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.

I think what might work is people coming together around this LLM like a God.

Similar to Rod of Iron Ministries (The Church of the AR-15) Taking what is says, fine tuning it, testing it, feeding back in and mostly waiting as LLMs improve.

LLMs will never be smarter than humans, but they can be a meeting place where people congregate to work on goals and worship.

Like QAnon, that's where the collective IQ and power comes from, something to believe in. At the micro level this is also mostly how LLMs are used in practical ways.

If you look to the Middle East there is a lot of work on rockets but a limited community working together.

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.

How about halting problem ? I see llm often got infinite recursive problem.

More evidence that we need fine tuned domain specific models. Some one should come up with a medical LLM fine tuned on a 640b model. What better RL dataset can you have than a patient with symptoms and the correct diagnosis?

A fundamental problem with this entire class of machine learning is that it is based on a model / simulation of reality. "RocketPy, a high-fidelity trajectory simulation library for high-power rocketry" in this case.

Nothing against this sim in particular but all such simulations that attempt to model any non-trivial system are imperfect. Nature is just too complex to model precisely and accurately. The LLM (or other DL network architecture) will only learn information that is presented to it. When trained on simulation the network can not help but infer incorrectly about messy reality.

For example, if RocketPy lacks any model of cross breezes, the network would never learn to design to counter them. Or, if it does model variable winds but does so with the wrong mean, or variance, or skew (of intensity, period, etc) the network can not properly learn and the design will not be optimal. The design will fail when it faces reality that differs from model.

Replace "rocket" with any other thing and you have AI/ML applied to science and engineering - fundamentally flawed, at least at some level of precision/accuracy.

At the least, real learning on reality is required. Once we can back-propagate through nature, then perhaps DL networks can begin to be actually trustworthy for science and engineering.