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Cake day: June 12th, 2023

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  • Redacted@lemmy.worldtoAsklemmy@lemmy.mlMy First Post on Lemmy
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    21 days ago

    You’ll need to sign up for a new account on the other instance. I’d recommend you persevere with getting a lemmy.world one and use that as your main one. Keep the one you’ve got in case of an lemmy.world outage.

    Content is generally shared amongst the main instances so you’ll generally be able to see the same stuff wherever you create an account. Most of the Lemmy apps have multi-account functionality so you can add both.






  • Whilst it’s true solar is growing, it is not likely to be the silver bullet you make out.

    Another way to look at the source you linked is that despite the ongoing climate catastrophe the US is still planning to add 4% more fossil fuel sources to their grid next year.

    It also leaves out the fact that 84% of the current US power is generated by fossil fuels and that figure is not being reduced.

    The source is also very US-centric. If we take the IEA’s projections, only 25% of the world’s new energy will be from renewable sources in 2024.

    Then there’s the weird choice of counting battery storage as energy generation. At the end of 2022 half of the battery storage was being powered by fossil fuels so should probably be left out of any statistics.

    We need people to understand the true scale of the problem rather than generating more hopium. The energy companies have teams of people for that.



  • Guessing you might have been being rhetorical but I’ll give my take anyway… The stabbings were just the spark that ignighted it all.

    They do not have a common goal but they are united in thinking immigration levels are too high, despite it being a net benefit to the country. From what I’ve seen they generally fall into 3 groups:

    1. EDL/BNP/facists/racists who have been whipping up anti-immigration rhetoric forever. Emboldened by extreme language used by Farage, successive Conservative governments and no doubt rhetoric from the convicted felon across the pond. They blamed the stabbings (and every problem ever) on immigrants despite the fact it was carried out by an autistic kid born in this country.

    2. Brexit/Reform voters who see themselves as “more centrist” because they aren’t as far-right as #1. They blame years of austerity, worstening living conditions and growing class divide on the idea “the country is full”. They thought Brexit was supposed to solve this and are now protesting because it hasn’t. They claim it wasn’t done properly so voted Reform but got a more centrist government instead. They want to make their dissatisfaction heard but as far as I know do not condone the violence. They might now know the stabbings were not linked to migration but the issue has escalated beyond that incident now.

    3. Young people who are generally not very well informed on any of the issues and have been fed misinformation on their various social media channels encouraging them to join the protests. It’s the holidays, they are impressionable and angry. They think they’re part of some revolutionary movement or just drunk and up for some chaos so are out with their mates filming it all in their phones for the views. I imagine as they become more informed they will align with #1 or #2.









  • Yep my sentiment entirely.

    I had actually written a couple more paragraphs using weather models as an analogy akin to your quartz crystal example but deleted them to shorten my wall of text…

    We have built up models which can predict what might happen to particular weather patterns over the next few days to a fair degree of accuracy. However, to get a 100% conclusive model we’d have to have information about every molecule in the atmosphere, which is just not practical when we have a good enough models to have an idea what is going on.

    The same is true for any system of sufficient complexity.


  • This article, along with others covering the topic, seem to foster an air of mystery about machine learning which I find quite offputting.

    Known as generalization, this is one of the most fundamental ideas in machine learning—and its greatest puzzle. Models learn to do a task—spot faces, translate sentences, avoid pedestrians—by training with a specific set of examples. Yet they can generalize, learning to do that task with examples they have not seen before.

    Sounds a lot like Category Theory to me which is all about abstracting rules as far as possible to form associations between concepts. This would explain other phenomena discussed in the article.

    Like, why can they learn language? I think this is very mysterious.

    Potentially because language structures can be encoded as categories. Any possible concept including the whole of mathematics can be encoded as relationships between objects in Category Theory. For more info see this excellent video.

    He thinks there could be a hidden mathematical pattern in language that large language models somehow come to exploit: “Pure speculation but why not?”

    Sound familiar?

    models could seemingly fail to learn a task and then all of a sudden just get it, as if a lightbulb had switched on.

    Maybe there is a threshold probability of a positied association being correct and after enough iterations, the model flipped it to “true”.

    I’d prefer articles to discuss the underlying workings, even if speculative like the above, rather than perpetuating the “It’s magic, no one knows.” narrative. Too many people (especially here on Lemmy it has to be said) pick that up and run with it rather than thinking critically about the topic and formulating their own hypotheses.