Prednet develops consciousness? On Wed, Jun 19, 2019, 06:51 Alan Grimes via AGI <agi@agi.topicbox.com> wrote:
> Yay, it seems peeps are finally ready to talk about this!! =P > > Lets see if I can fool anyone into thinking I'm actually making sense by > starting with a first principles approach... > > On first approximation, yer computer's memory is just a giant string of > charactors. Since everything you have ever done on your computer has > been encoded into this string, we can be reasonably confident that this > is a universal way to encode anything, so we go ahead and say that all > the modalities (sight, sound, etc,) are mapped to this memory and > continue on. > > [I'm maximally overtired right now, I didn't really get any sleep last > night, should wait until tomorrow at least to write this but I want to > get a head start...] > > If you prefer, you can think in terms of a multi-tape turing machine, > where the program and working variables are stored on other tapes and > the mind file is in main memory, but the point is that we presume there > exists a computation that does intelligence using the main memory, which > we will say is the complete state of the mind. > > So therefore what we are looking for is a set of principles and basic > operations that, when applied to the main memory, intelligent behavior > and subjective experience emerges. > > [next evening] > > The most important thing to realize is that the data is NOT > RANDOM/stochastic/whatever, it is STRUCTURED,?? It has patterns, it has > types, it may have meaning.?? This is true of all modalities. > > Therefore, in order to create a mind, we need the system to be able to > capture that structure and be able to use it in computations. Lets say > that we had some identified structure in the data which had some modest > but non-trivial k-complexity. Replacing that data with the K-complexity > description, even if if were a "good enough" approximation, would > satisify most definitions of understanding that structure and creating > some way to point to that description and incorporate it in other > descriptions would satisfy the definition of abstraction. > > I hope you are all boned up on your study of non-standard computational > models. What I'm getting at is that the expression of the K-complexity > is a structured object just like the structured object which it expands > to. So we can treat it the same way and approximate it using the basic > functions we use to manipulate abstractions. > > So if you had a string/object that basically meant "make big" and > another that expressed the shape, then you only need a way to apply one > to the other to make a big shape. It is important that things are done > like this because it is how general intelligence is achieved, because it > allows the operation as well as the object to be learned by essentially > the same mechanism. > > Now, let me shift gears here and go into a thread that I'd been meaning > to start for the past week or so. DEEP LEARNING IS DEAD! If we don't > have a viable replacement for it in the next 18 months or so, we're at > risk of another AI winter. =0???? There are certainly some good and useful > machine learning algorithms out there, some of which are just > rediscovery of techniques from ages past, but well that's OK too. > > When you get down to it, DL is the realization of ideas dating back a > very long time but only made feasible with gpGPU techniques, see > Nvidia's website for a recounting of the history. > > [few days later] > > Have a look at this and count up the number of problems this field of > study has from an AGI perspective: > > https://scholar.google.com/scholar?q=neural+architecture+search&hl=en&as_sdt=0&as_vis=1&oi=scholart > > Neural nets are brittile in that you can only select their topology when > you are creating them. (iirc) > > The brain, by contrast is quite infamous for exhibiting plasticity in > the face of dammage and/or changing requirements. For example, london > taxi drivers develop an enlarged hypocampus to help deal with the > maze-like roads in that city. > > https://www.youtube.com/watch?v=UoJf_tXU2Zk > > Ok, that video was ammusing and little else. ;) > > Also, consider what a "golden network" would look like. A golden network > is a network like a universal turing machine that exhibits general AI > instead of some special AI. > > The bottom line is that while we have come a long way from 2012, we are > reaching the top of the S-curve with respect to deep learning. Once the > RoI falls below some critical level, we will enter an AI winter. =| > > We do not have time for another winter. > > Deep learning is also hitting a computational complexity wall but I > couldn't find good referances for that so I left it out of this post. > > Here's something else to think about. > > Consider the near photorealistic graphics that you got with Assassin's > Creed on the Playstation 3. > > Consider the amount of power people are throwing at autonomous driving > and just recognising the general outlines of visual scenes. > > One of the reasons I flog prednet so much, > > https://coxlab.github.io/prednet/ > > is not just because it yields consciousness, but it reduces perception > to something a PS3 could do. > > You render what you think you are seeing, the LGN of the thalamus > computes the error signal, and then you use your expensive complex > computations or just that error signal! > > [midnight a day or two later, just jamming random related topics into > the post to make myself look smart...] > > Here's another fascinating area of study: > > https://scholar.google.com/scholar?q=explainable+neural+networks&hl=en&as_sdt=0&as_vis=1&oi=scholart > > To me, it's just proof that deep learning, despite what can be > accomplished with it, is NOT a form of intelligence. When you think > about it, it doesn't learn in any true sense, in that it can recite > facts, or recall events but it, over many thousands of iterations, can > *adapt*. > > The result is a system that essentially operates as mindlessly as a > reflex. The apparent complexity of the behavior not changing that fact. > > Going to a recurrent model does give the network a legit memory, after a > fassion. Still, the prospects for further progress is dim. > > [next night around midnight again] > > Consider, by contrast a system that works by analyzing structure. There > is no reason why it can't simply continuallly fill its memory, because > each thing is recorded as a representation of it's structure, the > storage requirement for even millenia of ordinary life experience is not > at all implausible. > > Ok, the magic sauce here is an ability to organize and catalog > representations. I think there exists a geometric technique that can > yield pattern recognition. This would involve a way to normalize the > input and then search the database, the transformation used to achieve > the normalization also being a relevant structure that is also analyzed > in the same manner. > > Other tricks such as the space-time rotation that I've described a > number of times are also important techniques for detecting spatial > patterns. I think that approach can be much more powerful than > "convolution" which is just scanning a larger matrix with a smaller... > I'm talking about taking the visual scene and turning the scan of it > into a temporal signal like a sound. Just as a speech recognizer doesn't > care when the speaking actually begins, treating spatial signals can > work the same way. > > Anyway, what was this post supposed to be about again? bleh -> hitting > send. > > -- > Please report bounces from this address to...@numentics.com > > Powers are not rights. > ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T395236743964cb4b-M686d9fcf7662ad8dc2fc1130 Delivery options: https://agi.topicbox.com/groups/agi/subscription
