One of the most common objections we hear when talking about artificial general intelligence (AGI) is that “AGI is ill-defined, so you can’t really say much about it.”
In an earlier post, I pointed out that we often don’t have precise definitions for things while doing useful work on them, as was the case with the concepts of “number” and “self-driving car.”
Still, we must have some idea of what we’re talking about. Earlier I gave a rough working definition for “intelligence.” In this post, I explain the concept of AGI and also provide several possible operational definitions for the idea.
The idea of AGI
As discussed earlier, the concept of “general intelligence” refers to the capacity for efficient cross-domain optimization. Or as Ben Goertzel likes to say, “the ability to achieve complex goals in complex environments using limited computational resources.” Another idea often associated with general intelligence is the ability to transfer learning from one domain to other domains.
To illustrate this idea, let’s consider something that would not count as a general intelligence.
Computers show vastly superhuman performance at some tasks, roughly human-level performance at other tasks, and subhuman performance at still other tasks. If a team of researchers was able to combine many of the top-performing “narrow AI” algorithms into one system, as Google may be trying to do, they’d have a massive “Kludge AI” that was terrible at most tasks, mediocre at some tasks, and superhuman at a few tasks.
Like the Kludge AI, particular humans are terrible or mediocre at most tasks, and far better than average at just a few tasks. Another similarity is that the Kludge AI would probably show measured correlations between many different narrow cognitive abilities, just as humans do (hence the concepts of g and IQ): if we gave the Kludge AI lots more hardware, it could use that hardware to improve its performance in many different narrow domains simultaneously.
On the other hand, the Kludge AI would not (yet) have general intelligence, because it wouldn’t necessarily have the capacity to solve somewhat-arbitrary problems in somewhat-arbitrary environments, wouldn’t necessarily be able to transfer learning in one domain to another, and so on.
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