Alan, my point didn't get through. My only contribution to science is the inference. I believe it is important, and I feel obligated to popularize it. To popularize, I need to talk applications, not just pure Math.
In NS, that means applications to the brain. I know a bunch of things about the brain. They are things neuroscientists do not know. And I don't know many of the things they do. In my mind, this calls for teamwork, not hide in a hole and shut up. Here are the things I know. I know the brain obeys laws of conservation, just because it is a physical system, and I know that laws of conservation are associated with symmetries in the physical system. I know the brain makes invariant representations (the chair upside down is still a chair). I would like to know if the two things are related. I see causality in the brain. Sensory organs collect causal information (it is still causal even if rated pulses originate from a cone). Muscles are driven by causal commands. Neurons firing cause other neurons to fire. There are exceptions, I know that too. Causal sets have symmetries, and they obey laws of conservation, which result in invariant representations. I have collected some experimental evidence suggesting that these representations are the same that the brain makes, given the same information. Should I pursue these matters further? Or should I just ignore the whole thing because, for example , "neurons sometimes fire at random?" Or because a cone on the retina gives out many pulses instead of just one? The clue here is to pursue the big matters without getting bugged down in the details. I am trying to say something useful about the brain that neuroscientists can understand, without sacrificing the big picture. I feel free to disregard details when I believe that the big picture is independent of those details. For example, if a cone produces a string of pulses, not just one as I proposed, would then the brain not be a physical system? Would it not obey conservation laws? Would it not make invariant representations? If I can show that a chair upside down is a chair with one pulse, would that be necessarily false for 3 pulses? I know even more things that concern the brain. I know that EI is not an algorithm, and can not be implemented as a circuit or network. I am very concerned about projects to reverse-engineer the brain and simulate it on a computer using a program. Because they are not even looking at the right things. They can simulate the entire brain in ultimate detail, with strings of pulses coming from cones, with all the details of the optical nerves, and still not find EI! Because it is not there. They ought to be looking at the dynamics of the neurons, doing simple experiments with brain-on-a-dish or retinas that compress, and trying to understand how it all works, before embarking in blind efforts. And so also should I. Try to apply EI to simple things, understand what they do, find the principle, and only then, with the principle in hand, embark in implementation details. The question is: do neurons do EI, or not? And if they do, how do they do it? So how about some team work? Sergio Some more: EI is behavior-preserving, there is no loss of information. EI can be viewed as a function that maps from causets to structured causets. The map is bijective. There exists an inverse function, which is computable. EI works by extracting entropy from a system, that's how it organizes the system. The inverse function does the reverse, it disorganizes the system by adding entropy. See Fig. 4 and text and reference in my Complexity paper for the question about edges. EI does not actually find edges, it classifies the points into categories, 3 in this case. To have edges, one would have to define what an edge is. Try finding "edges" for the "areas" in the figure. There is more than one way. -----Original Message----- From: Alan Grimes [mailto:[email protected]] Sent: Friday, June 22, 2012 8:33 PM To: AGI Subject: Re: [agi] Prediction Did Not Work (except in narrow ai.) Sergio Pissanetzky wrote: > Alan, > Alright. I thougth I answered 1 and 2 before, but here comes again. > Take a retina. Light from whatever is outside illuminates the cones. > Each cone generates an electric impulse. Wrong, a rate encoded (if I'm not mistaken) series of pulses that are processed by the retinal ganglion cells. > That's the causal set. That's it: (light on cone ==> electrical > impulse) times 100 million cones is the causal set. Prove it. Take an image and show how it can *REVERSIBLY* be encoded into a causal set. If the transformation is not reversible then you are throwing away information. --> FAIL. > And > that's all the information you get. Look at it anyway you want, that's > all the information you can get from the retina. Of course, each cone > has a position in the body, Wrong. It has a receptive field. It is sensitive to light passing through a cone, the tip of which is in the lense of the eye. The sum of the receptive fields of all the cells in your eye is your visual field. It can be claimed that the entire nervous system is designed around the visual sense. Because of the optical properties of the eye, the image on your retina is upside down and backwards. Because there