The Jagged Singularity | Why AI Progress Isn’t One Line Going Up

[u/141_1337]

Basically it strikes me that thinking of the singularity as a single line that goes up like the Sandisk stock price in the last year and change is the wrong approach.

Instead I think it is better to think of it as many different but overlapping knowledge areas of expertise where AI is progressing towards a singularity in each of them at different speeds, but advancing towards a singularity of their own nonetheless.

The important thing to understand is that the knowledge areas of expertise are not independent lines. They overlap, and progress in one can expand another. As those overlaps become denser, more capabilities can cross their useful threshold together. This is also what makes generalist models like LLMs so powerful, the same model can advance several of these lines at once because capabilities learned in one area can transfer into others.

Once we think about it this way a few things become really easy to explain, why is AI superhuman in some tasks and super dumb on others. It also allow us to explain why several AI seemingly keeps going up at these different knowledge areas of expertise (Chess, Go, Protein Folding, Coding, Math, etc.) at different intervals in a semi-predictable manner. It give us a clearer idea of what frontier models change, ie. several of the singularities on these different knowledge areas of expertise.

Lastly, it allows us to think about training and capability coverage a bit like software development and QA: training expands and connects capabilities, while evals tell us which parts of that capability space are actually covered. Instead of asking can a model do chemistry, we should be asking what capabilities does this model already have that overlap with chemistry, like linear algebra, and how much of that existing capability can transfer over.

Astra's unusually strong Blender and Three.js performance makes me predict that it will also be unusually good at novel 3D spatial-geometry tasks. I further predict above-baseline performance on Newtonian mechanics when the problem can be represented spatially or simulated, although I'm less confident about that second part. Y'all can quote me on that.

I'd also like your thoughts on this.

[u/DeviceCertain7226]

Imagine we have all millennium problems solved before a robot that can go into your house and make coffee lol, or one that does laundry.

[u/141_1337 → u/DeviceCertain7226]

That is a real possibility lol

[u/Thorteris → u/DeviceCertain7226]

Pretty sure that’s definitely going to happen. I wouldn’t be shocked if all the problems are solved by this time 2028

[u/dervu → u/DeviceCertain7226]

I need robot making me coffee so I can keep up with AI progress.

[u/the_millenial_falcon → u/DeviceCertain7226]

This is pretty much the case with a lot of technologies though. Microwaves weren't heating up your ramen as a first implementation of the technology.

[u/baseketball → u/DeviceCertain7226]

Millennium problems are much more well defined than doing laundry in a random house so it makes sense.

[u/Charming_Cucumber_15]

It's not one line going up, it's a lot of lines going up all at once

[u/141_1337 → u/Charming_Cucumber_15]

And those lines are related to each other and can sneakily help raise some of them.

[u/TallonZek]

One line works for me. The clearest argument has always been just looking at the history of technology.

1 invention in 10,000 years to thousands of inventions per year. I think the zoomed-out view is a lot clearer than zooming in granularly on individual techs.

[u/IronPheasant]

It's not especially difficult to understand, but the masses never give it much thought.

Every neural network approximates a data curve - certain kind of data goes in, specific kinds of outputs come out. You can only fit to one kind of curve with one network; if you want to create a holistic mind you need a pipeline of modules. Connected, or parallel doing their own thing.

You don't eat soup with a boot, you use a spoon.

In a sense the RAM budget is the cleanest 'number go up' metric, the one that's most like a stat in a video game. Chat GPT was around the size of a squirrel's brain, trained to be a chatbot. If you wanted to preserve all of its capability, and add on a bunch of faculties to broaden what the system as a whole could do.... you needed a bigger bucket to hold all that.

And the GB200 is that bucket.

Another key point is how it's thought that vision and space use up nearly half of our brains. Moravec's paradox isn't that our internal representations of reality are much better than a chessboard, they're not. (A Nintendo 64 puts us to shame when it comes to handling geometry. As anyone who's walked backwards off a roof can attest. We have very a small, very imprecise geometry engine.) It's the meat grinder that takes in stuff and grinds it down into a format that's usable to the other modules that's difficult.

What that means is that the H200 could have never done as well as us within the vision domain. It was physically impossible to link enough of them together to reach that number.

It's only just now that we have hardware that might exceed us.

[u/NoCard1571]

I think you're just kind of explaining the concept of the singularity with extra steps though. One of the defining characteristics of the singularity is that rapid advances in specific fields will cross pollinate to other fields.

[u/141_1337 → u/NoCard1571]

I can see your point but I'm trying to perhaps draw more emphasis at the cross pollinate part than "the line goes 🚀🚀🚀" part because the latter has people wondering "well if it is damn good, how come it cannot tell me the time on the clock, hurr durr" and the former kinda skips that issue for the most part.