Nope, people are quite resilient. As long as it’s not a literal new born, the chance of survival isn’t THAT low. Once you get past 4 years and up, a human can manage quite well.
Also dying because no one takes care of you and you fail to aquire food and dying of a stroke/seizure are 2 very different things.
The 5 year old baby LLM can’t learn shit and lacks the ability to understand new information. You’re assuming that we and LLMs “learn” in the same way. Our brains can reason and remember information, detect new patterns and build on them. An LLM is quite literally incapable of learning a brand new pattern, let alone reason and build on it. Until we have an AI that can accept new information without being tolled what is and isn’t important to remember and how to work with that information, we’re not even a single step closer to AGI. Just because LLMs are impressive, doesn’t mean they posses any cognition. The only way AIs “learn” is by countless people constantly telling it what is and isn’t important or even correct. The second you remove that part, it stops working and turns to shit real quick. More “training” time isn’t going to solve the fact that without human input and human defined limits, it can’t do a single thing. AI cannot learn form it self without human input either, there are countless studies that show how it degrades, and it degrades quickly, like literally just one generation down the line is absolute trash.
Language models are literally incapable of reasoning beyond what is present in the dataset or the prompt. Try giving it a known riddle and change it so it becomes trivial, for example “With a boat, how can a man and a goat get across the river?”, despite it being a one step solution, it’ll still try to shove in the original answer and often enough not even solve it. Best part, if you then ask it to explain its reasoning (not tell it what it did wrong, that’s new information you provide, ask it why it did what it did), it’ll completely shit it self hallucinating more bullshit for the bullshit solution. There’s no evidence at all they have any cognitive capacity.
I even managed to break it once through normal conversation, something happened in my life that was unique enough for the dataset and thus was incomprehensible to the AI. It just wasn’t able to follow the events, no matter how many times I explained.
I don’t understand why people don’t go for something like ZorinOS or Nobara. Both work great out of the box with support for like everything.
Even if it runs locally (which I doubt, altho they are asking to be verified by third parties for security, so we’ll see, sounds promising so far), it’s still a tool that has the authority to do literally anything from a prompt. Once the device is unlocked, it’s literally unlocked, as in every piece of information about the owner is now at your finger tips.
So when Microsoft says it’ll constantly screen shot your windows machine and then use AI on those screen shots, we all lose our collective minds, but when Apple unveils their system that’s fucking OS wide apparently, where the AI can literally see and interact with EVERYTHING, suddenly it’s worth celebrating?
Like don’t get me wrong, both are horrible, but what’s up with the double standard? It’s like apple can do no wrong, despite the fact that they’ve been doing nothing but wrong for almost a decade now.
The amount of corporate speak makes me sick. Especially the mix of buzzwords being mixed with shit like “KERNEL PROCESS”, shit’s cursed.
Sounds like the kind of work my analyst does. I guess he’s technically part of the development team, so sure??? Our 3 client mediators are totally taking over. Also pretty sure we’re the only IT department that even has such a thing. The only other person in our IT branch to be mainly doing calls and such is the top head of IT, every other IT boss still has a lot of technical work around their necks. So at least at my job “close to 100%” is an absolute farcry.
It’s a very similar story at my girlfriend’s work place. Except they don’t even have analysts.
Honestly, I have trouble imagining such a world. Hmmm… The more I think about it, the more it just kinda sounds like high school… Sends shivers down my spine.
As a developer I have to say OH hell nah. If I had to compare the issue to something more layman, I’d compare it to tesla’s self driving. If I have to watch it the entire time it does its thing because there’s an almost certain chance it’ll mess something up CATASTROPHICALLY due to the fact that it literally lacks the ability to understand, than I might as well just do it my self. It rarely saves time and only in dumb cases, that should have been automated in other ways a long time ago.
Not saying it’s not a very handy tool occasionally, just that it can’t come up with solutions to problems on its own, which is like 75% of my work. And it can’t do this due to a fundamental limitation in how learning models work, no amount of training will fix this.
If jobs consisted of only attending video calls and wiring emails, the world would be a very different place.
You can think of the brain as a set of modules, but sensors and the ability to adhere to a predefined grammar aren’t what define AGI if you ask me. We’re missing the most important module. AGI requires cognition, the ability to acquire knowledge and understanding. Such an ability would make larger language models completely redundant as it could just learn langue or even come up with one all on its own, like kids in isolation for example.
What I was trying to point out is that “neural networks” don’t actually learn in the way we do, using the world “learn” is a bit misleading, because it implies cognition. A neural network in the computer science sense is just a bunch of random operations in sequence. In goes a number, out goes a number. We then collect a bunch of input output pairs, the dataset, and semi randomly adjust these operations until they happen to somewhat match this collection. The reasoning is done by the humans assembling the input output pairs. That step is implicitly skipped for the AI. It doesn’t know why they belong together and it isn’t allowed to reason about why, because the second it spits out something else, that is an error and this whole process breaks. That’s why LLMs hallucinate with perfect confidence and why they’ll never gain cognition, because the second you remove the human assembling the dataset, you’re quite literally left with nothing but semi random numbers, and that’s why they degrade so fast when learning from themselves.
This technology is very impressive and quite useful, and demonstrates how powerful of a tool language alone is, but it doesn’t get us any closer to AGI.