Computers follow instructions, engineers make mistakes. Now engineers have instructed computers to make huge guesses, this is the new mistake.
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.
I the example you gave I would actually put the blame the software provider. It wouldn’t be ridiculously difficult to anonimize the data, get rid of name, race, gender, and leave only the information about the crime committed, the evidence, any extenuating circumstances, and the judgment.
It’s more difficult then simply throwing in all the data, but it can and should be done. It could still contain some bias, based on things like the location of the crime. But the bias would be already greatly reduced.
I don’t think you can completely anonymize data and still end up with useful results, because the AI will be faced with human inconsistency and biases regardless. Take away personally identifiable information and it might mysteriously start behaving harsher regarding certain locations, like, you know, districts where mostly black and poor people live.
We’d need to have a reckoning with our societal injustices before we can determine what data can be used for many purposes. Unfortunately many people who are responsible for these injustices are still there, and they will be the people who will determine if the AI output is serving their purpose or not.
The “AI” that I think is being referenced is one that instructs officers to more heavily patrol certain areas based on crime statistics. As racist officers often patrol black neighbourhoods more heavily, the crime statistics are higher (more crimes caught and reported as more eyes are there). This leads to a feedback loop where the AI looks at the crime stats for certain areas, picks out the black populated ones, then further increases patrols there.
In the above case, any details about the people aren’t needed, only location, time, and the severity of the crime. The AI is still being racist despite race not being in the dataset
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.
Not every piece of software is worth the resources to do that though. If your game crashes just restart it.
All the more reason that devs and admins need to take responsibility and NOT roll out “AI” solutions withoit 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 counters 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 to the bottom via blowing their collective loads too early.
Though let’s be real, we already know that too many companies are going to do this and then try to absolve themselves of liability when shit goes to hell because of their shit.
Having worked in IT for many years, bosses only hear the “it can be done” part and never the “but we should add these precautions” or “but we should follow these best practices”
Those translate to “those developers want to add unnecessary extra costs” to them.
“So we can create the dinosaurs immediately you say?”
Soon there will be modules added to LLMs, so that they can learn real logic and use that to (fact)check the output on their own.
https://deepmind.google/discover/blog/alphageometry-an-olympiad-level-ai-system-for-geometry/
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.
Here is an alternative Piped link(s):
https://piped.video/ZNK4nfgNQpM?si=CN1BW8yJD-tcIIY9
Piped is a privacy-respecting open-source alternative frontend to YouTube.
I’m open-source; check me out at GitHub.
Computers mostly don’t make mistake, software makes mistakes.
edit: Added mostly because I do suppose there are occasions where hardware level mistakes can happen…
Software is imperfect because it’s created by humans and humans are imperfect.