[silveraxe93]

Is it actually unbelievable?

It's basically what every major AI lab head is saying from the start. It's the peanut gallery that keeps saying they are lying to get funding.

[JeremyNT]

Even as a layman and AI skeptic, to me this entirely matches my expectations, and something like this seemed like it was basically inevitable as of the first demos of video rendering responding to user input (a year ago? maybe?).

Not to detract from what has been done here in any way, but it all seems entirely consistent with the types of progress we have seen.

It's also no surprise to me that it's from Google, who I suspect is better situated than any of its AI competitors, even if it is sometimes slow to show progress publicly.

[westbrookt]

https://worldmodels.github.io/

I think this was the first mention of world models I've seen circa 2018.

This is based on VAEs though.

[kranke155]

Google seems to have had the keys to changing the world years ago and decided not to.

Hard to fault them as the process towards ASI now appears to be runaway and uncontrollable.

[glenstein]

>It's basically what every major AI lab head is saying from the start.

I suppose it depends what you count as "the start". The idea of AI as a real research project has been around since at least the 1950s. And I'm not a programmer or computer scientist, but I'm a philosophy nerd and I know debates about what computers can or can't do started around then. One side of the debate was that it awaited new conceptual and architectural breakthroughs.

I also think you can look at, say, Ted Talks on the topic, with guys like Jeff Hawkins presenting the problem as one of searching for conceptual breakthroughs, and I think similar ideas of such a search have been at the center of Douglas Hofstadter's career.

I think in all those cases, they would have treated "more is different" like an absence of nuance, because there was supposed to be a puzzle to solve (and in a sense there is, and there has been, in terms of vector space and back propagation and so on, but it wasn't necessarily clear that physics could "pop out" emergently from such a foundation).

[jonas21]

When they say "the start", I think they mean the start of the current LLM era (circa 2017). The main story of this time has been a rejection of the idea that major conceptual breakthroughs and complex architectures are needed to achieve intelligence. Instead, it's better to focus on simple, general-purpose methods that can scale to massive amounts of data and compute (i.e. the Bitter Lesson [1]).

[1] http://www.incompleteideas.net/IncIdeas/BitterLesson.html

[satvikpendem]

The start of deep neural networks, ie AlexNet

[ivape]

It's akin to us sending a rocket to space and immediately discovering a wormhole. Sure, there's a lot of science about what's out there, but to discover all this in our first few trips to orbit ...

[pantalaimon]

Joscha Bach postulates that what we call consciousness must be something rather simple, an emergent property present in all sufficiently complex biological organisms.

We don't inherit any software, so cognitive function must bootstrap itself from it's underlying structure alone.

https://media.ccc.de/v/38c3-self-models-of-loving-grace

[silveraxe93]

Lemme start by saying this is objectively amazing. But I just really wouldn't call it a breakthrough.

We had one breakthrough a couple of years ago with GPT-3, where we found that neural networks / transformers + scale does wonders. Everything else has been a smooth continuous improvement. Compare today's announcement to Genie-2[1] release less than 1 year ago.

The speed is insane, but not surprising if you put in context on how fast AI is advancing. Again, nothing _new_. Just absurdly fast continuous progress.

[1] - https://deepmind.google/discover/blog/genie-2-a-large-scale-...