> then LLMs in their current form are not going to lead to AGI.
It always seemed to me a wild leap to assume that LLMs in their current form would lead to AGI. I never understood the argument.
It always seemed to me a wild leap to assume that LLMs in their current form would lead to AGI. I never understood the argument.
I suspect it is an intentional result of deceptive marketing. I can easily imagine an alternative universe where different terminology was used instead of "AI" without sci-fi comparisons and barely anyone would care about the tech or bother to fund it.
I mean, certainly people like Sam Altman was pushing it hard, so it’s easy to understand how an outside observer would be confused.
But it also feels like a lot of VCs and AI companies have staked several hundreds of billions of dollars on that bet, and I’m still… I just don’t see why the inside players—that should (and probably do!) have more knowledge than me—see. Why are they dumping so much money into this bet?
The market for LLMs doesn’t seem to support the investment, so it feels like they must be trying to win a “first to AGI” race.
Dunno, maybe the upside of the pretty unlikely scenario is enough to justify the risk?
“A person is smart. People are dumb, panicky dangerous animals and you know it.”
If AGI wants to hit human level intelligence, I think it’s got a long way to go. But if it’s aiming for our collective intelligence, maybe it’s pretty close after all…
It is still interesting tech. I wish it were being used more for search and compression.
Sam Altman is a very good hype man. I don’t think anyone on the inside genuinely thinks LLMs will lead to AGI. Ed Zitron has been looking at the costs vs the revenue in his newsletter and podcast and he’s got me convinced that the whole field is a house of cards financially. I already considered it much overblown, but it’s actually one of the biggest financial cons of our time, like NFTs but with actual utility.
If you find yourself agreeing, I highly recommend subscribing to his newsletter.
They only need to last until the exit (potentially next round).
> The market for LLMs doesn’t seem to support the investment
i.e. it doesn't matter as long as they find someone else to dump it to (for profit).
So yep, a lot of time, they bet on trends. Cryptocurrencies, NFTs, several waves of AI. The question is just the acquisition or IPO price.
I don't doubt that some VCs genuinely bought into the AGI argument, but let's be frank, it wasn't hard to make that leap in 2023. It was (and is) some mind-blowing, magical tech, seemingly capable of far more than common sense would dictate. When intuition fails, we revert to beliefs, and the AGI church was handing out brochures...
It...does seem hard to make that leap to me. I mean, again, to a casual and uncritical outside observer who is just listening to and (in my mind naively) trusting someone like Sam Altman, then it's easy, sure.
But I think for those thinking critically about it... it was just as unjustified a leap in 2023 as it is today. I guess maybe you're right, and I'm just really overestimating the number of people that were thinking critically vs uncritically about it.
I mean, see also, AR/VR/Metaverse. My suspicion is that, for the like of Google and Facebook, they have _so much money_ that the risk of being wrong on LLMs exceeds the risk of wasting a few hundred billion on LLMs. Even if Google et al don’t really think there’s anything much to LLMs, it’s arguably rational for them to pump the money in, in case they’re wrong.
That said, obviously this only works if you’re Google or similar, and you can take this line of reasoning too far (see Softbank).
People were declaring ELIZA was intelligent after interacting with it and ELIZA is barely a page of code.
In truth, basically everything in reality settles towards an equilibrium. There is no inevitable ultraviolet catastrophe, free energy machine, Malthusian collapse. Moore's law had a good run, but frequencies stopped improving twenty years ago and performance gains are increasingly specific and expensive. My car also accelerates consistently through many orders of magnitude, until it doesn't. If throwing more mass at the problem could create general superintelligence and do so economically to such strong advantages, then why haven't biological neural networks, which are vastly more capable and efficient neuron-for-neuron than our LLMs, already evolved to do so?
"Man selling LLMs says LLMs will soon take over the world, and YOU too can be raptured into the post-AI paradise if you buy into them! Non-believers will be obsolete!" No, he hasn't studied enough neuroscience nor philosophy to even be able to comment on how human intelligence works, but he's convinced a lot of rich people to give him more money than you can even imagine, and money is power is food for apes is fancy houses on TV, so that must mean he's qualified/shall deliver us, see...
That’s what we put in our 2017 paper.
It does mean that there is a simple way to keep building smarter LLMs.
I have never seen a clear definition of what AGI is and what it means to achieve it
Granted that latter delta is much harder to measure, but history has shown repeatedly that that delta is always orders of magnitude bigger than we think it is when $GOAL=AGI.
In nutshell: https://xkcd.com/605/
Grug version: Man sees exponential curve. Man assumes it continues forever. Man says "we went from not flying to flying in few days, in few years we will spread in observable universe. "
That may happen in 200 years. Geologically as you zoom out, the difference is negligible.
This is way way less than the observable universe.
(I've seen this 'enough money ought to surmount any barrier' take a few times, usually to reject the idea that we might not find any path to AGI in the near future.)
The delicious irony is that we know how to solve climate change.
We’re simply not doing enough about it.
Only a few edge cases remain.
Same for dark matter. There are a few hypothesis, but all in all they are pretty simplistic cases.
We know the particles, the forces, there is not really “new physics” to be discovered here.
All the interactions anybody can encounter in their lives is fully understood.
For example John says "Please ask Jane to buy me an ice-cream" and the AI might be able to do that. If she doesn't, John can ask it to coerse her.
I think what they should be saying is - from a software stack standpoint, current tech unlocks AGI if it can be sped up significantly. So what we’re really waiting for are more software and hardware breakthroughs that make their performance many orders of magnitude quicker.
Consider also that any advanced language model already surpasses individual human knowledge, since each model compresses and synthesizes the collective insights, wisdom, and information of human civilization.
Now imagine a frontier model with agency, capable of deliberate, reflective thought: if it could spend an hour thinking, but that hour feels instantaneous to us, it would essentially match or exceed the productivity and capability of a human expert using a computer. At that point, the line between current AI capabilities and what we term AGI becomes indistinguishable.
In other words: deeper reflection combined with computational speed means we’re already experiencing AGI-level performance—even if we haven’t fully acknowledged or appreciated it yet.