It’s a classic BigTech marketing trick. They are the only one able to build “it” and it doesn’t matter if we like “it” or not because “it” is coming.

I believed in this BS for longer than I care to admit. I though “Oh yes, that’s progress” so of course it will come, it must come. It’s also very complex so nobody else but such large entities with so much resources can do it.

Then… you start to encounter more and more vaporware. Grandiose announcement and when you try the result you can’t help but be disappointed. You compare what was promised with the result, think it’s cool, kind of, shrug, and move on with your day. It happens again, and again. Sometimes you see something really impressive, you dig and realize it’s a partnership with a startup or a university doing the actual research. The more time passes, the more you realize that all BigTech do it, across technologies. You also realize that your artist friend did something just as cool and as open-source. Their version does not look polished but it works. You find a KickStarter about a product that is genuinely novel (say Oculus DK1) and has no link (initially) with BigTech…

You finally realize, year after year, you have been brain washed to believe only BigTech can do it. It’s false. It’s self serving BS to both prevent you from building and depend on them.

You can build, we can build and we can build better.

Can we build AGI? Maybe. Can they build AGI? They sure want us to believe it but they have lied through their teeth before so until they do deliver, they can NOT.

TL;DR: BigTech is not as powerful as they claim to be and they benefit from the hype, in this AI hype cycle and otherwise. They can’t be trusted.

You do all this on three pounds of wet meat powered by cornflakes.

The idea we’ll never recreate it through deliberate effort is absurd.

What you mean is, LLMs probably aren’t how we get there. Which is fair. “Spicy autocorrect” is a limited approach with occasionally spooky results. It does a bunch of stuff people insisted would never happen without AGI - but that’s how this always goes. The products of human intelligence have always shown some hard-to-define qualities which humans can eventually distinguish from our efforts to make a machine produce anything similar.

Just remember the distinction got narrower.

The paper: https://link.springer.com/content/pdf/10.1007/s42113-024-00217-5.pdf

This is a silly argument:

[…] But even if we give the AGI-engineer every advantage, every benefit of the doubt, there is no conceivable method of achieving what big tech companies promise.’

That’s because cognition, or the ability to observe, learn and gain new insight, is incredibly hard to replicate through AI on the scale that it occurs in the human brain. ‘If you have a conversation with someone, you might recall something you said fifteen minutes before. Or a year before. Or that someone else explained to you half your life ago. Any such knowledge might be crucial to advancing the conversation you’re having. People do that seamlessly’, explains van Rooij.

‘There will never be enough computing power to create AGI using machine learning that can do the same, because we’d run out of natural resources long before we’d even get close,’ Olivia Guest adds.

That’s as shortsighted as the “I think there is a world market for maybe five computers” quote, or the worry that NYC would be buried under mountains of horse poop before cars were invented. Maybe transformers aren’t the path to AGI, but there’s no reason to think we can’t achieve it in general unless you’re religious.

EDIT: From the paper:

The remainder of this paper will be an argument in ‘two acts’. In ACT 1: Releasing the Grip, we present a formalisation of the currently dominant approach to AI-as-engineering that claims that AGI is both inevitable and around the corner. We do this by introducing a thought experiment in which a fictive AI engineer, Dr. Ingenia, tries to construct an AGI under ideal conditions. For instance, Dr. Ingenia has perfect data, sampled from the true distribution, and they also have access to any conceivable ML method—including presently popular ‘deep learning’ based on artificial neural networks (ANNs) and any possible future methods—to train an algorithm (“an AI”). We then present a formal proof that the problem that Dr. Ingenia sets out to solve is intractable (formally, NP-hard; i.e. possible in principle but provably infeasible; see Section “Ingenia Theorem”). We also unpack how and why our proof is reconcilable with the apparent success of AI-as-engineering and show that the approach is a theoretical dead-end for cognitive science. In “ACT 2: Reclaiming the AI Vertex”, we explain how the original enthusiasm for using computers to understand the mind reflected many genuine benefits of AI for cognitive science, but also a fatal mistake. We conclude with ways in which ‘AI’ can be reclaimed for theory-building in cognitive science without falling into historical and present-day traps.

That’s a silly argument. It sets up a strawman and knocks it down. Just because you create a model and prove something in it, doesn’t mean it has any relationship to the real world.

Obviously those claims are overblown lol, AIs literally cannot think. They are currently LLMs. They are impressive, sure, but anyone knows the technology knows that this is NOT AGI, and it is entirely possible we will never get AGI. It’s also possible we will get AGI, but this ain’t it. lol

The problem is when your boss believes in hype and makes layoffs (already happenning)

To be honest I really think that an AI surprising human brain in many ways is a matter of time, but what people don’t tend to talk about is whether or not we are slowly approaching the limit what we can do with technology, because I already see tech progress slowing down in some areas.

Man I dunno if we’ve ever been here before. Nope.

It better not be inevitable…

[Loads bolter with religious intent]