496
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
this post was submitted on 19 Nov 2023
496 points (86.9% liked)
Technology
58133 readers
4626 users here now
This is a most excellent place for technology news and articles.
Our Rules
- Follow the lemmy.world rules.
- Only tech related content.
- Be excellent to each another!
- Mod approved content bots can post up to 10 articles per day.
- Threads asking for personal tech support may be deleted.
- Politics threads may be removed.
- No memes allowed as posts, OK to post as comments.
- Only approved bots from the list below, to ask if your bot can be added please contact us.
- Check for duplicates before posting, duplicates may be removed
Approved Bots
founded 1 year ago
MODERATORS
Well, I would disagree that they don't predict things. That's entirely what LLMs and such are.
Making predictions about global supply chains isn't the "hard but boring" type of problem I was talking about.
Circling a defect, putting log messages under the right label, or things like that is what it's suited for.
Nothing is good at predicting global supply chain issues. It's unreasonable to expect AI to be good at it when I is also shit at it.
They make probabilistic predictions. Which are ok if you're doing simple forecasting or bucketing based upon historical data, and correlates and all of that.
What they are crappier about is things that are somewhat intuitively obvious but can't be forecasted on the basis of historical trends. So, like new and emerging trends or things like panic buying behavior making it so the whole world is somehow out of TP for a time.
I'd argue that relying solely on "predictive analytics" and just in time supply chains aggravated a lot of issues during the big COVID crunches, and also makes your supply chain more brittle in general.
All predictions are probabilistic.
AI indeed isn't great at modeling complex or difficult to quantify phenomenon, but neither are people.
Our recent logistical issues are much more based on the frailty of just in time supplying than the methods we use to gauge demand. Most of those methods aren't what would typically be called AI, since the system isn't learning so much as it's drawing a line on a graph.
We didn't actually run out of toilet paper, people just thought we did and so would buy all of it if they saw it in the shelves. It's a relatively local good, so it didn't usually get caught up in the issues with shipping getting bogged down, it's just that people chose to override the model that said that stores should buy five trucks full of TP because it would fill their warehouse and they were worried they'd be stuck with the backlog.
Eh, not really. All math / model based predictions are probabilistic. There's other ways to make predictions, and not all of them are based on math, and they might be wrong more often than a probabilistic model, but they exist.
Sure, fair enough, but there are times where a computer model is missing obvious context, and it's those times that I think we have to pay attention to.
The current industry adoption patterns seem to veering pretty close to "the computer did that auto-layoff thing" from Idiocracy in my opinion.