• 7 Posts
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Joined 1 year ago
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Cake day: June 12th, 2023

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  • Thorry84@feddit.nltoScience Memes@mander.xyz"Theory" of Evolution (SMBC)
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    11 days ago

    I like to think of it in this way: What we call dark matter isn’t the cause/source, but the discrepancies we’ve seen in our observations/data. So anybody who says dark matter doesn’t exist is plain wrong, the discrepancies are there plain as day. And it isn’t a single thing, it’s many discrepancies in a lot of data. Now the name is probably not as good, as it isn’t clear it’s actually matter and it isn’t dark but simply doesn’t interact with EM radiation. So we can’t “see” it directly, only indirectly. The name is so poor, it leads to a lot of miscommunications. But the fact is, the data doesn’t match up. So there has to be something there. And that’s data going back almost 100 years.

    Just like I said about gravity. There’s dark matter, the real thing that exists and we can “see”. And then there’s the theory of dark matter, the how and why, the thing we haven’t figured out yet.



  • Thorry84@feddit.nltoScience Memes@mander.xyz"Theory" of Evolution (SMBC)
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    11 days ago

    People always confuse multiple things.

    There is gravity, the actual effect we see every day all around us. Gravity is a real thing, it exists. Then there’s the law of gravity, this is a math formula you can use to predict the effect gravity has on things. There’s multiple variations of this one, think Newton and Einstein. For almost everything the Newton version works just fine. Then there’s the theory of gravity, this is our attempt to explain why gravity exists and why it does the things it does. This is the tricky one we don’t really have a grip on.

    By mixing these things it is often portrayed that “scientists” don’t know anything, they don’t even understand something as simple as gravity.



  • This is pretty dumb, machine learning algorithms (fuck off with calling it AI) are especially good at seeing signs of disease in data such as xrays, CT and MRI scans. It’s the one place they really help save time and prevent mistakes. And even if it’s just to flag shit for a second opinion by a doctor and not to replace the doctor, that’s still super useful. Pattern recognition is hard and these kinds of algorithms are very good at them if provided the right source data to work off.

    If only the media and big corps would stop claiming LLMs are general AI, then maybe people would stop using them for stuff it’s clearly not good at and not meant for.






  • Good advice, just to add to this:

    • Comments should be part of code review, having at least two pairs of eyes on comments is crucial. Something that’s obvious to one person maybe isn’t so obvious to another. Writing good comments is as hard or harder than writing good code, so having it checked for mistakes and quality is a must
    • Comments aren’t the actual documentation and aren’t a reason not to write documentation to go along with your code. Often I see larger projects where each class and function is documented in comments, but the big picture and the how and why of the overall structure is completely missing. Remember that in the real world you often have a lot of folk that need to understand how the code works, who aren’t programmers themselves. They can’t read the code or don’t have access to the code. Writing documentation is still important.
    • Please for the love of god when you change code, check if the comments need to be updated as well. Not just around the immediate area, but also the entire file/class and related files. I’ve worked on large codebases before with a high wtf factor and having the code do something different to or even opposite the comments is a nightmare. I’d rather have no comments than wrong comments.





  • Thorry84@feddit.nltoScience Memes@mander.xyzExploration
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    26 days ago

    Well no, if you think that I’ve failed to communicate it properly. Sorry for that. I mean the exact opposite.

    Say for example we have some unit of knowledge called T. The moon has in this hypothetical unit about 1000T of possible knowledge and humans know about 900T of things about the moon. In this case the oceans would have at least 1000000T of possibility knowledge and humans know about 800000T. We thus know much more about the oceans than we could even ever know about the moon.

    You might argue that we know 90% about the moon and only 80% about the oceans and thus know less about the oceans than the moon. But this fails on three parts:

    First of all, we can’t know what we don’t know. So whilst we might guess the moon has somewhere around 1000T of total knowledge, we can’t know this for sure. This means talking about percentages makes no sense. We can only say with some certainty there is orders of magnitudes more to learn about the oceans than there is about the moon.

    Second of all, we can estimate the total number of knowledge about the moon is a relatively low order of magnitude compared to the order of magnitude of total knowledge possible about the oceans. This means the percentage is meaningless as even relatively little knowledge leads to a high percentage.

    Third of all, knowledge isn’t linear. There is always low hanging fruit that can be learnt with little efforts and says a lot about what a thing is. Then as it is studied further, more details emerge which fill in the gaps. The gaps in knowledge get smaller and smaller, and the overall picture stays more and more the same. As I said we’ve studied the overall structure of the ocean and focused down where interesting stuff is.

    Thus comparing knowledge based on percentages makes little sense.

    These kinds of things are often used to justify things that aren’t grounded in reality. Such as the lost civilization. It’s in the same vain as something having a non zero chance of happening means it can happen. For example there is a non zero chance your atoms scatter within the next nanosecond. It’s theoretically possible but can’t happen in the real world.

    Hope this makes more sense to you.