My hypothesis is until they can really nail down image to text and text to image, such that training on diagrams and drawings can produce fruitful multi modal output, classic engineering is going to be a tough nut to crack.
Software engineering lends itself greatly to LLMs because it just fits so nicely into tokenization. Whereas mechanical drawings or electronic schematics are sort of more like a visual language. Image art but with very exacting and important pixel placement, with precise underlying logical structure.
In my experience so far, only O3 can kind of understand an electronic schematic, but really only at a "Hello World!" level difficulty. I don't know how easy it will be to get to the point where it can render a proper schematic or edit one it is given to meet some specified electronic characteristics.
There are programming languages that are used to define drawings, but the training data would be orders of magnitude less than what is written for humans to learn from.
heisenzombie · 2h ago
My experience is that SOTA LLMs still struggle to read even the metadata from a mechanical drawing. They're getting better -- they now are mostly ok at reading things like a BOM or revision table -- but moderately complicated title blocks often trip them up.
As for the drawings themselves, I have found them pretty unreliable at reading even quite simple things (i.e. what's the ID of the thru hole?), even when they're specifically dimensioned. As soon as spatial reasoning is required (i.e. there's a dimension from A to B and from A to C and one asks for the dimension B to C), they basically never get it right.
This is a place where there's a LOT of room for improvement.
Terr_ · 45m ago
I'm scared of something like the Xerox number-corruption bug [0], where some models will subtly fuck everything up in a way that is too expensive to recover from by the time it's discovered.
Problem #1 with text-to-image models is that focus is on producing visually attractive photo-realistic artistic images, which is completely orthogonal from what is needed for engineering: accurate, complete, self-consistent, and error-free diagrams.
Problem #2 is low control over outputs of text-to-image models. Models don't follow prompts well.
slicktux · 3h ago
Electrical schematics can be represented with linear algebra and Boolean logic…
Maybe their being able to “understand” such schematics is just a matter of them becoming better at mathematical logic…which is pretty objective.
davemp · 1h ago
Not entirely true. Routing is a very important part of electrical schematics.
yieldcrv · 1h ago
Tell it how to read schematics in the prompt
akomtu · 16m ago
Imagine a fake engineer who read books about engineering as scifi, and thanks to his superhuman memory, he's mastered the engineer-speak so well that he sounds more engineery than top engineers in the world. Except that he has no clue about engineering and to him it's the same as literature or prose. Now he's tasked with designing a bridge. He pauses for a second and starts speaking, in his usual polished style: "sure, let me design a bridge for you." And while he's talking, he's starring at you with his perfect blank face expression, for his mind is blank as well.
Think of the absurdity of trying to understand the Pi number by looking at its first billion digits and trying to predict the next digit. And think of what it takes to advance from memorizing digits of such numbers and predicting continuation with astrology-style logic to understanding the math behind the digits of Pi.
Software engineering lends itself greatly to LLMs because it just fits so nicely into tokenization. Whereas mechanical drawings or electronic schematics are sort of more like a visual language. Image art but with very exacting and important pixel placement, with precise underlying logical structure.
In my experience so far, only O3 can kind of understand an electronic schematic, but really only at a "Hello World!" level difficulty. I don't know how easy it will be to get to the point where it can render a proper schematic or edit one it is given to meet some specified electronic characteristics.
There are programming languages that are used to define drawings, but the training data would be orders of magnitude less than what is written for humans to learn from.
As for the drawings themselves, I have found them pretty unreliable at reading even quite simple things (i.e. what's the ID of the thru hole?), even when they're specifically dimensioned. As soon as spatial reasoning is required (i.e. there's a dimension from A to B and from A to C and one asks for the dimension B to C), they basically never get it right.
This is a place where there's a LOT of room for improvement.
[0] https://www.dkriesel.com/en/blog/2013/0802_xerox-workcentres...
Problem #2 is low control over outputs of text-to-image models. Models don't follow prompts well.
Think of the absurdity of trying to understand the Pi number by looking at its first billion digits and trying to predict the next digit. And think of what it takes to advance from memorizing digits of such numbers and predicting continuation with astrology-style logic to understanding the math behind the digits of Pi.