Technology

it would be quite trope inversion if people sided with the ai overlord

I’m pretty sure the simplest way to look at is an LLM can only respond, not generate anything on its own without prompting. I wish humans were like that sometimes, especially a few in particular. I would think an AGI would be capable of independent thought, not requiring the prompt.

Roger Penrose wrote a whole book on the topic in 1989. https://www.goodreads.com/book/show/179744.The_Emperor_s_New_Mind

His points are well thought out and argued, but my essential takeaway is that a series of switches is not ever going to create a sentient being. The idea is absurd to me, but for the people that disagree? They have no proof, just a religious furver, a fanaticism. Simply stated, they want to believe.

All this AI of today is the AI of the 1980s, just with more transistors than we could fathom back then, but the ideas are the same. After the massive surge from our technology finally catching up with 40-60 year old concepts and algorithms, most everything has been just adding much more data, generalizing models, and other tweaks.

What is a problem is the complete lack of scalability and massive energy consumption. Are we supposed to be drying our clothes at a specific our of the night, and join smart grids to reduce peak air conditioning, to scorn bitcoin because it uses too much electricity, but for an AI that generates images of people with 6 fingers and other mangled appendages, that bullshit anything it doesn't know, for that we need to build nuclear power plants everywhere. It's sickening really.

So no AGI anytime soon, but I am sure Altman has defined it as anything that can make his net worth 1 billion or more, no matter what he has to say or do.

I hope someone will finally mathematically prove that it's impossible with current algorithms, so we can finally be done with this bullshiting.

They did! Here's a paper that proves basically that:

van Rooij, I., Guest, O., Adolfi, F. et al. Reclaiming AI as a Theoretical Tool for Cognitive Science. Comput Brain Behav 7, 616–636 (2024). https://doi.org/10.1007/s42113-024-00217-5

Basically it formalizes the proof that any black box algorithm that is trained on a finite universe of human outputs to prompts, and capable of taking in any finite input and puts out an output that seems plausibly human-like, is an NP-hard problem. And NP-hard problems of that scale are intractable, and can't be solved using the resources available in the universe, even with perfect/idealized algorithms that haven't yet been invented.

This isn't a proof that AI is impossible, just that the method to develop an AI will need more than just inferential learning from training data.

There are already a few papers about diminishing returns in LLM.

I mean, human intelligence is ultimately too "just" something.

And 10 years ago people would often refer to "Turing test" and imitation games in the sense of what is artificial intelligence and what is not.

My complaint to what's now called AI is that it's as similar to intelligence as skin cells grown in the form of a d*ck are similar to a real d*ck with its complexity. Or as a real-size toy building is similar to a real building.

But I disagree that this technology will not be present in a real AGI if it's achieved. I think that it will be.

The only text predictor I want in my life is T9

Oh yeah!? If I'm so dang smart why am I not generating 100 billion dollars in value?

So then how do we define natural general intelligence? I'd argue it's when something can do better than chance at solving a task without prior training data particular to that task. Like if a person plays tetris for the first time, maybe they don't do very well but they probably do better than a random set of button inputs.

Likewise with AGI - say you feed an LLM text about the rules of tetris but no button presses/actual game data and then hook it up to play the game. Will it do significantly better than chance? My guess is no but it would be interesting to try.

Any or every task?

So if you give a human and a system 10 tasks and the human completes 3 correctly, 5 incorrectly and 3 it failed to complete altogether... And then you give those 10 tasks to the software and it does 9 correctly and 1 it fails to complete, what does that mean. In general I'd say the tasks need to be defined, as I can give very many tasks to people right now that language models can solve that they can't, but language models to me aren't "AGI" in my opinion.

That's an Onion level of capitalism

The same way these capitalists evaluate human beings.