Technology

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.

Its a definition, but not an effective one in the sense that we can test and recognize it. Can we list all cognitive tasks a human can do? To avoid testing a probably infinite list, we should instead understand what are the basic cognitive abilities of humans that compose all other cognitive abilities we have, if thats even possible. Like the equivalent of a turing machine, but for human cognition. The Turing machine is based on a finite list of mechanisms and it is considered as the ultimate computer (in the classical sense of computing, but with potentially infinite memory). But we know too little about whether the limits of the turing machine are also limits of human cognition.

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.

The only text predictor I want in my life is T9

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.

Lol. We’re as far away from getting to AGI as we were before the whole LLM craze. It’s just glorified statistical text prediction, no matter how much data you throw at it, it will still just guess what’s the next most likely letter/token based on what’s before it, that can’t even get it’s facts straith without bullshitting.

This is correct, and I don't think many serious people disagree with it.

If we ever get it, it won’t be through LLMs.

Well... depends. LLMs alone, no, but the researchers who are working on solving the ARC AGI challenge, are using LLMs as a basis. The one which won this year is open source (all are if are eligible for winning the prize, and they need to run on the private data set), and was based on Mixtral. The "trick" is that they do more than that. All the attempts do extra compute at test time, so they can try to go beyond what their training data allows them to do "fine". The key for generality is trying to learn after you've been trained, to try to solve something that you've not been prepared for.

Even OpenAI's O1 and O3 do that, and so does the one that Google has released recently. They are still using heavily an LLM, but they do more.

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

I'm not sure if it's already proven or provable, but I think this is generally agreed. just deep learning will be able to fit a very complex curve/manifold/etc, but nothing more. It can't go beyond what was trained on. But the approaches for generalizing all seem to do more than that, doing search, or program synthesis, or whatever.

I just tried Google Gemini and it would not stop making shit up, it was really disappointing.

That's an Onion level of capitalism

The same way these capitalists evaluate human beings.

Guess we're never getting AGI then, there's no way they end up with that much profit before this whole AI bubble collapses and their value plummets.

Cars, and snake oil, and propaganda