Sergio: And no, there is no limit on time of execution either. This is still unpublished, so I can only give you a hint. Assuming a neural-network computer simulation where each element of the causal set is represented by exactly one individual neuron, and assuming near-neighbor coupling, the time of execution is constant and independent of size. This is massive parallelism. I am just curious, are you still with me? May I ask a quiz to verify? You don't have to answer, the answer is below. Aside from the obvious fact that this is going to be fast, what is the real, profound significance of this result?
Jim: I am not able to understand why this is truly relevant to solving the contemporary problem of AGI. In my opinion, massive interrelations seem like a necessity and combinatorial complexity the problem. Jim Bromer On Fri, Aug 17, 2012 at 12:40 PM, Sergio Pissanetzky <[email protected] > wrote: > Jim,**** > > ** ** > > JIM> I am listening to you. However, I am very skeptical. **** > > SERGIO> Fair enough. **** > > ** ** > > ** ** > > JIM> So far you haven't explained anything other than a few ideas that are > interesting but do not constitute convincing evidence. **** > > SERGIO> It seems to me that you are better accostumed to a top-down > approach, where you start from complexity and try to simulate it. My > approach, instead, is constructivist, and this may be the source of your > feeling that I haven't explained anything yet. I start from simplicity and > build up. I try to explain how complex things come to be. My foundation are > the 4 fundamental principles of nature. I was trying to explain this first > and tell you how I got there. I tried to start from the Schroedinger's cat > example, but it didn't work very well, so I tried something else. **** > > ** ** > > Of course the two approaches, bottom-up and top-down, must meet somewhere. > Frister's approach is bottom-up, and he is meeting the top-down > observational results. Mine is more fundamental than his, so his work makes > mine a lot easier: now my top is the bottom he's working from (in these > matters only, I mean, not necessarily in AGI). **** > > ** ** > > I suggest you watch how I build, from the ground up. You need to watch of > course where I am going, but much more importantly you need to watch the > limits of the theory. If you read it carefully, you will realize that there > aren't any. There is nothing in the theory about the size of the causal > sets that could stop it from being valid. It is not like, it applies to > sets of size 14,703, but I can't prove anything for 14,704 or larger. That > realisation should put to rest your concerns, expressed on another post, > that many approaches work for toy problems but not for real-world ones. > That's possibly because they rely on assumptions or approximations that > apply only in certain conditions. A classical example is Fluid Mechanics, > where fluids are treated as continuous by way of differential equations > such as Stokes and Navier-Stokes, but the theory can not explain heat > conductivity or viscosity. These are molecular phenomena, and when you > change scale, the continuous theory collapses. There is nothing like that > in causal sets because they are fractals: they are scale-invariant. The > only limit, will be the size of the computer you have. **** > > ** ** > > And no, there is no limit on time of execution either. This is still > unpublished, so I can only give you a hint. Assuming a neural-network > computer simulation where each element of the causal set is represented by > exactly one individual neuron, and assuming near-neighbor coupling, the > time of execution is constant and independent of size. This is massive > parallelism. I am just curious, are you still with me? May I ask a quiz to > verify? You don't have to answer, the answer is below. Aside from the > obvious fact that this is going to be fast, what is the real, profound > significance of this result? **** > > ** ** > > So this is, in a nutshell, where I am going. For convincing evidence you > should go directly to my Complexity paper. If you haven't, you can't blame > me for that. If you can't access or understand the paper, you can always > ask for help. **** > > ** ** > > ** ** > > JIM> I wish I could understand what you are getting at more efficiently. * > *** > > SERGIO> Again, the efficient way would be for you to read my paper and > comment on it, or publish another rebuffing me, or work with me to solve > the differences. I consider and respect the possibility that you may come > from a different discipline and lack the background or inclination to read > my paper, and this is the reason why I attempted a more expanded and > detailed approach. Tell you what. I'll try bigger steps, and you stop me if > I go too far. Again, there is no rush at all, I'll wait one year if I have > to. **** > > ** ** > > ** ** > > I SAID> As a result, I now consider self-organization as fully explained. > **** > > I ALSO SAID IMMEDIATELY AFTER> As everything, my conclusions are subject > to scientific scrutiny, and a long, arduous process will have to follow to > actually apply the theory to a miriad of particular cases, the brain being > only one of them, the GUAPs being another. **** > > ** ** > > JIM> So give me a simple example of fully explained self-organization.