Some men have told me I was crazy to pass up the “opportunity”. And some women have told me that I was a “gentleman” for doing so.

I don’t know why men are so awful sometimes. But I think a big part of the problem is that taking advantage of women is normal and accepted.

The term “rape culture” is often misunderstood, lampooned, etc. but that’s a really good distillation of it. SA is not “encouraged” in a legal sense, it’s punished, but there is a cultural expectation of men to be uncontrollably sexually aggressive while women are supposed to “defend their virtue”.

If men aren’t sufficiently sexually assertive, they are often seen as less masculine. This isn’t always the case - it can support the conception of masculinity as being chivalrous. But can also be viewed as being more passive, less virile, less strong, etc.

If women are perceived not successfully “defending their virtue”, it’s seen as a failure on their part except sometimes in extreme cases of sexual violence if seen as being “unprovoked”. Hence the “look what she was wearing”, “asking for it”, etc. etc. tropes. These are often less overt now, which causes people to overlook the extent to which they blame women for being victims of SA.

Rape culture - it’s defo a thing and that’s it.

And so… would you want to block all israeli people? That’d be the analagous situation. What is it you’re trying to say?

If it’s just what you want on your own app that’s fine, of course - to each their own.

In a functioning society maybe that’s how it’d work. In our society? No it’s basically just a way to funnel our tax dollars to corporations which in turn are incentivized to extract maximum profit from those same taxpayers without giving any ownership to the government representing those taxpayers.

Society has changed a lot since air travel was invented

It’s almost like airlines don’t make sense as for-profit businesses. The margins are razor thin, they go out of business regularly, they are vital for society to function… so who does it benefit for airlines to be run for profit? Nationalize em.

Question: where do you get duck fat? Or do you just cook a lot of ducks on the reg

Apparently sold for $4.5M last, per Redfin. Less than I’d have thought.

Say you had a version of chat GPT that hadn’t been trained on a corpus that included enough information about the number of Rs the word “strawberry” contains, didn’t have workarounds for character counting, and has context sharing.

You ask it “How many Rs does Strawberry contain?”

It burns a bunch of tokens, returns “Strawberry has 7 Rs. No wait, that’s not right, strawberry has 5 Rs. No wait…” etc.

You say “Strawberry has 3 Rs. How many Rs does Strawberry have?”

It says “Strawberry has 3 Rs.”

You ask “How many Rs does this exact string contain: ‘Strawrberrrry’?”

A new session using shared context responds “Strawberry has 3 Rs.”

You say “No, it has a different number of Rs. I didn’t ask about Strawberry this time”

It responds “Sorry about that! You didn’t ask about Strawberry, you asked about Strawrberrrry. Strawrberrrry has 7 Rs. No wait, that’s not right. Strawrberrrry has 5 Rs…”

LLMs, at their core, use models that have computed lexical & semantic similarity to predict words (really they perform contextualized vector transformations, but let’snot get into that). When they “learn”, they just do this more effectively using more text or using the same text more efficiently. Next word prediction is not the same thing as learning concepts, like the concept of numbers, non-numerical things being ascribed numerical values (ironically, since LLMs function by turning words into numerical vectors), or counting.

When it responds “Strawberry has 3 Rs” it isn’t because it knows what that means or how to gain that knowledge about other words. It is merely parroting back what you’ve told it because “Strawberry has 3 Rs” in its shared context has very close lexical similarity to your query “How many Rs does Strawberry have?”. It also parses Strawberry as [“straw”, “berry”] encoded into numerical values representing their relationship in the corpus’ vector space (e.g. {[ .420, -.67, .67], [.420, .69, -.69]}) - so unless it has instructions to further break those tokens into characters, it does not have the capability to count. You can actually do some weird math using these vectors and their relationships (their distance apart in the 3D vector space, angles between vectors, etc actually relate to the semantic content of the tokens), but you lose granularity such as the number of letters in a token when you look at words this way.

Learning is more than just computing lexical similarity. Modern LLMs mimic reasoning by expanding the query with intermediary tokens, basically creating a temporary scratch pad of related words on the same vector, but don’t actually reason (this is known as chain of thought). Learning is partly computing lexical similarity, but it is also about extrapolating concepts from facts and inferences, applying those concepts to novel simuli, etc. LLMs really don’t do reasoning, which is part of learning.

That is sort of true, but it still isn’t learning. Basically, it can store information usable by other sessions. But to do so, it just processes that information the same way as a prompt. So less like learning and more like just tacking something on to its context window. So, it never “learns” that information, it just tells itself that thing every time it processes any prompt.

(don’t downvote MartianSands’ comment - it’s a good point. Shame on you)

2. is an insanely fast learner

This person very clearly does not understand, even at a basic level, how llms work. They are completely incapable of “learning” outside of training, and training cannot happen while you are using them - in no small part because they require MILLIONS of tokens of data (minimum) to be able to recognize patterns.

Meanwhile that theoretical stupid kid she thinks should be paying her masters to have her teach them can learn instantaneously from even a single datapoint.