Sergio, I can't spend the time that I would like to take to read your presentation right now. I appreciate the example of the invariant and how it relates to your theory of self organization. However, you are making claims which have some similarities to claims (or implicit claims) that the probability people have made. You cannot just rule on an uncertainty and fit it into the certainties of a system of knowledge. I don't know why this is so difficult. There are many different frames of reference which we can use to think about ideas. These don't always fit perfectly together. If our certainties were in truth certainties then I'd have to stop what I was doing and really study what you are saying. But the truth is that our uncertainties rob our certainties of complete certainty. Just as our frames of reference may not fit perfectly together, the domain in which uncertainties are defined may not fit in perfectly with a systematic methodology of thought.
The theory that all knowledge may be effectively modeled using the theory that the mind organizes information so that it reduces energy is pretty far fetched. I think that if the theory was more qualified it would be more acceptable. Jim Bromer On Mon, Aug 13, 2012 at 3:53 PM, Sergio Pissanetzky <[email protected]>wrote: > Jim, **** > > ** ** > > FIRST PART. UNCERTAINTY, CAUSALITY, AND SCHROEDINGER'S CAT. **** > > ** ** > > You are very poor and your only hope is to sell your cat. You put it in a > box to take to the market. But you start thinking. What if the cat dies in > the box? I can't sell a dead cat. But if it is still alive when I get > there, how am I going to sell it? Your brain is effectively considering two > different universes. You can't decide between them so you consider both. > From your perspective, you are living in two different universes at the > same time. **** > > ** ** > > We say that your brain is the system, and the system has only two states: > cat dead, or cat alive. We call this situation an uncertainty, you are not > certain which way it is. So you need an answer to your uncertainty. You go > after the answer, and you choose a behavior that is independent of the > uncertainty. We say that your behavior is invariant under a transformation > from one state to the other. You take the box to the market and open it. > The cat is alive. **** > > ** ** > > As a result of your invariant behavior, you have now acquired new > information: the cat has survived. Your two universes collapse into one. > But you start thinking again. If my cat looked better I could get more > money. Again you have two universes, it either looks well or it doesn't. > You seek an answer. You need an invariant behavior. You take the cat out of > the box and put it in the light. Now you acquire more info: the cat doesn't > look well. Again, you are left with only one universe. And so on. **** > > ** ** > > In Physics, we describe a system by means of a set of variables, and we > say that each possible combination of values of the variables is a state of > the system. The variables can be boolean, or have integer values, or > anything appropriate. Then, we specify an initial state, and a dynamics for > the system. A dynamics is a rule or set of rules that specify how the > system transitions from one state to another. And the rules account for the > uncertainty. If the system is in state A, it can transition to state B, or > C, or D. We don't know which one it will be. And if the system transitions > to B, then it can transition from there to X or to Y or to Z, again we > don't know which. The dynamics is not **causal** because the state and > the rule to do not add up to a transition. Yet, the response behavior, an > algorithm, should be causal so it can be executed. Where does the algorithm > come from? **** > > ** ** > > The algorithm comes from what we know, from the information we have. So > here is what we do know. We know that states B, C, or D can not exist > unless state A has existed before. So we say that A **precedes** B, C, > and D. We also know that X, Y, or Z can exist only if B has existed. And > this is the precise point where **causal sets** come in. We formalize > our knowledge by writing: **** > > ** ** > > A<B, A<C, A<D, B<X, B<Y, B<Z.**** > > ** ** > > which, together with the set of states, {A, B, C, X, Y, Z} in this case, > is known as a causal set (read the '<' sign as "precedes"). We can write a > computer program for this. It would look as follows:**** > > if(A) then**** > > {**** > > if(B) then**** > > {**** > > if(X) then ...**** > > else if(Y) then ...**** > > else if(Z) then ...**** > > }**** > > else if(C) then ...**** > > else if(D) then ...