boemComputers make mistakes and AI will make things worse — the law must recognize that
All the more reason that devs and admins need to take responsibility and NOT roll out “AI” solutions with backstopping them with human verification, or at minimum ensure that the specific solutions they employ are ready for production.
It’s all cool and groovy that we have a new software stack that can remove a ton of labor from humans, but if it’s too error-prone, is it really useful? I get that the bean counter and suits are ready to boot the data entry and other low level employees to boost their bottom line, but this will become a race too the bottom with blowing their collective loads too early.
Soon there will be modules added to LLMs, that they can use to logically (fact)check the output on their own.
deepmind.google/…/alphageometry-an-olympiad-level…
This is so awesome, watch Yannic explaining it:
You might be presenting it backwards. We need LLMs to be right-sized for translation between pure logical primitives and human language. Let a theorem prover or logical inference system (probably written in Prolog :-) ) provide the smarts. A LLM can help make the front end usable by regular people.
We spent decades on educating people that “computers don’t make mistakes” and now you want them to accept that they do?
We filled them with shit, that’s what. We don’t even know how that shit works, anymore.
Let’s be honest here.
HumaniusI agree with this distinction, but ultimately if you are at the receiving end of a decision made my either a classic algorithm or an machine learning algorithm, and a mistake is made… You probably won’t care whether it was the computer making the mistake or the programmer making the mistake.
if you are at the receiving end of a mistake made my either a classic algorithm or an machine learning algorithm, then you probably won’t care whether it was the computer or the programmer making the mistake
I’m absolutely expecting corporations to get away with the argument that “they cannot be blamed for the outcome of a system that they neither control nor understand, and that is shown to work in X% of cases”. Or at least to spend billions trying to.
And in case you think traceability doesn’t matter anyway, think again.
IMHO it’s crucial we defend the “computers don’t make mistakes” fact for two reasons:
You’ll care if you’re trying to sue someone and you want to win.
Short of a floating point bug, computers don’t make mistakes. They do exactly what they’re programmed to do. The issue is the people developing them are fallible and QC has gone out the window globally, so you’re going to get computers that operate as good as the Devs and QC are.
There’s always small hardware quirks to be accounted for, but when we are talking about machine learning, which is not directly programmed, it’s less applicable to blame developers.
The issue is that computer system are now used to whitewash mistakes or biases with a veneer of objective impartiality. Even an accounting system’s results are taken as fact.
Consider that an AI trained with data from the history of policing and criminal cases might make racist decisions, because the dataset includes a plenty of racist bias, but it’s very easy for the people using it to say “welp, the machine said it so it must be true”. The responsibility for mistakes is also abstracted away, because the user and even the software provider might say they had nothing to do with it.
Perfectly good computers do make random bit flip mistakes, and the smaller they get the more issues we will see with that.
Even highly QA’d code like they put on the space shuttle put 5 redundant computers in to reduce the chance they all fail.
MoogleMaestroComputers don't make mistake, software makes mistakes.