Comment by dist-epoch
1 day ago
Most of them are text-only models. Like asking a person born blind to draw a pelican, based on what they heard it looks like.
1 day ago
Most of them are text-only models. Like asking a person born blind to draw a pelican, based on what they heard it looks like.
That seems to be a completely inappropriate use case?
I would not hire a blind artist or a deaf musician.
Yeah, that's part of the point of this. Getting a state of the art text generating LLM to generate SVG illustrations is an inappropriate application of them.
It's a fun way to deflate the hype. Sure, your new LLM may have cost XX million to train and beat all the others on the benchmarks, but when you ask it to draw a pelican on a bicycle it still outputs total junk.
tried starting from an image:
https://chatgpt.com/share/684582a0-03cc-8006-b5b5-de51e5cd89...
lol: https://gemini.google.com/share/4d1746a234a8
I guess the idea is that by asking the model to do something that is inherently hard for it we might learn something about the baseline smartness of each model which could be considered a predictor for performance at other tasks too.
Sorry, Beethoven, you just don’t seem to be a match for our org. Best of luck on your search!
You too, Monet. Scram.
It's a proxy for abstract designing, like writing software or designing in a parametric CAD.
Most the non-math design work of applied engineering AFAIK falls under the umbrella that's tested with the pelican riding the bicycle. You have to make a mental model and then turn it into applicable instructions.
Program code/SVG markup/parametric CAD instructions don't really differ in that aspect.
I would not assume that this methodology applies to applied engineering, as a former actual real tangible meat space engineer. Things are a little nuanced and the nuances come from a combination of communication and experience, neither of which any LLM has any insight into at all. It's not out there on the internet to train it with and it's not even easy to put it into abstract terms which can be used as training data. And engineering itself in isolation doesn't exist - there is a whole world around it.
Ergo no you can't just say throw a bicycle into an LLM and a parametric model drops out into solidworks, then a machine makes it. And everyone buys it. That is the hope really isn't it? You end up with a useless shitty bike with a shit pelican on it.
The biggest problem we have in the LLM space is the fact that no one really knows any of the proposed use cases enough and neither does anyone being told that it works for the use cases.
4 replies →
The point is about exploring the capabilities of the model.
Like asking you to draw a 2D projection of 4D sphere intersected with a 4D torus or something.
Yeah, I suppose it is similar.. I don't know their diameters, rotations, nor the distance between their centers, nor which two dimensions, so I would have to guess a lot about what you meant.
Even Beethoven?