Live data from Hacker News

Deep image prior 'learns' on just one image

dmitryulyanov.github.io

151–160 of 235 posts

Re: Deep image prior 'learns' on just one image

#151

Earlier quoted context omitted.

To throw a dissenting voice into the mix: I do not think intelligence is a consequence of structure. Transparency is a consequence of how light interacts with objects. There is no "transparent gold". And being transparent is not something we can program gold to do. Programming is the application of an electric field across a silicon surface: this can no more transmute silicon into gold as it can into a nervous system…

Where do you think human consciousness comes from?

I think all consciousness (a feature of most animals) is a specific kind of neuro-bio-chemical process.

To put it another way, to improve your focus I can give you Modafinil. To stabilize your mood lithium. To give you hallucinations, lsd. To distort your visual perception, ketamine. To make you more empathetic, trusting and less hostile: mdma. To speed up your thinking, cocaine. To make you aggressive, too much cocaine.

And this applies across the animal kingdom, in large part.

Where in this list is a program?

A program source is a description. A running programming is an electrical field across a piece of silicon. A description has no causal effect (the mere act of writing a description down does nothing). An an electrical field across a piece of silicon has effects, but none meaningful on consciousness.

Consciousness is not hiding somewhere. It takes only a baseball bat to deprive someone of it. And its various parameters are easily enough mapped about by chemical manipulation.

The modern (general/strong) AI sort follow in the tradition of Descartes who imagined that consciousness was its own sort of substance apart from the body. That its effects and structure were idealizable. Whether we call this idealization a "soul" or a "program" seems to make no odds to the mistake being made...

Consciousness is something animal bodies do. It is not a pattern in the sand, or a current in a wire. An certainly not a soul, a number, a program (which is only a number) or any other idealized abstraction.

To be even more explicit: no, you will not be able to upload your consciousness. The suggestion and its homology to heaven ought concern anything modern thinking scientist.

A description of water (H20) is not some water. I can no more upload a drink to amazon, than I can upload a thought you are thinking. A thought is a biochemical reaction. A description of a thought, even if very accurate, doesnt think. The internet may one day hold very detailed descriptions of people's brains. These will sit like textbooks and tombs though, and not care/wish for/consider/want/desire/understanding anything.

Re: Deep image prior 'learns' on just one image

#153
post #85

I don't see how any choice of a function g(theta) could have the property they desire, ie could eliminate R(g(theta)). Can anyone explain?

It's expressed somewhat awkwardly, but what's going on is that R(x) is zero if x is in range of g, and infinite otherwise. Choice of g is such that natural images are in range of g, and non-natural images aren't.

Sorry I still don't understand. They require g to be surjective. Edit: in their paper they call it f_theta and it's explicitly not surjective. Dunno why their writeup is so confused.

Re: Deep image prior 'learns' on just one image

#154
post #127
post #32

Earlier quoted context omitted.

Thanks. I'm not mixing them up! I'm just wondering whether and to what degree architecture , i.e., network structure, will prove important for other, more advanced AI tasks, including up to AGI.

Although probably not sufficient for AGI, network architecture is essentially guaranteed to be important, because of both ample empirical evidence of the importance of architectures and ample reason, from facts about numerics, to believe that it is important. In the first category (empirical evidence), - The discrete leap from non-LSTM RNN to LSTM network performance on NLP was essentially due to a "better factoring…

Thanks. I agree. The anecdotal evidence suggests that architecture is indeed important.

This paper is the only direct evidence I've seen of it, though.

Great work. Compelling.

Re: Deep image prior 'learns' on just one image

#155

Earlier quoted context omitted.

> Even current AI can do that. No it can't. A machine'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. "The opposite of consciousness" is not a meaningful criterion of interest. I'm not even su…

> 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. [...] Many of these tests you're outlining aren't relevant…

What you've done here is expose an epistemological problem but not the ontological one under consideration.

My ontological premises are: consciousness exists and it is a neurochemical process.

From this follows: machines which are not instances of this process are not conscious.

Now you have basically said: but we do not know for certain that it is this neurobiochemical process that accounts for consciousness. Isnt it reasonable to suppose that we might encounter things not actually conscious and think they are? YES. Isnt it reasonable, therefore, to suppose that "consciousness" isnt actually ontologically the same thing as this neurobiochemical process? NO!!

The magnitude of human foolishness is no guide to what exists and what it is like: our being able to be fooled by anything tells us little.

