• 0 Posts
  • 32 Comments
Joined 2 years ago
cake
Cake day: July 31st, 2023

help-circle
  • Clearly the author doesn’t understand how capitalism works. If Apple can pick you up by the neck, turn you upside down, and shake whatever extra money it can from you then it absolutely will do so.

    The problem is that one indie developer doesn’t have any power over Apple… so they can go fuck themselves. The developer is granted the opportunity to grovel at the feet of their betters (richers) and pray that they are allowed to keep enough of their own crop to survive the winter. If they don’t survive… then some other dev will probably jump at the chance to take part in the “free market” and demonstrate their worth.




  • That was an example of a situation where time zones make sense. Any time it is important where the sun is in the sky, the time that it occurs will differ depending on where you are in the world. When is lunch break? When do backups run? When can you see the eclipse? If we weren’t in an interconnected world, it wouldn’t matter much but we need some convention to communicate information that is dependent on where the sun is, as that very often dictates human activity.

    It seems like a universal time makes sense but I can’t think of a way to get around the fact that activity will vary according to timezones anyway.


  • theparadox@lemmy.worldtoAsklemmy@lemmy.mlWhat hills are you dying on?
    link
    fedilink
    English
    arrow-up
    4
    ·
    edit-2
    2 months ago

    And there are no other external factors that could possibly influence their compensation besides their objective “worth” to the hiring organization?

    Edit: To clarify, might personal bias from the employer lead to a higher compensation? If two CEOs are interviewed and one went to the same college as several members of the board, or if several members of the board know one personally, but the known CEO isn’t as accomplished… is it possible that the CEO benefitting from bias is going be hired? Will the benefitting CEO receive a lower compensation, higher compensation, or the same compensation?

    Is it possible for a CEO to lie about their ability and get hired under false pretenses? Is it possible for a CEO to be hired for political or “public image” reasons rather than talent/productivity reasons? Are these reflected in their compensation?


  • I think the word “learning”, and even “training”, is an approximation from a human perspective. MLs “learn” by adjusting parameters when processing data. At least as far as I know, the base algorithm and hyperparameters for the model are set in stone.

    The base algorithm for “living” things is basically only limited by chemistry/physics and evolution. I doubt anyone could create an algorithm that advanced any time soon. We don’t even understand the brain or physics at the quantum level that well. Hell, we are using ML to create new molecules because we don’t understand it well.




  • theparadox@lemmy.worldtoAsklemmy@lemmy.mlWhat hills are you dying on?
    link
    fedilink
    English
    arrow-up
    15
    arrow-down
    2
    ·
    2 months ago

    We should stop using time zones

    Check this out. I’m a business with at least one office in every US state. You want to know when my New York office opens so you can come by. Instead of seeing “Offices are open 9 AM to 5 PM” You now need to check every office… by state… by city? Time zones would be helpful even if we all used GMT, so that you could easily determine which time zone a business is in to set a reasonable time to be open.

    DST can fuck off though.



  • I think you’re either being a little dismissive of the potential complexity of the “thinking” capability of LLMs or at least a little generous if not mystical in your imagination of what the purely physical electrical signals in our heads are actually doing to learn how to interpret all these little shapes we see on screens.

    I don’t think I’m doing either of those things. I respect the scale and speed of the models and I am well aware that I’m little more than a machine made of meat.

    Babies start out mimicking. The thing is, they learn.

    Humans learn so much more before they start communicating. They start learning reason, logic, etc as they develop their vocabulary.

    The difference is that, as I understand it, these models are often “trained” on very, very large sets of data. They have built a massive network of the way words are used in communication - likely built from more texts than a human could process in several lifetimes. They come out the gate with an enormous vocabulary and understanding of how to mimic, replicate it’s use. If they had been trained on just as much data, but data unrelated to communication, would you still think it capable of reasoning without the ability to “sound” human? They have the “vocabulary” and references to mimic a deep understanding but because we lack the ability to understand the final algorithm it seems like an enormous leap to presume actual reasoning is taking place.

