But we consider humans intelligent.
But we consider humans intelligent.
That some tech people think that human intelligence can be reduced to such "textural mechanics" betrays a lack of depth of understanding and even appreciation of the deep and complex world within which we find ourselves. Our written corpus is but a particular reflection of this reality - the shadows on the wall in Plato's cave if you will.
Can you expand on that a little?
Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations.
Which is more probable?
a) Linda is a bank teller. b) Linda is a bank teller and is active in the feminist movement.
It is an example of lack of mathematical (really probability) know how, not naïve pattern matching.
Obviously, people rephrase the question into : is there more chance that she's a feminist or that she isn't ?
The fact is, you can infer things about people based on things you know about people. I can pick a random user on HN, and knowing they're a user of HN, I can say it's probable that they work in technology. We don't need to bring statistics into it and turn it into a math problem.
a) Hank has written us a human interest problem b) Hank has written us a human interest and a probability problem.
I don't think people are simply wrong about the Linda problem, I think they're imprecise about which question they're answering, and more or less think they're answering a question about what chances that Linda is a feminist vs what are the chances she's a bank teller not only using the givens+relevant priors about people but also their priors about what kind of question they're answering. It isn't "no real reasoning", it's just not high resolution enough to be technically correct by the standards of a constructed probability problem.
You can argue LLMs are also not quite high resolution enough and I'd accept that. In my mind the question is what it would take to get some kind of ML software to a place where if you trained it on enough probability problems it would be able to evaluate the Hank problem above, including the issue of whether (a) and (b) are actually independent. ;)
I, like most people intuitively answered b). Given the explanation on the Wikipedia page I went "oh, of course, yeah", but then I thought about why I'd answer b) given that I'm fairly familiar with basic probability.
If you give me two options and ask me to pick between them, my brain is usually going to assume it's not a trivially true problem.
Language needs context for any sense to be made of it.
As a result of the above, reading the question, the intuitive reading makes the answer choices
a) Linda is a bank teller (implicitly, a bank teller NOT active in the feminist movement)
b) Linda is a bank teller and is active in the feminist movement.
This question is one of language, context, and interpretation, not of people failing to understand basic probability.
I suspect that if you prime people to excise interpretation of the question by presenting it as the following, the majority of people would guess correctly:
---
"Consider the following two statements:
1) Linda is a Bank Teller
2) Linda is active in the feminist movement
Which is more likely?
a) 1
b) 1 ^ 2"
a) Linda is a bank teller (and NOT active in the feminist movement) b) Linda is a bank teller and is active in the feminist movement
What you want it to be asking here is:
a) Linda is a bank teller, and may or may not be active in the feminist movement b) Linda is a bank teller, and is active in the feminist movement
Most college students have taken quite a few multiple-choice tests (particularly in the US, high-schools train for standardized multiple-choice tests). The question isn't asking what the mathematicians seem to think it's asking, because it's format conveys extra restrictions.