sure it does. it won’t tell you how to build a bomb or demonstrate explicit biases that have been fine tuned out of it. the problem is McDonald’s isn’t an AI company and probably is just using ChatGPT on the backend, and GPT doesn’t give a shit about bacon ice cream out of the box.
Comment on [deleted]
bstix@feddit.dk 8 months ago
Those mistakes would be easily solved by something that doesn’t even need to think. Just add a filter of acceptable orders, or hire a low wage human who does not give a shit about the customers special orders.
In general, AI really needs to set some boundaries. “No” is a perfectly good answer, but it doesn’t ever do that, does it?
chrash0@lemmy.world 8 months ago
dch82@lemmy.zip 8 months ago
They really should have used a genetic algorithm to optimise their menu items for maximum
customer satisfactionprofits instead of using an LLM!The execs do know other algorithms than LLMs exist right?
bstix@feddit.dk 8 months ago
So, what happens if you order a bomb in in a McD?
Lost_My_Mind@lemmy.world 8 months ago
You get bacon on ice cream.
lvxferre@mander.xyz 8 months ago
That wouldn’t address the bulk of the issue, only the most egregious examples of it.
For every funny output like “I asked for 1 ice cream, it’s giving me 200 burgers”, there’s likely tens, hundreds, thousands of outputs like “I asked for 1 ice cream, it’s giving 1 burger”, that sound sensible but are still the same problem.
It’s simply the wrong tool for the job. Using LLMs here is like hammering screws, or screwdriving nails. LLMs are a decent tool for things that you can supervision (not the case here), or where a large amount of false positives+negatives is not a big deal (not the case here either).