are more optic nerves than somatic nerves, there is no "pyramidal tract" in the optic nerve, it is a straight shot back to V1 which is upside down and backwards. To keep everything consistent with the brain, the somatic nerves ARE crossed over the center line of the body and you find an upside down and backward representation of your body on the post-central gyrus of your brain. So yeah, your brain is upside down and backwards in your skull. > and the electric impulse came at a certain instant of time. To within about 1/15th-1/20th of a second or so depending on several factors... > With hearing it is the same. Regarding "binaural", I do not disagree, > I am only saying let's start with something simpler, monaural. If we > can do it for a monaural causal set, we can also do it for a binaural > one, but will have to buy an even bigger computer. Hearing, as is now commonly known, is processed in an organ called the cochlea, it's a snail-shell shaped piece of bony anatomy that is embedded in the skull. I forget the exact mapping but it basically operates as a spectrum analyzer for incoming frequencies. This system of the brain has been reverse engineered to a large extent and there are some fairly accurate functional diagrams on the net. > "Reallistic" Well, sunlight and black > dots is reallistic, you can look at black dots. For a more reallistic > case, we will have to engineer the cones! (Yes, it's me saying engineer). Well there are a wide variety of cameras available. Furthermore, we know that common video recordings, such as DVDs contain enough information for visual perception, so therefore we have no reason to do anything with regards to cone cells. > Surprisingly, however, we will NOT have to tell the causal set > anything about the cones, so the engineering is external to EI, or to the neurons. > All that is needed is that "something" has caused the electric > impulse. And then, we let EI or the brain to figure out what it was. Wrong. During embryonic development in some species, scientists have detected patters of spontaneous activation across the retina. The hypothesis is that these "test patterns" pattern the cortex of the occipital lobe so that an accurate spatial map is established. You continue to deny this fact without explaining how your theory handles spatial information. > Burden of proof? That's unreallistic. Who do you think I am, Qicken Loans? > EI touches a variety of disciplines. AGI is just one of them. I have > communication with people from several other disciplines. Should I > become an expert in each one of them? My contribution to science is to > have proved that EI exists and to have characterized it, and that's > why I keep insisting about the section on Small Systems in my > Complexity paper. Now, experts from the disciplines must take over. My > part: continue developing the theory (it is not finished, not even > close) and try to attract help from experts by popularizing my finding. You're jumping the gun. I'm not satisfied that you are continuing to think critically about your idea. Instead, I see you promote it as a solution to problems in fields you have not studied, even as an amateur. > Regarding 2, I thought I did that too. I showed useful results for the > GUAPs in Computer Science, recognition of edges in vision, and > Physics. That's only an enticement. Splendid! Where's the paper? I may have forgotten reading it already... > Sorry I am pissing you off so much about Neuroscience. I declare > emphatically my knowledge on that to be very limited. But I can draw > conclusions that may, repeat, may be useful to Neuroscience, and > that's all I am trying to do. NO GOOD. I'm still a 2nd rate ignoramus when it comes to neural science but I still know the essentials. If you can't be bothered to study, then STFU. > I have also insisted that the brain is the only known intelligent > system, and that we have a lot to learn from it. But I've also said, > the "lot" does NOT include the implementation of the brain. All we > need is to understand the principles, and then we can start using the > principles in new and creative ways, such as an artificial system, > without ever having to simulate the brain in all its complexity. Sorry > if you don't like this, but this is a cornerstone for me. Agreed. > For the sake of principles, I don't need to know very much about the > 200 types of neurons. That doesn't mean I disdain that vast knowledge. > Quite on the contrary. All I am saying is that, for now, it is not > needed. Once the principle is set, then, not now, then it will be the > right time to start examining neurological knowledge. By you, not me. I'll help all I can. > People work in teams in Science, you know. But there are many principles. There are many anatomically and functionally distinct neural circuits in the brain. The differences between cortical regions is relatively minor but there are many other regions in the brain with a great deal more diversity in structure and function. -- E T F N H E D E D Powers are not rights. ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/18883996-f0d58d57 Modify Your Subscription: https://www.listbox.com/member/?&; d2 Powered by Listbox: 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