**** > > }**** > > which means that if A has existed then either B, C, or D can exist, and in > the case where B has existed then either X, Y or Z can exist, and so on > until the program stops because there are no more state transitions left. > But what have we achieved by writing the program? Nothing. The uncertainty > is still there, only now it has been transferred to the data. In order to > run the program we have to specify all the uninitialized variables as data, > for example we could specify A, B and X, or A, C, etc. In other words we > have to specify the exact sequence of state transitions. Guess, rather than > specify. **** > > ** ** > > There are many possible sequences of execution that satisfy the > constraints in the causal set, and no apparent reason to prefer one over > the other. But our brains make a unique solution, every time, when in > possession of certain information. For example, if I want to travel from > Houston to Dallas I can fly first to San Francisco and from there to > Dallas, or I can fly directly from Houston to Dallas. And brains are very > consistent. Every person would choose the second alternative, unless they > have some other reason to go to San Francisco first, in which case they > would have additional information. So how do our brains make that unique > selection? Obviously, we are missing something here. How does the brain do > it? **** > > ** ** > > ** ** > > SECOND PART: ENTROPY AND SELF-ORGANIZATION.**** > > ** ** > > This is now the heart of my work. The brain doesn't do "it". It does > something else, and "it" follows as a result. The brain must satisfy its > never-ending hunger for energy. Information carries energy. Yes, > information itself. In March 2012, they have actually measured the amount > of heat generated by erasing one bit of information, thus confirming the 50 > years old Landauer's prediction. When the brain learns something, that is, > when it receives information, it supplies energy to its memory so it can > store that information. As it stores, it immediately recovers any energy it > can from the stored information, so it can use that energy to store more > information. **** > > ** ** > > And here is some Physics. When energy is extracted from information, then > entropy is also extracted. This is the Second Law of Thermodynamics. But > entropy is the measure of uncertainty in the information. The uncertainty > is reduced by the removal of energy because high energy states are no > longer accessible to the system. The state space actually shrinks, and the > system converges to its attractors, which is how self-organization takes > place. And, as if that were not enough, the transformation induced in the > information by the removal of entropy is behavior-preserving. The result is > an algorithm, and the algorithm is causal. In the brain, by the simple act > of conserving energy, also removes all the uncertainty from the information > and results in the unique, invariant behavior. **** > > ** ** > > In AI/AGI, the energy consumption by computers has been the focus of > attention for decades. But few seem to have noticed that the brain goes > beyond that point, and reduces the energy consumption of the information > itself, not just the machine. Removing energy from information also removes > entropy, and casues it to self-organize into invariants. That's what the > brain does, all the time, create invariants, or invariant representations > of the information it has acquired. That's how 100,000,000 dots of light in > your retina become "hi, mom." We use the invreps for everything, to think, > to communicate, to create more invreps. Every word I write here is an > invrep in my mind. A language is an invrep. There can be no intelligence > without invreps. **** > > ** ** > > It generally seems to me, but I can't promise, that little will be left to > explain intelligence once the invresps are understood. There is an infinite > numerable quantity of causal sets, there is and infinite numerable quantity > of invariants, and there is a bijective correspondence between each causal > set to each invrep. What is left? Of course, understanding the > implementation details of the brain is another matter. **** > > ** ** > > AIers and AGIers alike try to process information while leaving all the > entropy in it. Alternatively, they pass the information to humans and use > the human brain to remove the entropy and create the invreps, which are > then fed back to computers, but also results in the familiar man-machine > inter-dependency perpetuated in both fields. We all remember the quest for > the perpetual motion machine, which stopped only when the energy-entropy > interplay was understood in thermodynamic machines. AI/AGI is the pursuit > of the perpetual certainty machine, one that can make its own uncertainty > disappear. It has been going on for 60 years. It is time to understand the > more complex energy-entropy-structure interplay in information. **** > > ** ** > > ** ** > > Sergio**** > > ** ** > *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
