Technology

am I understanding correctly that it's just a fancy random word generator

The biggest disappointment in the image generation capabilities was the realisation that there is no object permanence there in terms of components making up an image so for any specificity you're just playing whackamole with iterations that introduce other undesirable shit no matter how specific you make your prompts.

They are also now heavily nerfing the models to avoid lawsuits by just ignoring anything relating to specific styles that may be considered trademarks, problem is those are often industry jargon so now you're having to craft more convoluted prompts and get more mid results.

It does require fact-checking. You might ask a human and get someone with 10 fingers on one hand, you might ask people in the background and get blobs merged on each other. The fact check in images is absolutely necessary and consists of verifying that the generate image adheres to your prompt and that the objects in it match their intended real counterparts.

I do agree that it's a different type of fact checking, but that's because an image is not inherently correct or wrong, it only is if compared to your prompt and (where applicable) to reality.

Image generation requires no fact checking whatsoever

Sure it does. Let's say IKEA wants to use midjourney to generate images for its furniture assembly instructions. The instructions are already written, so the prompt is something like "step 3 of assembling the BorkBork kitchen table".

Would you just auto-insert whatever it generated and send it straight to the printer for 20000 copies?

Or would you look at the image and make sure that it didn't show a couch instead?

If you choose the latter, that's fact checking.

That said, LLMs will always have limitations and true AI is still a ways away.

I can't agree more strongly with this point!

In my personal experience given that's how I general manage to shortcut a lot of labour intensive intellectual tasks, using intuition to guess possible answers/results and then working backwards from them to determine which one is right and even prove it, is generally faster (I guess how often it's so depends on how good one's intuition is in a given field, which in turn correlates with experience in it) because it's usually faster to show that a result is correct than to arrive at it (and if it's not, you just do it the old fashion way).

That said, it's far from guaranteed faster and for those things with more than one solution might yield working but sub-optimal ones.

Further, merelly just the intuition step does not yield a result that can be trusted without validation.

Maybe by being used as intuition is in this process, LLMs can help accelerate the search for results in subjects one has not enough experience in to have good intuition on but has enough experience (or there are ways or tools to do it inherent to that domain) to do the "validation of possible results" part.

It's only easier to verify a solution than come up with a solution when you can trust and understand the algorithms that are developing the solution. Simulation software for thermodynamics is magnitudes faster than hand calculations, but you know what the software is doing. The creators of the software aren't saying "we don't actually know how it works".

In the case of an LLM, I have to verify everything with no trust whatsoever. And that takes longer than just doing it myself. Especially because an LLM is writing something for me, it isn't doing complex math.

No scientific discover has value