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Joined 26 days ago
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Cake day: August 18th, 2026

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  • ::: spoiler

    Most so called inventions and discoveries are things that others have already done or will do anyway but get no credit for.

    I’m under the impression that in most of those cases it is a matter of duplication of work, rather than plagiarism as is being discussed here.

    For this problem, the reward is also 1 million dollars. Surely you’re not claiming it’s ok for Sam Altman to take the prize over someone who did the majority of the work.

    I say this as someone who releases all their software under open source licenses which maximize the ability to share under the condition that attribution and the ability to further share/alter is preserved.

    There is such a thing as malicious sharing of information/software/academic works, e.g. the distribution of proprietary software tools, plagiarism, etc. Harmful sharing ultimately limits the sharing of other information such as original authorship and source code. :::

    tl;dr

    If the Clay Math Institute knew for certain that Sam Altman was lying about authorship, they wouldn’t want to give him the prize. That makes these actions deceptive and by principles of radical sharing of knowledge, morally wrong.

    I appreciate your perspective, thanks for the discussion.


  • Knowledge cannot be stollen. That concept is human fabrication. Knowledge is meant to be shared and once share is flow freely.

    Stealing knowledge in an academic context means using it without credit, aka plagiarism, much unlike the common definition for media.

    Attribution doesn’t restrict the flow of information, it simply gives credit where it is due. Refusing to attribute a work, strictly speaking, limits the flow of authorial information.

    As stated by the article, if sharing work may be punished by a bad actor people will selfishly restrict the flow of useful information in response to protect themselves from plagiarism. Doing something that makes academics choose between further advancement of their field and their own self interest is the principle malfeasance.






  • It’s not about voting for the lesser evil, but against the worse one. The way the United States is structured, which influences the way its people think about voting and vice versa, you are given only two options when voting for president:

    • Vote for the Democrat one
    • Vote for the Republican one

    But… you might think you’re clever and say “I should just vote for neither of them, nobody will stop me”. If your state has RCV then put your preferred candidate as a first pick and just place Democrat before Republican. Problem solved, no more strategic voting. Unfortunately, precious few states have adopted this for any elections, let alone the presidency.

    In states without RCV, there are 3 equivalent cases from your perspective:

    • Vote Republican: Increment their number by 1
      • Overall effect +1 for Republicans
    • Vote Democrat: Increment a threatening score by 1, thereby requiring Republicans to gain 1 more vote to win
      • Overall effect -1 for Republicans
    • Don’t vote or vote for a non threatening party/candidate
      • Overall effect 0 for Republicans

    Forgive me for assuming the reader wants to get Republicans (and Republican aligned Democrats) out of office. In this case, it is your responsibility to vote for the most threatening candidate. It’s a sick trick of game theory, which is why RCV is so important.








  • This kind seems like it’s basically what I said?

    My mistake, I associated what you said as

    (machine learning) is not the same as (“AI” (LLMs, presented as beings capable of thought))

    (aka LLMs aren’t ML) rather than

    machine learning is not the same as “AI” e.g. (LLMs, presented as beings capable of thought)

    (aka LLMs are ML, and when presented as a replacement for humans they are AI)

    Either way, I believe there is some value in comparing even simple decision making technology to the human brain. After all, we are capable of making those sorts of decisions as well. Tbf I may be biased by my internal mental model of coding, where I imagine describing a set of steps, relations, abstractions, etc to an entity with an extremely direct manner of interpretation.

    My personal moral of (seemingly malicious) language ambiguity is to avoid the term AI altogether.

    Thanks for the discussion!



  • machine learning is not the same as “AI” (LLMs, presented as beings capable of thought)

    (insert nerd face) LLMs are a kind of machine learning algorithm. AI is actually a superset of ML, in fact pretty much all computer programs count as AI. (a lot of old computer science departments were once called AI research departments in stead)

    The phrase “AI”, as promoted by tech bros, however, should be considered seperately to the correct definition: I agree that software is generally harmful when presented this way by its creators.