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emporas | 2 months ago

There is also Mercury LLM, which computes the answer directly as a 2D text representation. I don't know if you are familiar with Mercury LLM, but you read correctly, 2D text output.

Mercury LLM might work better getting input as an ASCII diagram, or generating an output as an ASCII diagram, not sure if both input and output work 2D.

Plumbing/electrical/electronic schematics are pretty important for AIs to understand and assist us, but for the moment the success rate is pretty low. 50% success rate for simple problems is very low, 80-90% success rate for medium difficulty problems is where they start being really useful.

discuss

order

jacquesm|2 months ago

It's not really the quality of the diagramming that I am concerned with, it is the complete lack of understanding of electronics parts and their usual function. The diagramming is atrocious but I could live with it if the circuit were at least borderline correct. Extrapolating from this: if we use the electronics schematic as a proxy for the kind of world model these systems have then that world model has upside down lanterns and anti-gravity as commonplace elements. Three legged dogs mate with zebras and produce viable offspring and short circuiting transistors brings about entirely new physics.

baq|2 months ago

it's hard for me to tell if the solution is correct or wrong because I've got next to no formal theoretical education in electronics and only the most basic 'pay attention to polarity of electrolytic capacitors' practical knowledge, but given how these things work you might get much better results when asking it to generate a spice netlist first (or instead).

I wouldn't trust it with 2d ascii art diagrams, there isn't enough focus on these in the training data is my guess - a typical jagged frontier experience.