ascense

@ascense@lemm.ee

• ⁨Comment⁩ on ⁨Uber Eats undercover: Delivering your food for $1.74 an hour⁩ ⁨⁨4⁩ ⁨days⁩ ago⁩:

  One issue I’ve heard is if a restaurant chooses not to use the service someone else can set up a page in their name without permission, and the platforms often won’t do anything to prevent it. Then confused delivery drivers start to show up, and customers complain to the restaurant about the markups/high pricing even when the restaurant is not actually involved at all.

  On top of all that, many people just use delivery apps to find local restaurants, so you lose a lot of visibility if you aren’t listed, but for that one you can argue it’s in fact paying for the service you get (i.e. marketing).

• ⁨Comment⁩ on ⁨We like music because our brains crave pattern recognition.⁩ ⁨⁨4⁩ ⁨days⁩ ago⁩:

  I strongly believe that our brains are fundamentally just prediction machines. We strive for a specific level of controlled novelty, but for the most part ‘understanding’, i.e. being able to predict, the world around us is the goal. We get boredom to push us beyond getting too comfortable and simply sitting in the already familiar, and one of the biggest pleasures in life is the ‘aha’ moment when understanding finally clicks in place and we feel we can predict something novel.

  I feel this is also why LLMs (ChatGPT etc.) can be so effective working with language, and why they occasionally seem to behave so humanlike – The fundamental mechanism is essentially the same if massively more limited. Animal brains continuously adapt to predict sensory input (and to an extent their own output), while LLMs learn to predict a sequence of text tokens during a restricted training period.

  It also seems to me the strongest example of this kind of prediction in animals is the noticing (and wariness) when something feels ‘off’ about the environment around us. We can easily sense specific kinds of small changes to our surroundings that signify potential danger, even in seemingly unpredictable natural environments. From an evolutionary perspective this also seems like the most immediately beneficial aspect of this kind of prediction. Interstingly, this kind of prediction seems to happen even on the level of individual neurons. As predictive capability improves, it also necessitates an increasingly deep ability to model the world around us, leading to deeper cognition.

• ⁨Comment⁩ on ⁨Wood smells like we should be able to eat it, but we can't.⁩ ⁨⁨5⁩ ⁨months⁩ ago⁩:

  This comes to mind: en.m.wikipedia.org/wiki/Bark_bread

• ⁨Comment⁩ on ⁨Interesting how artists don't make enough money from their creations, so our solution is to make certain information illegal to share, rather than give them a universal basic income.⁩ ⁨⁨1⁩ ⁨year⁩ ago⁩:

  This, and also working part time would become a lot more feasible. I would imagine there would be quite a bit of pressure to improve working conditions as well, which wouldn’t exactly be a bad thing. A lot more hours would be spent on things people consider meaningful, and bullshit jobs would have to be compensated appropriately, which to me feels like a win for society collectively.

  One caveat though is that for abolishing minimum wages to be safe the UBI has to be high enough to be actually livable, and would likely be a target of constant politicking. A model I’ve been thinking about would be to set the level of UBI as a percentage of GDP, distributed evenly across the population, which to me would feel fair but may have practical issues I don’t see. It would create a sense of everyone benefiting from collective success, which appeals to me.

• ⁨Comment⁩ on ⁨Microsoft Needs So Much Power to Train AI That It's Considering Small Nuclear Reactors⁩ ⁨⁨1⁩ ⁨year⁩ ago⁩:

  A corporation running a nuclear reactor to train AIs might just be the most cyberpunk news headline I’ve ever seen.