Maybe this is because humans have good intuition to know the difference between us. But this type of intuition does not work on the behaviour of LLMs.

Today, I trust the other meaning of "fake images" is that an image was generated by AI.

Sometimes they do all the operations by themselves. Finally, this is a race about who can find a scientist to design the best "research project" for their kids.

Maybe the most benefits are from the condition that people can read another new paper with enough background knowledge.

If patients are lawyers, doctors, or engineers, this system will still work for them.

But I'm sure that 30% employee is more valuable than just calling API in one month. So the price is too high.

It happened at least once; when I asked too many questions, the Gemini web page stopped working because it was occupying too much RAM...

Thanks for your kind comment! I do not have any systematic leaning of computer science; I often feel confused when reading textbooks on algorithms hahaha.

Should I be familiar with every step of Dijkstra’s search algorithm and remember the pseudocode at all times? Why don’t the textbooks explain why the algorithm is correct?

> Should I be familiar with every step of Dijkstra’s search algorithm and remember the pseudocode at all times?

Somehow, I think you already know the answer to that is "no".

I've been working as a software engineer for over 8 years, with no computer science education. I don't know what Dijkstra's search algorithm is, let alone have memorised the pseudocode. I flicked through a book of data structures and algorithms once, but that was after I got my first software job. Unless you're only aiming for Google etc, you don't really need any of this.

You should know the trade-offs of different algorithms, though. Many libraries let you choose the implementation for a spcific problem. For instance tree vs. hash map where you trade memory for speed.

> Why don’t the textbooks explain why the algorithm is correct?

The good ones do!

> Should I be familiar with every step of Dijkstra’s search algorithm and remember the pseudocode at all times?

If it’s the kind of thing you care to be familiar with, then being able to rederive every step of the usual-suspect algorithms is well within reach, yes. You don’t need to remember things in terms of pseudocode as such, more just broad concepts.

I found this line in the first line of the first page of this thread:

>It's still an open problem as to whether there exists a spaceship in B3/S23 which fits within a 1-by-N bounding box in one of its phases.

So they use the "typical" rule here.

Maybe we can make school harder so they will go there earlier.