**** > > SERGIO> I already have. I said: "My theorem says that every causal system > has symmetries and establishes a general procedure to obtain the > attractors." The theory is published in my Complexity paper, where Section > 4 presents a fully-developed simple example and summarizes hundreds of > computer experiments I carried out within the limitations of my computer. > **** > > ** ** > > You may also want to see my (2009a) > paper<http://www.scicontrols.com/ReferencesForThisWebsite.htm> where Sections > IV and V cover a fully developed real-world case study, a > Java program used in European universities to teach refactoring to students > in Computer Science. The study includes learning (with a teacher in this > case, but that makes no difference), simulates the increase of entropy and > uncertainty caused by learning (I didn't use those precise terms because my > target audience wouldn't understand them), the conversion of the program to > causal set format (I used a canonical matrix, but it's just a notation for > causal sets), and the application of my theorem to the causal set > resulting in self-organization, which in this case is the block system > illustrated in Fig.6 of the paper. I did discuss but did not publish the > resulting object-oriented design or UML diagram in this paper. **** > > ** ** > > This reply would not be complete if I didn't explain why causal sets. In > brief (most is published too, but some is still in press), three steps: (1) > causal set = algorithm = computer program. Any algorithm that halts or any > computer program that halts is a causal set. Therefore, anything I say > about causal sets, such as they can self-organize, applies to computer > programs that halt; (2) computer programs have been used to simulate > virtually everything; and (3) virtually everything can self-organize. > Living (such as Hawkins' invariant repressentations) or not (such as a > hurricane). **** > > Quiz: the more profound significance is that the brain also has the same > property, particularly noticeable in vision. Since my approach is > bottom-up, and my algorithm comes from the bottom, this is one point where > the bottom-up approach meets a direct and so far unexplained observation. > This result also explains why our brains need to be so large. **** > > ** ** > > It is now time to begin the long and arduous process I mentioned above. I > am trying to jump-start this process, and that's where I am getting at. If > I don't hear anything from you, I shall continue my presentations shortly. > **** > > ** ** > > ** ** > > Sergio**** > > ** ** > > ** ** > > *From:* Jim Bromer [mailto:[email protected]] > *Sent:* Thursday, August 16, 2012 5:55 PM > > *To:* AGI > *Subject:* Re: [agi] Uncertainty, causality, entropy, self-organization, > and Schroedinger's cat.**** > > ** ** > > Sergio,**** > > I am listening to you.**** > > However, I am very skeptical.**** > > So far you haven't explained anything other than a few ideas that are > interesting but do not constitute convincing evidence. I wish I could > understand what you are getting at more efficiently.**** > > **** > > Let's try again. You said **** > > Until very recently, explaining the self-organization was not possible. > Recalling that we are talking about a physical system, there is a principle > in Physics that actually explains self-organization. It says that every > dynamical system that has symmetries, also has a conservation law that > applies to a "conserved quantity." The conserved quantity is something that > is: 1. a property of the system, and 2. remains invariant under the > dynamics. In other words, it is what we call an attractor. There are two > ways to calculate the conserved quantity: Noether's 1918 theorem (and its > many extensions), and my recent work with causal sets. Noether's theorem > and extensions are limited to Lagrangian systems and of little interest in > AGI. My theorem is general, and contains Noether's theorem and extensions > (so far I have proved only one particular case). My theorem says that every > causal system has symmetries and establishes a general procedure to obtain > the attractors. **** > > As a result, I now consider self-organization as fully explained. **** > > So give me a simple example of fully explained self-organization.**** > > Jim**** > > **** > > *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> | > Modify Your > Subscription****<https://www.listbox.com/member/archive/rss/303/18883996-f0d58d57> > > **** <https://www.listbox.com/member/archive/rss/303/18883996-f0d58d57> > > ** ** <https://www.listbox.com/member/archive/rss/303/18883996-f0d58d57> > > *AGI | Archives | Modify Your > Subscription<https://www.listbox.com/member/archive/rss/303/18883996-f0d58d57> > * > > **** <https://www.listbox.com/member/archive/rss/303/18883996-f0d58d57> > > ** ** <https://www.listbox.com/member/archive/rss/303/18883996-f0d58d57> > *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/10561250-164650b2> | > Modify<https://www.listbox.com/member/?&;>Your Subscription > <http://www.listbox.com> > ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-c97d2393 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-2484a968 Powered by Listbox: http://www.listbox.com
