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0 pointsemporas3mo ago0 comments

I have used Gemini for reading and solving electronic schematics exercises, and it's results were good enough for me. Roughly 50% of the exercises managed to solve correctly, 50% wrong. Simple R circuits.

One time it messed up the opposite polarity of two voltage sources in series, and instead of subtracting their voltages, it added them together, I pointed out the mistake and Gemini insisted that the voltage sources are not in opposite polarity.

Schematics in general are not AIs strongest point. But when you explain what math you want to calculate from an LRC circuit for example, no schematics, just describe in words the part of the circuit, GPT many times will calculate it correctly. It still makes mistakes here and there, always verify the calculation.

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jacquesm3mo ago

I guess I'm just more critical than you are. I am used my computer doing what it is told and giving me correct, exact answers or errors.

dagss3mo ago

I think most people treat them like humans not computers, and I think that is actually a much more correct way to treat them. Not saying they are like humans, but certainly a lot more like humans than whatever you seem to be expecting in your posts.

Humans make errors all the time. That doesn't mean having colleagues is useless, does it?

An AI is a colleague that can code very very fast and has a very wide knowledge base and versatility. You may still know better than it in many cases and feel more experienced that in. Just like you might with your colleagues.

And it needs the same kind of support that humans need. Complex problem? Need to plan ahead first. Tricky logic? Need unit tests. Research grade problem? Need to discuss through the solution with someone else before jumping to code and get some feedback and iterate for 100 messages before we're ready to code. And so on.

jacquesm3mo ago

This is an excellent point, thank you.

emporasOP3mo 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.

jacquesm3mo 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.

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0 pointsemporas3mo ago0 comments

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jacquesm3mo ago

I guess I'm just more critical than you are. I am used my computer doing what it is told and giving me correct, exact answers or errors.

dagss3mo ago

Humans make errors all the time. That doesn't mean having colleagues is useless, does it?

jacquesm3mo ago

This is an excellent point, thank you.

emporasOP3mo 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.

jacquesm3mo ago

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