Exploring Generative Pre-Training and Human-Level Performance in Modern Transformers

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Modern Transformers are AGI, and Human-Level, published by Abram Demski on March 26, 2024 on The AI Alignment Forum.
This is my personal opinion, and in particular, does not represent anything like a MIRI consensus; I've gotten push-back from almost everyone I've spoken with about this, although in most cases I believe I eventually convinced them of the narrow terminological point I'm making.
In the AI x-risk community, I think there is a tendency to ask people to estimate "time to AGI" when what is meant is really something more like "time to doom" (or, better, point-of-no-return). For about a year, I've been answering this question "zero" when asked.
This strikes some people as absurd or at best misleading. I disagree.
The term "Artificial General Intelligence" (AGI) was coined in the early 00s, to contrast with the prevalent paradigm of Narrow AI.
I was getting my undergraduate computer science education in the 00s; I experienced a deeply-held conviction in my professors that the correct response to any talk of "intelligence" was "intelligence for what task?" -- to pursue intelligence in any kind of generality was unscientific, whereas trying to play chess really well or automatically detect cancer in medical scans was OK.
I think this was a reaction to the AI winter of the 1990s. The grand ambitions of the AI field, to create intelligent machines, had been discredited. Automating narrow tasks still seemed promising. "AGI" was a fringe movement.
As such, I do not think it is legitimate for the AI risk community to use the term AGI to mean 'the scary thing' -- the term AGI belongs to the AGI community, who use it specifically to contrast with narrow AI.
Modern Transformers[1] are definitely not narrow AI.
It may have still been plausible in, say, 2019. You might then have argued: "Language models are only language models! They're OK at writing, but you can't use them for anything else." It had been argued for many years that language was an AI complete task; if you can solve natural-language processing (NLP) sufficiently well, you can solve anything. However, in 2019 it might still be possible to dismiss this.
Basically any narrow-AI subfield had people who will argue that that specific subfield is the best route to AGI, or the best benchmark for AGI.
The NLP people turned out to be correct. Modern NLP systems can do most things you would want an AI to do, at some basic level of competence. Critically, if you come up with a new task[2], one which the model has never been trained on, then odds are still good that it will display at least middling competence. What more could you reasonably ask for, to demonstrate 'general intelligence' rather than 'narrow'?
Generative pre-training is AGI technology: it creates a model with mediocre competence at basically everything.
Furthermore, when we measure that competence, it usually falls somewhere within the human range of performance. So, as a result, it seems sensible to call them human-level as well. It seems to me like people who protest this conclusion are engaging in goalpost-moving.
More specifically, it seems to me like complaints that modern AI systems are "dumb as rocks" are comparing AI-generated responses to human experts. A quote from the dumb-as-rocks essay:
GenAI also can't tell you how to make money. One man asked GPT-4 what to do with $100 to maximize his earnings in the shortest time possible. The program had him buy a domain name, build a niche affiliate website, feature some sustainable products, and optimize for social media and search engines. Two months later, our entrepreneur had a moribund website with one comment and no sales. So genAI is bad at business.
That's a bit of a weak-man argument (I specifically searched for "generative ai is dumb as rocks what are we doing"). But it does demonstrate a pat...