71–80 of 208 posts
It's funny that the authors are 'anonymous' but they have access to Imagen so obviously it's by Google.
Can someone explain what's going on in this example from the gallery? The prompt is "a humanoid robot using a rolling pin to roll out dough": https://dreamfusion-cdn.ajayj.com/gallery_sept28/crf20/a_DSL... But if you look closely, the pin looks like it's actually rolling across the dough as the camera orbits.
The rolling pin is above the table but the shading is wrong because they don't render shadows.
Did we hit some sort of technical inflection point in the last couple of weeks or is this just coincidence that all of these ML papers around high quality procedural generation are just dropping every other day?
It's funny that the authors are 'anonymous' but they have access to Imagen so obviously it's by Google.
- they rarely provide the data or code used so it's basically "i swear it works bro" research
- what they achieve is usually through having the most pristine dataset on the planet and is often unusable by other researchers
- other times they publish papers that are basically "we slightly modified this excellent open source paper, slapped an internal name on it and trained it on our proprietary dataset"
- sometimes they achieve remarkably little but their papers still get a shiny spot because they're a big name and sponsor all the conferences
- they've also been caught trying to patent/copyright ML techniques; disregarding that this is the same as privatizing math, these are often techniques they plainly didn't come up with
Also ever since OpenAI did their "we have to go closed-source for-profit to save humanity" PR campaign, every company that releases models that can achieve a large amount in NLP/CV gets dragged by the media and equated to Skynet.
Gives a new perspective on a classic verse: "For he spoke, and it came to be; he commanded, and it stood firm." Psalm 33:9, NIV :)
It's funny that the authors are 'anonymous' but they have access to Imagen so obviously it's by Google.
Once the paper is accepted (or rejected) the names may be revealed.
Though, in reality, the reviewers can often easily tell who wrote the paper.
It's funny that the authors are 'anonymous' but they have access to Imagen so obviously it's by Google.
This is par for the course - there have been other instances where an 'anonymous' paper mentioned training on a cluster of TPUs that weren't publicly available yet - dead giveaway it was Google.
Earlier quoted context omitted.
> This seems like basically plugging a couple of techniques together that already existed as with a majority of ML research
Isn't that what the Singularity was described as a few decades ago? Progress so fast it's unpredictable even in the short term.
Earlier quoted context omitted.
From the abstract: “We introduce a loss based on probability density distillation that enables the use of a 2D diffusion model as a prior for optimization of a parametric image generator. Using this loss in a DeepDream-like procedure, we optimize a randomly-initialized 3D model (a Neural Radiance Field, or NeRF) via gradient descent such that its 2D renderings from random angles achieve a low loss.” This seems like b…
> This seems like basically plugging a couple of techniques together that already existed [...] In his Lex Fridman interview, John Carmack makes similar assertions about this prospect for AGI: That it will likely be the clever combination of existing primitives (plus maybe a couple novel new ones) that make the first AGI feasible in just a couple thousand lines of code.