Comment by echelon
7 days ago
My problem with AGI is the lack of a simple, concrete definition.
Can we formalize it as giving out a task expressible in, say, n^m bytes of information that encodes a task of n^(m+q) real algorithmic and verification complexity -- then solving that task within a certain time, compute, and attempt bounds?
Something that captures "the AI was able to unwind the underlying unspoken complexity of the novel problem".
I feel like one could map a variety of easy human "brain teaser" type tasks to heuristics that fit within some mathematical framework and then grow the formalism from there.
>My problem with AGI is the lack of a simple, concrete definition.
You can't always start from definitions. There are many research areas where the object of research is to know something well enough that you could converge on such a thing as a definition, e.g. dark matter, consciousness, intelligence, colony collapse syndrome, SIDS. We nevertheless can progress in our understanding of them in a whole motley of strategic ways, by case studies that best exhibit salient properties, trace the outer boundaries of the problem space, track the central cluster of "family resemblances" that seem to characterize the problem, entertain candidate explanations that are closer or further away, etc. Essentially a practical attitude.
I don't doubt in principle that we could arrive at such a thing as a definition that satisfies most people, but I suspect you're more likely to have that at the end than the beginning.
one of those cases where defining it and solving it is the same. If you know how to define it then you've solved it.
After researching this a fair amount, my opinion is that consciousness/intelligence (can you have one without the other?) emerges from some sort of weird entropy exchange in domains in the brain. The theory goes that we aren't conscious, but we DO consciousness, sometimes. Maybe entropy, or the inverse of it, gives way to intelligence, somehow.
This entropy angle has real theoretical backing. Some researchers propose consciousness emerges from the brain's ability to integrate information across different scales and timeframes. This would essentially create temporary "islands of low entropy" in neural networks. Giulio Tononi's Integrated Information Theory suggests consciousness corresponds to a system's ability to generate integrated information, which relates to how it reduces uncertainty (entropy) about its internal states. Then there is Hammeroff and Penrose, which I commented about on here years ago and got blasted for it. Meh. I'm a learner, and I learn by entertaining truths. But I always remain critical of theories until I'm sold.
I'm not selling any of this as a truth, because the fact remains we have no idea what "consciousness" is. We have a better handle on "intelligence", but as others point out, most humans aren't that intelligent. They still manage to drive to the store and feed their dogs, however.
A lot of the current leading ARC solutions use random sampling, which sorta makes sense once you start thinking about having to handle all the different types of problems. At least it seems to be helping out in paring down the decision tree.