Agreed. But also his point about AGI is incorrect. AI that will perform on the level of average human in every task is AGI by definition.
"By AGI, we mean highly autonomous systems that outperform humans at most economically valuable work."
AWS: https://aws.amazon.com/what-is/artificial-general-intelligen...
"Artificial general intelligence (AGI) is a field of theoretical AI research that attempts to create software with human-like intelligence and the ability to self-teach. The aim is for the software to be able to perform tasks that it is not necessarily trained or developed for."
DeepMind: https://arxiv.org/abs/2311.02462
"Artificial General Intelligence (AGI) is an important and sometimes controversial concept in computing research, used to describe an AI system that is at least as capable as a human at most tasks. [...] We argue that any definition of AGI should meet the following six criteria: We emphasize the importance of metacognition, and suggest that an AGI benchmark should include metacognitive tasks such as (1) the ability to learn new skills, (2) the ability to know when to ask for help, and (3) social metacognitive abilities such as those relating to theory of mind. The ability to learn new skills (Chollet, 2019) is essential to generality, since it is infeasible for a system to be optimized for all possible use cases a priori [...]"
The key difference appears to be around self-teaching and meta-cognition. The OpenAI one shortcuts that by focusing on "outperform humans at most economically valuable work", but others make that ability to self-improve key to their definitions.
Note that you said "AI that will perform on the level of average human in every task" - which disagrees very slightly with the OpenAI one (they went with "outperform humans at most economically valuable work"). If you read more of the DeepMind paper it mentions "this definition notably focuses on non-physical tasks", so their version of AGI does not incorporate full robotics.
General-Purpose (Wide Scope): It can do many types of things.
Generally as Capable as a Human (Performance Level): It can do what we do.
Possessing General Intelligence (Cognitive Mechanism): It thinks and learns the way a general intelligence does.
So, for researchers, general intelligence is characterized by: applying knowledge from one domain to solve problems in another, adapting to novel situations without being explicitly programmed for them, and: having a broad base of understanding that can be applied across many different areas.
If something can be better than random chance in any arbitrary problem domain it was not trained on, that is AGI.
The implication here is that they excel at things that occur very often and are bad at novelty. This is good for individuals (by using RLMs I can quickly learn about many other aspects of human body of knowledge in a way impossible/inefficient with traditional methods) but they are bad at innovation. Which, honestly, is not necessarily bad: we can offload lower-level tasks[0] to RLMs and pursue innovation as humans.
[0] Usual caveats apply: with time, the population of people actually good at these low-level tasks will diminish, just as we have very few Assembler programmers for Intel/AMD processors.
Find me one that can solve it entirely in their head without touching the actual thing and externalizing state.
Since there's not really a whole lot of unique examples of general intelligence out there, humans become a pretty straightforward way to compare.
No so unconventional in many cultures.
In this case, I was thinking of unusual beliefs like aliens creating humans or humans appearing abruptly from an external source such as through panspermia.
If somebody claims "computers can't do X, hence they can't think". A valid counter argument is "humans can't do X either, but they can think."
It's not important for the rebuttal that we used humans. Just that there exists entities that don't have property X, but are able to think. This shows X is not required for our definition of "thinking".
But yes, you’re right that software needs not be AGI to be useful. Artificial narrow intelligence or weak AI (https://en.wikipedia.org/wiki/Weak_artificial_intelligence) can be extremely useful, even something as narrow as a services that transcribes speech and can’t do anything else.
Or perhaps AGI should be able to reach the level of an experienced professional in any task. Maybe a single system can't be good at everything, if there are inherent trade-offs in learning to perform different tasks well.
It's surprisingly simple to be above average in most tasks. Which people often confuse with having expertise. It's probably pretty easy to get into the 80th percentile of most subjects. That won't make you the 80th percentile of people that do the thing, but most people don't. I'd wager 80th percentile is still amateur.
But only the limited number of tasks per human.
> Or perhaps AGI should be able to reach the level of an experienced professional in any task.
Even if it performs just better than untrained human but on any task this will be superhuman level. As no human can do it.
Models still have extreme limits relative to humans. Context size and reasoning depths, being the two most obvious. A third being their inability to incorporate new information with as little effort as humans do, without creating unintended conflicts across previously learned information.
But they vastly exceed human capabilities in other ways. The most obvious, being their ability to do shallow reasoning incorporating information from virtually any combination out of the vast number of topics that humans find useful or interesting. Another being their ability to by default produce discourse with such high written organization and grammatical quality.
For now, they are artificial "better at different things" intelligences.