It's not specific to software, it's the entire World of business. Most knowledge work is translation from one domain/perspective to another. Not even knowledge work, actually. I've been reading some works by Adler[0] recently, and he makes a strong case for "meaning" only having a sense to humans, and actually each human each having a completely different and isolated "meaning" to even the simplest of things like a piece of stone. If there is difference and nuance to be found when it comes to a rock, what hope have we got when it comes to deep philosophy or the design of complex machines and software?
LLMs are not very good at this right now, but if they became a lot better at, they would a) become more useful and b) the work done to get them there would tell us a lot about human communication.
By now it should know this stuff.
Although I don't think they actually "know" it. This particular trick question will be in the bank just like the seahorse emoji or how many Rs in strawberry. Did they start reasoning and generalising better or did the publishing of the "trick" and the discourse around it paper over the gap?
I wonder if in the future we will trade these AI tells like 0days, keeping them secret so they don't get patched out at the next model update.
They won’t get this specific question wrong again; but also they generalise, once they have sufficient examples. Patching out a single failure doesn’t do it. Patch out ten equivalent ones, and the eleventh doesn’t happen.
"Well, you need your car to be at the car wash in order to wash it, right?"
You will get exactly what you asked for, not what you wanted… probably. (Random occurrences are always a possibility.)
E.g.: I may ask someone to submit a ticket to “extend my account expiry”.
They’ll submit: “Unlock Jiggawatts’ account”
The service desk will reset my password (and neglect to tell me), leaving my expired account locked out in multiple orthogonal ways.
That’s on a good day.
Last week they created Jiggawatts2.
The AIs have got to be better than this, surely!
I suspect they already are.
People are testing them with trick questions while the human examiner is on edge, aware of and looking for the twist.
Meanwhile ordinary people struggle with concepts like “forward my email verbatim instead of creatively rephrasing it to what you incorrectly though it must have really meant.”
But in this given case, the context can be inferred. Why would I ask whether I should walk or drive to the car wash if my car is already at the car wash?
Even the higher level reasoning, while answering the question correctly, don't grasp the higher context that the question is obviously a trick question. They still answer earnestly. Granted, it is a tool that is doing what you want (answering a question) but let's not ascribe higher understanding than what is clearly observed - and also based on what we know about how LLMs work.
In fact, it's particularly true for AI models because the question could have been generated by some kind of automated process. e.g. I write my schedule out and then ask the model to plan my day. The "go 50 metres to car wash" bit might just be a step in my day.
