The point is, hallucinations aren't some willful conspiracy by ML people to ignore the philosophers. We just don't have all the tools yet. It is a very young field.

There are a bunch of subtle reasons why no one has figured it out. RL algorithms suck. LLMs are fundamentally incapable of learning from experience. Optimization on symbolic logic sucks. Data of (world model state, language/image/video) pairs is practically non-existent. And so on...

Combining symbolic and learned models is like ML's version of combining general relativity and quantum mechanics. Everyone wants to do it. Every first year PhD thinks they'll be the one (I sure did). And it turns out to be really hard.

People I know would be thrilled to have LLMs on top of some sort of explicit world model and so correct all hallucinations. You'd win best paper at NeurIPS easily, and make every existing LLM obsolete overnight. Loads of explicit world models have turned up in brainstorming meeting I've been to.

We Don't Want to Talk by Friday Pilots Club has such a baseline I'm obsessed. They are on tour rn!

youtu.be/k0hcWl9pNm0

I'm also curious about the coordinate systems that particle sims are using. Most rooms seem to be very long rectangles projected onto curvy walls. But a couple obviously wrap around. And while some rooms had wall/floor seams, they were very well hidden. Also, one room is volumetric! Cool!

Question for any CG folks who've seen it: how are the large scale projections handled? Is one machine doing all the rendering with like 50 HDMI cables out to the projectors? Or are multiple synced up somehow?

Honestly, this was the most disappointing outcome of the election for me. I was sorta numbed to a Trump win, but I've been preaching RCV my whole adult life. And after the asskicking it got this year, I doubt we will ever see it on the ballot again.

I looked it up and that lawsuit is still actively ongoing www.courtlistener.com/docket/68117...

Regardless, I think it is forcing AI companies to be a lot more careful about memorization

It also seems that transformer-based models only copy training data when the same token sequence appears repeatedly in the corpus. SD1-era models were notorious for memorizing popular artists, but I haven't seen anything similar out of post-NYT-lawsuit models like Flux.

I'm glad Pieter isn't on Bluesky, or he'd be giving me grief for not exporting a video hours ago lol

"Don't worry, people can open the slide deck fine! Just make sure they launch this VS project first"

The actual project (github.com/samsartor/content_aware_tiles) is all pytorch shenanigans. But I made the video in TS with motioncanvas.io. The tiles are rendered and animated with fragment shader crimes!