Yes, we can reverse the picture for the spinning-top and dog. But this is an epistemic reversal: we can suppose we are being fooled by the dog, but the spinning top is the truth!

This seems highly unlikely for reasons basically summarized as, "science works".

The spinning top isn't thinking and the dog is. That it is possible to doubt this claim, ie., that it fails to be certain, tells us nothing. Almost all scientific claims fail to be certain, that's neither here nor there as to whether they are accurate.

And I am appealing to no ghosts to draw distinctions between dogs and spinning tops. I am appealing to a reasonable scientific inference: let us modify the dogs behaviour and let us modify the spinning tops. Cocaine might well be involved in the former, or neurosurgery -- and in the latter, wood carving.

The distinction between a chisel and cocaine is hopefully clear enough: they act on their target objects in extremely different ways. The way that cocaine acts informs what stuff I take to comprise "consciousness" -- that is how the behaviour of the dog is modified.

> skillful concernful action does not exist. It is merely illusions

I dont know what you mean by "illusion" here. I don't see why the observation that skilllful actions is a bodily process somehow diminishes its reality. Skillfull action is just what people do in order to achieve their goals, we have discovered all of these things are biochemical but that doesnt make them fake.

If we observe the target phenomenon "skillful action" we discover is it biological. This rules out silicon and electric doing it. Or, in other words, to modify the behaviour of a machine I cannot use cocaine. It has no thoughts to disrupt. I'd have more luck with a chisel.

Re: Deep image prior 'learns' on just one image

#158
post #32

Earlier quoted context omitted.

Thanks. I'm not mixing them up! I'm just wondering whether and to what degree architecture , i.e., network structure, will prove important for other, more advanced AI tasks, including up to AGI.

To throw a dissenting voice into the mix: I do not think intelligence is a consequence of structure. Transparency is a consequence of how light interacts with objects. There is no "transparent gold". And being transparent is not something we can program gold to do. Programming is the application of an electric field across a silicon surface: this can no more transmute silicon into gold as it can into a nervous system…

> Transparency is a consequence of how light interacts with objects. There is no "transparent gold". And being transparent is not something we can program gold to do.

Your remark made me research this, and apparently transparent gold exists, we can create it, and you can buy it [1]. The trick appears to be making the gold thin enough.

> Line up everything in the known universe that is conscious and you will find that it engages in a highly specific biochemical activity that requires a whole set of highly specific chemical interactions.

Line up everything in the known universe that engages in this highly specific biochemical activity, and you'll find a whole lot of things that most people wouldn't call "conscious". So the biochemical activity can't be enough on it's own to create "consciousness". If you compare those "unconscious" organisms with "conscious" ones, you'll find that that the "conscious" organisms have a large amount of cells with this biochemical activity arranged in an intricate network structure.

Now why do you think it's not this structure that creates "consciousness"? Why do the details of the biochemical activity matter beyond being a particular physical realization of some differential equations? Why would a machine made out of silicon, when it precisely emulates the behavior of that biochemical activity, be unable to replicate it's large-scale properties (including "consciousness")?

[1] http://www.reynardcorp.com/products/optical-components/coati...

Re: Deep image prior 'learns' on just one image

#159

Earlier quoted context omitted.

To throw a dissenting voice into the mix: I do not think intelligence is a consequence of structure. Transparency is a consequence of how light interacts with objects. There is no "transparent gold". And being transparent is not something we can program gold to do. Programming is the application of an electric field across a silicon surface: this can no more transmute silicon into gold as it can into a nervous system…

Where do you think human consciousness comes from?

People are conscious (actually self-aware?) because we say we are. Which isn't actually evidence. I'm doubtful there's anything to explain.

Re: Deep image prior 'learns' on just one image

#160
post #21

Wow: "In this work, we show that, contrary to expectations, a great deal of image statistics are captured by the structure of a convolutional image generator rather than by any learned capability. This is particularly true for the statistics required to solve various image restoration problems, where the image prior is required to integrate information lost in the degradation processes. To show this, we apply untrain…

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 be…

Another way to think of it:

Convnets are a method to speed up learning by sharing filter-coefficients. In other words, one could just as well train the network by replacing the convnets by fully-connected layers, except that of course training would take a lot longer.

If one views convnets as purely a computational optimization, then the approach of the paper can be considered a "hack" that only works because of this optimization.

Post reply on HN