[emsign]

> The affordances of AI systems have the effect of eroding expertise, short-circuiting decision-making, and isolating people from each other.

This affordability is HEAVILY subsidized by billionaires who want to destroy institutions for selfish and ideological reasons.

[layer8]

I think you have misread the word “affordances”. It’s not about affordability [0]. The main text also explains what it means.

[0] https://en.wikipedia.org/wiki/Affordance

[DarkNova6]

This is literally corporate textbook 101. Subsidize your product, become market leader, cause lock-in and make your customers dependant.

Every large enough corporate wants to become the new Oracle.

[ahartmetz]

Given how nobody properly understands LLMs, I doubt that they are intentionally designed like that. But the effect... yeah. I can see that happening.

(By the way, are you confusing affordance, the UX concept, with affordability?)

[tyingq]

You can intentionally market the use cases without knowing exactly how they work, though. So it's intentional investment and use case targeting, rather than directly designing for purpose. Though, the market also drives the measures...so they iteratively get better at things you pour money into.

[esperent]

Nobody properly understands dog brains either and yet you can still train a dog to sit.

[ahartmetz]

If you just met a dog for the first time, you can't :) - my guess is LLMs are somewhere in between. It would be cool to see what happens if somebody tried to make an LLM that somehow has ethical principles (instead of guardrails) and is much less eager to please.

[chrisjj]

The stochastic parrot LLM is driven by nothing but eagerness to please. Fix that, and the parrot falls off its perch.

[xpe]

> The stochastic parrot LLM is driven by nothing but eagerness to please. Fix that, and the parrot falls off its perch.

I see some problems with the above comment. First, using the phrase “stochastic parrot” in a dismissive way reflects a misunderstanding of the original paper [1]. The authors themselves do not weaponize the phrase; the paper was about deployment risks, not capability ceilings. I encourage everyone people who use the phase to go re-read the paper and make sure they can articulate what the paper claims and be able to distinguish that from their usage.

Second, what does the comment mean by “fix that, and the parrot falls off the perch.”? I don't know. I think it would need to be reframed in a concrete direction if we want to discuss it productively. If the commenter can make a claim or prediction of the "If-Then" form, then we'd have some basis for discussion.

Third, regarding "eagerness to please"... this comes from fine-tuning. Even without it (RLHP or similar) LLMs have significant prediction capabilities from pretraining (the base model).

All in all, I can't tell if the comment is making a claim I can't parse and/or one I disagree with.

[1]: "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?" (Bender et al., 2021)

[wesammikhail]

The institutions have been doing a fine job of destroying all their credibility and utility all on their own for far longer than this new AI hype cycle.

ZIRP, Covid, Anti-nuclear power, immigration crisis across the west, debt enslavement of future generations to buy votes, socializing losses and privatizing gains... Nancy is a better investor than Warren.

I am not defending billionaires, the vast majority of them are grifting scum. But to put this at their feet is not the right level of analysis when the institutions themselves are actively working to undermine the populace for the benefit of those that are supposed to be stewards of said institutions.