• 4 Posts
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Joined 2 years ago
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Cake day: July 22nd, 2024

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  • 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.


  • We know that our current way of economic growth and consistent new “inventions” is destroying the basis of our life. We know that the only way to stop is to fundamentally redesign the social system, moving away from capitalism, growth economics and ever new gadgets.

    But facing this is difficult. Facing this and winning elections with it is even more difficult. Instead claiming there is some wonder technology that will safe us all and putting the eggs in that basket is much easier. It will fail inevitably, but until then it is easier.



  • Did you consider that project managers often have to follow all sorts of company standards, have to figure out a way to get a dozen departments with conflicting standards together, on top of that have to catch the stupid ideas from the upper-management and marketing without telling the upper-management that they have no idea what they are talking about, on top of getting something actually done in the project?

    Because often the level of tech competency has very little to do with the decision corridor that the project manager has, given everything else.