The major difference is in PCB, every single track has to abide by the rules, no exceptions are allowed if you want your board to work.
While AI-generated art is full of small defects which people are just ignoring, who cares about non-natural shadows or unrealistically large fingers.
It is possible to iteratively combine AI with DRC checker and loop until all is good, but it's not obvious to me that this would be performant enough, or if this system will stay in some sort of local minimum forever once the circuit is complex enough.
The same way claude never outputs code that has a syntax error, the image transformers will output DRC compliant “images”!
I think spatial partitioning can help solve issues with minor DRC violations as well- it should be easier to correct an image than to generate one from scratch. But I’m not even sure it’ll be necessary because of how coherent image models already are.
Claude doesn't usually produce code that actually works though. Passing DRC is one thing (no syntax errors). Actually works is another (compiles and executes with the desired effect as a complete application).
And you don't even get to use unit tests to check correctness.
You're suggesting the robots can learn the routing algorithms and rules just by looking at a bunch of pictures?
Sure, maybe, given a super-massive amount of data.
I see it as the difference between "I want a photo-realistic portrait of a beagle" and "I want a photo-realistic portrait of my neighbor's beagle, Bob". The first one there's some general rules as to what makes something a 'beagle' so is not too hard while the second has specific constraints which can't be solved without a bunch of pictures of Bob.
To address the specific issue, an AI would have to learn the laws of physics (aka, "Bobness") from a bunch of pictures of, essentially, beagles in order to undertake the task at hand.
Comments
The major difference is in PCB, every single track has to abide by the rules, no exceptions are allowed if you want your board to work.
While AI-generated art is full of small defects which people are just ignoring, who cares about non-natural shadows or unrealistically large fingers.
It is possible to iteratively combine AI with DRC checker and loop until all is good, but it's not obvious to me that this would be performant enough, or if this system will stay in some sort of local minimum forever once the circuit is complex enough.
The same way claude never outputs code that has a syntax error, the image transformers will output DRC compliant “images”!
I think spatial partitioning can help solve issues with minor DRC violations as well- it should be easier to correct an image than to generate one from scratch. But I’m not even sure it’ll be necessary because of how coherent image models already are.
Claude doesn't usually produce code that actually works though. Passing DRC is one thing (no syntax errors). Actually works is another (compiles and executes with the desired effect as a complete application).
And you don't even get to use unit tests to check correctness.
You're suggesting the robots can learn the routing algorithms and rules just by looking at a bunch of pictures?
Sure, maybe, given a super-massive amount of data.
I see it as the difference between "I want a photo-realistic portrait of a beagle" and "I want a photo-realistic portrait of my neighbor's beagle, Bob". The first one there's some general rules as to what makes something a 'beagle' so is not too hard while the second has specific constraints which can't be solved without a bunch of pictures of Bob.
To address the specific issue, an AI would have to learn the laws of physics (aka, "Bobness") from a bunch of pictures of, essentially, beagles in order to undertake the task at hand.
I forgot the name unfortunately but there was a project recently where they made AI + physically correct modeling.
EDIT found it, the Genesis project: https://x.com/zhou_xian_/status/1869511650782658846