It also doesn't help when the messaging is linked to how "there will be no jobs where you use your brain anymore everything will be automated". What motivation does the average 16 year old have to try hard and learn anything beyond what they immediately need.

No jobs, ai Jesus is coming, and if you use ai it will use all of the worlds compute power to try to convince you it's correct even when it's not.

A more robust measurement might be the (former) US Department of Education's "Adult Literacy in the United States" survey, most recently conducted in 2019. The results of this are sobering enough:

<https://nces.ed.gov/pubs2019/2019179/index.asp>.

There's a related study of adult technical literacy conducted in 33 OECD nations:

<https://www.oecd-ilibrary.org/education/skills-matter_978926...>.

Both show that only a small fraction (5--10%) of adults operate at high levels of literacy (whether of text, numeracy, or technology), and that a large fraction (roughly 50%) operate at a minimal or below-minimal rate.

True education? What idiot would say yes to this?

Even if you _know_ the debit card transaction is safe, there’s no reason to risk it when a weirdo is filming you with some wild contraption.

Anything like this is going to have a very heavy selection bias, don't take any of this kind of content as a reflection of the average person.

Many of of witnessed the technical literacy of the general population when we ran to show them ChatGPT 3.5, and they just kind of shrugged like "So? What are you showing me?"

To be fair, this should probably be covered by basic physics/maybe cooking classes. “You can’t determine the calories in food by looking at it” isn’t really ML specific.

Considering the lack of basic math skills I encounter each and every day, I don't think schools did enough; they certainly aren't going to do enough w/LLMs.

It's more complicated than a calculator. Even researchers who have dedicated their lives to the field don't know all of the limitations of any given model. That fact alone isn't helpful when a model is 80% correct in one area but 2% in another.

Fwiw invoking Dunning-Kruger is beyond trite at this point.

Which is a good thing because it means we can talk like normal humans ("people don't know that it's unreliable") instead of acting like we're making such a profound claim that it needs a citation and psychological dissection.