I am right now implementing an imagining pipeline using OpenCV and TypeScript.

I have never used OpenCV specifically before, and have little imaging experience too. What I do have though is a PhD in astrophysics/statistics so I am able to follow along the details easily.

Results are amazing. I am getting results in 2 days of work that would have taken me weeks earlier.

ChatGPT acts like a research partner. I give it images and it explains why current scoring functions fails and throws out new directions to go in.

Yes, my ideas are sometimes better. Sometimes ChatGPT has a better clue. It is like a human collegue more or less.

And if I want to try something, the code is usually bug free. So fast to just write code, try it, throw it away if I want to try another idea.

I think a) OpenCV probably has more training data than circuits? and b) I do not treat it as a desperate student with no knowlegde.

I expect to have to guide it.

There are several hundred messages back and forth.

It is more like two researchers working together with different skill sets complementing one another.

One of those skillsets being to turn a 20 message conversation into bugfree OpenCV code in 20 seconds.

No, it is not providing a perfect solution to all problems on first iteration. But it IS allowing me to both learn very quickly and build very quickly. Good enough for me..

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.

I have this mental model of LLMs and their capabilities, formed after months of way too much coding with CC and Codex, with 4 recursive problem categories:

1. Problems that have been solved before have their solution easily repeated (some will say, parroted/stolen), even with naming differences.

2. Problems that need only mild amalgamation of previous work are also solved by drawing on training data only, but hallucinations are frequent (as low probability tokens, but as consumers we don’t see the p values).

3. Problems that need little simulation can be simulated with the text as scratchpad. If evaluation criteria are not in training data -> hallucination.

4. Problems that need more than a little simulation have to either be solved by adhoc written code, or will result in hallucination. The code written to simulate is again a fractal of problems 1-4.

Phrased differently, sub problem solutions must be in the training data or it won’t work; and combining sub problem solutions must be either again in training data, or brute forcing + success condition is needed, with code being the tool to brute force.

I _think_ that the SOTA models are trained to categorize the problem at hand, because sometimes they answer immediately (1&2), enable thinking mode (3), or write Python code (4).

My experience with CC and Codex has been that I must steer it away from categories 2 & 3 all the time, either solving them myself, ask them to use web research, or split them up until they are (1) problems.

Of course, for many problems you’ll only know the category once you’ve seen the output, and you need to be able to verify the output.

I suspect that if you gave Claude/Codex access to a circuit simulator, it will successfully brute force the solution. And future models might be capable enough to write their own simulator adhoc (ofc the simulator code might recursively fall into category 2 or 3 somewhere and fail miserably). But without strong verification I wouldn’t put any trust in the outcome.

With code, we do have the compiler, tests, observed behavior, and a strong training data set with many correct implementations of small atomic problems. That’s a lot of out of the box verification to correct hallucinations. I view them as messy code generators I have to clean up after. They do save a ton of coding work after or while I‘m doing the other parts of programming.

Yes, but for that you have to know that the output it gave you is wrong in the first place and if that is so you didn't need AI to begin with...

It’s less shifting goalposts and more of a very jagged frontier of capabilities problem.