Deep image prior 'learns' on just one image

We need more than just the information where corrupted pixels are.

For example, consider an image of a woman, severly blurred such that her individual fingers cannot be discerned but you can still see that it's a woman. A "learned" context-aware approach can now deduce that the "blob" that is her hand should be inpainted by fingers. A non-learned approach can't do that.

> Even current ("real") intelligence can do that.

No it can't. A biological creature's output is not deterministic from its input and can be sensitive to conditions not anticipated at-programming-time. That isn't understanding.

A spinning top may spin on many surfaces not anticipated by the designer and acquire all sorts of interesting behaviors by doing so.

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Many of these tests you're outlining aren't relevant to the question "does this biological creature have what we are interested in". Ie., "is this piece of lead actually gold". Not "is it shiny with a brass coating" -- but can it participate in all the causal interactions gold can. I'm not concerned with how good the tool is, or how close we are to fooling people, i'm concerned with whether the biological creature can think.

Does the biological creature posses any concept? Any idea? Any understanding?

No, only metaphorically. It seems as-if it does to people who use it to aid in their understanding. It is only a trick, no more than the sun being ascribed agency by ancient human being.

Intelligences which we're targeting do not possess concepts. They are not meaningfully connected to their environment. They don't understand it. The dog's finding its bone is just the same as the spinning top find its grove. The spinning is only much more intricate, and the nature of and interaction with the surface much less easily understandable.

The dog experiences illusions of thoughts, concepts, ideas, imagination (and many other things besides) that are about its environment. Those have been caused by the nature of its environment. The spinning top topples towards its final point as-if it understood, just like the dog.

Biological creatures are rivers of biochemical and electrical currents that topple toward and outcome that it sensitive to their current state, like a top spinning about a board. They have no active, navigating, motivated concernful goal-directed action.

My view is that they never will, since on all the best evidence, skillful concernful action does not exist. It is merely illusions that emerge from chemical and electrical interactions.

It is not obviously clear whether your argument is any more valid than the above (or the other way around).

To expand, if a robot in every imaginable way behaves exactly like another human would, how can you know know that one possesses "intelligence" (or rather, consciousness), while the other doesn't?

Why would it be possible to construct the high level structures from which intelligence emerges on top of one set of primitives (electrical/chemical in biological context), and not the other (electrical-based logic on silicon).

The argument you pose have many signs of being an appeal to the ghost in the machine.

For anyone interested in exploring ideas around (self)consciousness and the mind, The Mind's I by Douglas Hofstadter and Daniel C. Dennett is a good read.

This is interesting news to neuroscientists as well. Even without plasticity cortical networks might be performing useful functions. Plasticity may then perform some different function.

But the fingers it draws will be random fingers. Not her fingers.

Since a convolutional network applies parameters (filters) to different parts of an image evenly, this means that this approach still learns, except not from a corpus of images, but from the image itself (i.e. one part of the network learns from different parts of the image).

> This makes me wonder whether and to what degree the structure of the brain's connectome might be a necessary prior for AGI.

Convnets have been invented to speed up learning (reusing parameters). The human brain (afaik) doesn't contain such speedup. Instead it relies on massive parallellism of neurons, and thus trains all "filters" separately instead of reusing them. Therefore, I suppose this approach would not apply to humans.