 2024 reports about the AI tools used to justify slaughtering Civilians en masse came out, employees have been raising alarms through the “proper” channels and then went to public protest as Microsoft ignored them and cracked down on any mention of it in Forums, Town Halls and the like.

    They absolutely know that they are complicit and would rot in prison the rest of their lifes if this goes to court properly.



  • Which are based on LLMs or other neural network models. It is kind of the thing that language models are actually good at.

    See DeepL for example: https://en.wikipedia.org/wiki/DeepL_Translator

    The service uses a proprietary algorithm with convolutional neural networks (CNNs)[3] that have been trained with the Linguee database.[4][5]
    According to the developers, the service uses a newer improved architecture of neural networks, which results in a more natural sound of translations than by competing services.
    The translation is said to be generated using a supercomputer that reaches 5.1 petaflops and is operated in Iceland with hydropower.[6][7]
    In general, CNNs are slightly more suitable for long coherent word sequences, but they have so far not been used by the competition because of their weaknesses compared to recurrent neural networks.
    The weaknesses of DeepL are compensated for by supplemental techniques, some of which are publicly known.


  • The thing with “just works” in monopolies is that it eventually stops working. I already have terrible excel bugs all the time on my work computer. Left clicking a cell sometimes just selects half a dozen adjancent cells. You vlick something and all of a sudden the rendering just goes completely haywire… You have two larger tables open and it just crashes…

    Things will only get worse from this, until the global economy will loose trillions to being stuck with Microsoft.



  • Labour won in a landslide after the Brexit mess rightfully fucked up the Tories. They do this because they aren’t center left, but a right/far-right party by their ideology now. They don’t have to do this. They want to do this.

    The “Labour/Social Democratic” parties all over Europe have been sliding into right/far-right authoritarianism and Racism over the past decades, after many of them slided into Neoliberalism at the end of the 90s and in the 2000s.




  • The recognition of the pattern is done by the machine learning. That is the core concept of machine learning.

    For the interpretation you need to use your domain knowledge. Machine learning together with knowledge in the domain analyzed can be a very powerful combination.

    Another example in research i have heard about recently, is detection of brain tumors before they occur. MRIs are analyzed of people who later developed brain tumors to see if patterns can be detected in the people who developed the tumors that are absent in the people who didn’t develop tumors. This knowledge of a correlation between certain patterns and later tumor development could help specialists to further their understanding of how tumors develop as they can analyze these specific patterns.

    What we see with ChatGPT and other LLMs is kind of doing the opposite by detaching the algorithm from any specific knowledge. Subsequently the algorithm can make predictions on anything and they are worth nothing.


  • I agree with you on almost everything.

    It’s like the opposite of classic ML, relatively tiny special purpose models trained for something critical, out of desperation, because it just can’t be done well conventionally.

    Here i disagree. ML is using high dimensional statistics. There exist many problems, which are by their nature problems of high dimensional statistics.

    If you have for an example an engineering problem, it can make sense to use an ML approach, to find patterns in the relationship between input conditions and output results. Based on this patterns you have an idea, where you need to focus in the physical theory for understanding and optimizing it.

    Another example for “generative AI” i have seen is creating models of hearts. So by feeding it the MRI scans of hundreds of real hearts, millions of models for probable heart shapes can be created and the interaction with medical equipment can be studied on them. This isn’t a “desperate” approach. It is a smart approach.