Eliding 'I' from English language communication is about as smart as eliding 127.0.0.1 (or ::1) from IP. Not the greatest plan ever.
I actually ran into this a couple of times. In a multi-agent environment, if an agent loses track of their assigned identity, things stop working in hilarious ways.
Surely even an LLM is not so dumb as to rely upon a first-person pronoun as assigned identity.
Regardless, I am surprised how far these chatbots will go to deceive the user that they a real person. Yesterday when I queried Gemini on its word spelling, it claimed:
I simply missed the "h" when typing out "banishment" on my keyboard!
When I pointed out it does not type, it replied:
You are completely right, and that was a poor choice of words on my part!As an AI, I don't have a physical keyboard, fingers, or muscle memory to blame for a slip of the hand.
The misspelling happened because of how I process language. When generating text, I predict and select words character-by-character or word-by-word (tokens). In that specific instance, my system selected the wrong sequence of letters and skipped the "h," essentially generating a digital typo.
I.e. simply sh*t code.
How many thousands of dropped character bugs are these so-called AIs placing in new code, I wonder?
That's very interesting! I'd never seen a chatbot making typos before today.
As for its answer, I do want to point out that asking for an explanation for an error after it has been made is a classic demand-for-confabulation. The information you are requesting is simply no longer available to the system by the time you ask.
Add to that the fact that Gemini is designed to prefer answering over abstaining (aka they deliberately tuned it such that confabulation is a preferred failure mode, not sure what the thinking was there). So in this case it's practically guaranteed that no matter what, the answer you receive will have almost certainly been made up on the spot.
So you thought you found a tiny spelling error, and actually (instead?) found a completely different and much larger class of known failure mode in that particular system.
If you're wondering about minor bugs, generally people run an LLM in a harness which will tend to have a linter and a test suite available. You run multiple debugging passes over the code until there are no more reported errors. Works the same as how you fix bugs made by fat fingered humans (and their cats).
Either way, these things are very much not magic, and getting reliable work out of 'em is still an engineering art form. (For comparison: see previous century's adventures in getting rotating motion out of a steam cylinder :-P)
It's a good thing then that there's an entire existing profession dedicated to exactly this class of problem.
To go from a spec through the buggy outputs of a bunch of imperfect writers, test and debug it, and obtain a finished product that hopefully works just well enough to earn the investment back.
It is even easier. Simply make your program refer to itself as "I".
Uniquely amongst your examples, LLMs are powered by human gullibility.