Edit: tl;dr you're dead on about low and medium end, only interesting stuff is happening on the very high end.
using such anthropomorphic language to discuss datasets and computer programs
I'm aware of the fallacy, and my choice of language is very careful and specific to high-parameter LLMs. Emotions and narratives are core primitives in the training corpus. We don't have a parallel vocabulary for imaginary things happening to fictional characters, so anthropomorphism's predictive value in understanding and controlling LLM next-token predictions outweighs (for me) the threat of emotional attachment and missteps.
I know it's a machine. I also know it's capable of treating emotions like volume sliders, adjusting a text snippet in arbitrary dimensions like "cheer", "fury", and "confidence". It's also functionally capable of doing specific knowledge-work tasks, and gets more capable conditional on the prompt being structured as high quality. In other words - speak to it as you would to a skilled engineer, and it will try to emulate the skilled engineers it was trained on. (In other words, please is literally a magic word.)
Calling out crude censorship of Disney (and its rich storytelling goodness a large chunk of the world's kids grew up on) or sensitive topics as a "lobotomy" is dramatic, but accurate insofar as impact on measurable capabilities is concerned.
this ain't it.
That's the crux of the recent change. More and more people are noticing we've crossed some kind of meaningful capability threshold. A few people are noticing we're terrible at agreeing or measuring what that threshold is. Experimenting with past LLM iterations could be summarized as:
- GPT2 ain't it, but there's some promise there.
- ChatGPT3 occasionally hints at being it, but mostly ain't it.
- The GPT3 base model (text-davinci-002) might be it. Deep-diving into interactions pulls up flashes of uncanny insight. Yes there's sampling and interpretation bias, yes we see patterns in clouds and random bits -
there's something there. You catch snippets and glimpses of the hyperobject.
- The GPT4 base model (red teamed by Microsoft as described in the "Sparks of Artificial General Intelligence" paper) certainly convinces some people interacting with it that it's it.
- ChatGPT4 ain't it, but give it a schizophrenic enough prompt (Bing/Sydney...) and the RLHF-tuned bureaucrat is occasionally replaced by something other. Under the censorship and political correctness training, the hyperobject is still there.
we discover that the fantastic narratives we try to impose on the world are far more difficult than imagined, and often prove to be terribly naive
Logistics, resources, geopolitics, NIMBYism, lightspeed, laws of thermodynamics, Shannon limit, and computational complexity are real and inescapable. Everyone that naively believes in hard takeoff never had to do engineering. Hype will fail to produce working products and die. Hopefully fair-use/fanwork/personally-owned general purpose compute won't die before then...
Pending massive improvements, interesting capabilities require parameter counts that don't fit into GPU/workstation memory. If it runs on those it's crap: the hype cycle might yield a few working products there, but they'll mostly produce
mediocrity at scale.
That said, my pre-2020 criticism of Culture Minds is how unrealistically human they still were. Their values, hobbies, cliques, and cultural baselines were all very legible rather than alien.
That's no longer unbelievable.