    Frankly, I see no reason for models like LLMs at this stage. I’m fine putting the breaks on this shit - even if we disagree on the reasons why. ML can and has been employed to achieve far more practical goals. Use it alongside humans for a while until it is verifiably more reliable at some task - recognizing cancer in imaging or generating molecules likely of achieving a desired goal. LLMs are just a lazy shortcut to look impressive and sell investors on the technology.

    Maybe I am failing to see reality - maybe I don’t understand the latest “AI” well enough to give my two cents. That’s fine. I just think it’s being hyped because these companies desperately need VC money to stay afloat.

    It works because humans have an insatiable desire to see agency everywhere they look. Spirits, monsters, ghosts, gods, and now “AI.”


  • Yes, both systems - the human brain and an LLM - assimilate and organize human written languages in order to use it for communication. An LLM is very little else beyond this. It is then given rules (using those written languages) and then designed to create more related words when given input. I just don’t find it convincing that an ML algorithm designed explicitly to mimic human written communication in response to given input “understands” anything. No matter *how convincingly" an algorithm might reproduce a human voice - perfectly matching intonation and inflexion when given text to read - if I knew it was an algorithm designed to do it as convincingly as possible I wouldn’t say it was capable of the feeling it is able to express.

    The only thing in favor of sentience is that the ML algorithms modify themselves and end up being a black box - so complex with no way to represent them that they are impossible for humans to comprehend. Could it somehow have achieved sentience? Technically, yes, because we don’t understand how they work. We are just meat machines, after all.



  • I replied to the following statement:

    I could look up my dad’s name and all I get are articles about a serial killer who just happened to have the same name

    I countered this dismissal by quoting the article, which explains that it was more than just a coincidental name mix up.

    You response is not really relevant to my response, unless you are assuming I’m arguing for one side or the other. I’m just informing someone who dismissed the article’s headline using an explanation that demonstrated that they didn’t bother to read the article.

    Nothing is wrong with the tech (except it doesn’t seem very useful when you firmly know what it can’t do), but everything is wrong with that tech being called artificial intelligence.

    If the owners of the technology call it artificial intelligence and hype or sell it as a potential replacement for intelligent human decision making then it should be absolutely be judged on those grounds.






  • the same process

    It doesn’t necessarily involve the middle man, who is ultimately the bigger fish that enshittifiers are looking to land. I think that’s relevant. Enshittification’s process involves capturing both a “retail” user base and a business user base and then squeezing both.

    Edit. Enshittification is layered and more specific to industries and markets that are not inherently profitable. It starts with seed money being burned for that initial user base and fucks over everyone up and down the chain because the business is not really profitable otherwise. Skimp/shrinkflation is more about squeezing more profit than you are already making.


  • I’ve see it used a lot recently to describe the general degradation of quality in service of increasing profits. I think technically, it is not enshittification. Below is my general definition of the process enshittification describes. Repost from another comment.

    1. Attract users/customers with high quality services/products to create a captive/dependent user base.
    2. Attract business customers (ex. advertisers or businesses that can benefit from access to the user base in some way) by offering them high value services by fucking over your captive user base create a captive/dependent busiess customer base.
    3. Fuck over your captive business customers to increase your own profit.

    A word that includes the word “shit” in it has a very nice ring to it when describing things getting generally shittier in favor of profit. I suppose language can evolve rapidly and things mean what people believe them to mean.

    Edit: As per Wikipedia’s Shrinkflation Entry:

    Skimpflation involves a reformulation or other reduction in quality.

    I see skimpflation as a form of shrinkflation. The idea is still that the price stays the same but to try and hide the cost increase from the customer they give you less. I guess fewer strawberries per “smoothie” is even more subtle than fewer ounces of the original “smoothie” formula per bottle.