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Cake day: June 30th, 2023

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  • Were probably? That’s a giant understatement and you know it.

    Ai will save billions of lives and improve the living standard for everyone on the planet, it’ll be just like mobile phones where the biggest benefits come to the poorest communities - tech haters often ignore this reality, millions of children in Africa, Asia, etc were only able to get access to education through mobile infrastructure.

    The internet has given everyone access to huge amounts of education resources and it’s only increased as they technology matures - current LLMs are amazing for language learners and for people who need things like English articles explained in their own language, I just asked chatgpt to explain the code I’m working on in Tagalog and it did it without hesitation (I can’t speak for the accuracy personally but looks legit) it even translated variable names but not function calls.

    And this before we’ve scratched the surface of it’s utility, I’ll tell you one thing if you ever say to your grandkids ‘o I was against ai when it came out’ they’ll look at up like you’d look at someone who said they didn’t think math would catch on or that iron would never be as popular as bronze.



  • Yeah the amount of good ai can do for the world is staggering, even just giving a speed boost and quality improvement to open source Devs will unlock a lot of new potential.

    The problem is people in a certain age bracket often fear change because they feel they’ve put effort into learning how things work and if things change then all that effort will be worthless.

    It doesn’t really matter though, gangs of idiots literally smashed the prototype looms when they were demonstrated because despite the cost of cloth being one of the major factors in poverty at the time a handful of people took it on themselves to fight to maintain the status quo – of course we know how it turned out, the same that it always does…

    Areas that resisted technological and social growth stagnated and got displaced by those which welcomed it



  • Yeah it’s frightening that there’s literally a cult developing of home schooled zealots brought up on the wackiest conspiracies on YouTube. I was friends with a guy that was brought up by intese Jehovah’s witnesses and even though he stopped participating and believing it took a long time for him to understand the stuff they’d taught him was crazy - like he was shocked that I believed in evolution because as far as he knew it was long since disproven.

    The real problem was critical reasoning skills and learning skills, he had no idea how to verify information or seek out out from reasonable sources. I think we’re going to get a lot of people in the same boat, except a lot more violent and terrified of the world.




  • You’re confusing a few things, firstly you mean current gen large language models not AI, ai is often used to evolve novel strategies from scratch without any human training data - chess ai don’t have to study human games for example, in fact grand master chess players have been studying what the ai learned and discovered things that humans hadn’t realised even after a thousand years of the games popularity.

    Secondly that’s not really how LLMs work either, they’re much more mathematically complex and very much create their own ideas on a similar process we do of assembling concepts then structure then word choice.

    It’s fine you not understanding how this works but the problem is that journalists don’t either even when they’re writing about it - this puts us in a situation where they’re making childishly naive but of course clickbait titles claiming there’s some relevance to the output when the tool is used very wrong so you rightly point out it’s stupid and that’s not how llms work but then we get this overstep where it’s being refuted with an equal amount of magical thinking and false conclusions made.

    An LLM can make novelty and originality but it can’t create with intent, it doesn’t use reason or structure - there are AI that do these things to limited degrees and of course the NSA one that they spent all that money on and no one is allowed to talk about. Using chat GPT play a silly fantasy won’t tell us anything about how they’ll think so this article is entirely worthless







  • If you ask it to make up nonsense and it does it then you can’t get angry lol. I normally use it to help analyse code or write sections of code, sometimes to teach me how certain functions or principles work - it’s incredibly good at that, I do need to verify it’s doing the right thing but I do that with my code too and I’m not always right either.

    As a research tool it’s great at taking a basic dumb description and pointing me to the right things to look for, especially for things with a lot of technical terms and obscure areas.

    And yes they can occasionally make mistakes or invent things but if you ask properly and verify what you’re told then it’s pretty reliable, far more so than a lot of humans I know.


  • Why would I rebut that? I’m simply arguing that they don’t need to be ‘intelligent’ to accurately determine the colour of the sky and that if you expect an intelligence to know the colour of the sky without ever seeing it then you’re being absurd.

    The way the comment I responded to was written makes no sense to reality and I addressed that.

    Again as I said in other comments you’re arguing that an LLM is not will smith in I Robot and or Scarlett Johansson playing the role of a usb stick but that’s not what anyone sane is suggesting.

    A fork isn’t great for eating soup, neither is a knife required but that doesn’t mean they’re not incredibly useful eating utensils.

    Try thinking of an LLM as a type of NLP or natural language processing tool which allows computers to use normal human text as input to perform a range of tasks. It’s hugely useful and unlocks a vast amount of potential but it’s not going to slap anyone for joking about it’s wife.


  • People do that too, actually we do it a lot more than we realise. Studies of memory for example have shown we create details that we expect to be there to fill in blanks and that we convince ourselves we remember them even when presented with evidence that refutes it.

    A lot of the newer implementations use more complex methods of fact verification, it’s not easy to explain but essentially it comes down to the weight you give different layers. GPT 5 is already training and likely to be out around October but even before that we’re seeing pipelines using LLM to code task based processes - an LLM is bad at chess but could easily install stockfish in a VM and beat you every time.


  • That’s only true on a very basic level, I understand that Turings maths is complex and unintuitive even more so than calculus but it’s a very established fact that relatively simple mathematical operations can have emergent properties when they interact to have far more complexity than initially expected.

    The same way the giraffe gets its spots the same way all the hardware of our brain is built, a strand of code is converted into physical structures that interact and result in more complex behaviours - the actual reality is just math, and that math is almost entirely just probability when you get down to it. We’re all just next word guessing machines.

    We don’t guess words like a Markov chain instead use a rather complex token system in our brain which then gets converted to words, LLMs do this too - that’s how they can learn about a subject in one language then explain it in another.

    Calling an LLM predictive text is a fundamental misunderstanding of reality, it’s somewhat true on a technical level but only when you understand that predicting the next word can be a hugely complex operation which is the fundamental math behind all human thought also.

    Plus they’re not really just predicting one word ahead anymore, they do structured generation much like how image generators do - first they get the higher level principles to a valid state then propagate down into structure and form before making word and grammar choices. You can manually change values in the different layers and see the output change, exploring the latent space like this makes it clear that it’s not simply guessing the next word but guessing the next word which will best fit into a required structure to express a desired point - I don’t know how other people are coming up with sentences but that feels a lot like what I do



  • I use LLMs to create things no human has likely ever said and it’s great at it, for example

    ‘while juggling chainsaws atop a unicycle made of marshmallows, I pondered the existential implications of the colour blue on a pineapples dream of becoming a unicorn’

    When I ask it to do the same using neologisms the output is even better, one of the words was exquimodal which I then asked for it to invent an etymology and it came up with one that combined excuistus and modial to define it as something beyond traditional measures which fits perfectly into the sentence it created.

    You can’t ask a parrot to invent words with meaning and use them in context, that’s a step beyond repetition - of course it’s not full dynamic self aware reasoning but it’s certainly not being a parrot


  • But also the people who seem to think we need a magic soul to perform useful work is way way too high.

    The main problem is Idiots seem to have watched one too many movies about robots with souls and gotten confused between real life and fantasy - especially shitty journalists way out their depth.

    This big gotcha ‘they don’t live upto the hype’ is 100% people who heard ‘ai’ and thought of bad Will Smith movies. LLMs absolutely live upto the actual sensible things people hoped and have exceeded those expectations, they’re also incredibly good at a huge range of very useful tasks which have traditionally been considered as requiring intelligence but they’re not magically able everything, of course they’re not that’s not how anyone actually involved in anything said they would work or expected them to work.