I would put it differently. Those inventions came from humans interacting with the physical world.

When LLMs were first introduced, they didn't have much of a feedback loop. They wrote code, but they couldn't compile it. Not surprisingly, the code had bugs.

Now, they run with harnesses that allow them to compile the code, and react to the issues they observe. They can fix their own bugs and solve problems that they create, just like humans.

Give an agent access to the physical world, and it seems highly likely that they will be able to "invent" things based on feedback they receive while working towards goals.

Of course, there are some well-known limitations of LLMs, one of the biggest being that they're pretrained. So there may be some things where they're not as good. Just like how some humans aren't as good at certain tasks, depending on their genetics and/or how they've been trained.

> it seems highly likely that they will be able to "invent" things based on feedback they receive while working towards goals...

I don't think so. Imagine a model trained on data from an Internet that believes in hypothesis that earth is the center of the world. Even if you feed all the physical data, I don't think it can come up with the idea that all of its training data was wrong.

This might be also a good argument for why this LLMs are not "intelligent". You can feed contradicting training data all day and it will accept it without bating an eye. But that won't work with an entity that is truly intelligent.