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Cake day: June 16th, 2023

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  • paying a peasant to work

    Peasants (serfs) were not paid. They were bound to the land they worked, and were given a fraction of the harvest they produced. The rest was property of the Lord who’s title controlled the land.

    There was a (very small) artisan class where the concept of payment existed, though often it was payment-in-kind - smith the plow for my oxen and I’ll give you some food after the harvest. Money was rarely encountered for the vast majority of people.


  • Explaining what happens in a neural net is trivial. All they do is approximate (generally) nonlinear functions with a long series of multiplications and some rectification operations.

    That isn’t the hard part, you can track all of the math at each step.

    The hard part is stating a simple explanation for the semantic meaning of each operation.

    When a human solves a problem, we like to think that it occurs in discrete steps with simple goals: “First I will draw a diagram and put in the known information, then I will write the governing equations, then simplify them for the physics of the problem”, and so on.

    Neural nets don’t appear to solve problems that way, each atomic operation does not have that semantic meaning. That is the root of all the reporting about how they are such ‘black boxes’ and researchers ‘don’t understand’ how they work.


    1. Hans has admitted to cheating in the past
    2. Hans played a near-perfect game as black against the best player in the world who hadn’t lost as white in years
    3. Hans made some suspiciously good moves quickly, without much time passing
    4. Magnus played a very rare opening that Hans was somehow able to perfectly respond to without skipping a beat

    From these, many people think he cheated. The vibrating butt plug is unlikely, but what is more likely is that Magnus’ prep got leaked and Hans was able to hyper-prepare for a specific line of play.




  • Interesting how you ignore how the US did not recognize the goverment installed by the people of korea (PRK)

    The brief existence of the PRK has essentially no bearing on the civil war. It existed less than a year, and was dismantled in both the South and the North by the actions of the US and Soviet Union.

    Neither power cared to entertain what the people of Korea wanted in the Post-War period.

    I wonder what half-truth or outright lie y’all will respond with next to paint the US and SK as Satan next to the Angelic Soviet Union and DPRK.

    No power were the ‘good guys’. None had the moral high ground. All deserve blame for what happened. The history of the period is one of tragedy and ambition.

    None of that changes the fact that North Korea, backed by the Soviets and later China, started the shooting war by invading the South.










  • The fundamental issue with declining populations - fundamental as in regardless of the economic system of the country - is decreasing standard of living.

    The very simple metric is productivity-adjusted hours worked per person. This invariably falls in cases where overall population is declining, because populations age as they decline, and older people work less (retirement) than younger ones.

    As this metric falls, the country’s economy basically just produces less stuff per-person than it did in the past. This makes everyone effectively poorer.

    In extreme cases, there can also be issues with availability of services. E.g. healthcare: Each doctor/nurse/caregiver can only effectively attend to so many patients and this number is difficult to increase with technology.



  • TheChurn@kbin.socialtoComic Strips@lemmy.worldGood news!
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    1 year ago

    I’m not sure how the other poster interpreted this comic to be anything else.

    It is an expansion on a Stephen Jay Gould quote:

    “I am, somehow, less interested in the weight and convolutions of Einstein’s brain than in the near certainty that people of equal talent have lived and died in cotton fields and sweatshops”



  • In the language of classical probability theory: the models learn the probability distribution of words in language from their training data, and then approximate this distribution using their parameters and network structure.

    When given a prompt, they then calculate the conditional probabilities of the next word, given the words they have already seen, and sample from that space.

    It is a rather simple idea, all of the complexity comes from trying to give the high-dimensional vector operations (that it is doing to calculate conditional probabilities) a human meaning.