Intention is Physical by Natesh Ganesh

Thank you for your patience with me, Natesh. I see you are at a nanotechnology lab, so I imagine that "I squared R" dissipates a lot of heat that is of concern. How much dissipation in your hypothesis is from I^(2) R ?

From my work experience in software, implementing a FSM by an array with two indexes, one for current state, one for current input signal, storing at those indexes next state and output signal, is much quicker and easier to debug than leaving the FSM in a lot of "if-then" statements-- I.e. using data instead of code increases the speed of execution and reduces debugging significantly. Then if the rest of the code is needed for the full Turing machine, most of the speed loss and complexity in my coding experience would come from the full Turing machine, not the FSM. That's why I ask. And, in your line of work, it seems to me that the vonNeumann architecture would be more relevant for I^(2) R dissipation than more abstract models like co-algebras, streams, or Chu spaces for modeling computing. For example, as in Samson Abramsky's Big Toy Models.

In engineering terms, apoptosis is a quality control in animals (it never occurs in plants) that seems like a way to minimize energy loss and therefore may be relevant to your dissipation hypothesis. In apoptosis, defective cells that would be energy-wise inefficient are destroyed and their components re-used to build other cells, thus holding onto the potential energy in the sub-assemblies and again reducing energy loss. But I don't see how I^(2) R heat loss plays a role in apoptosis.

If you had an equation for dissipation rather than a verbal explanation, It might give me something more general than I^(2) R to think about-- especially regarding apoptosis as an example of minimizing dissipation of energy. What are your thoughts?

Regarding your references-- I will try to find them online. Are they in arxiv? The closest big universities where I could photocopy these papers are hours away from me.

Sorry, the previous post was me. Don't know how that happened.

Lee Bloomquist

Natesh. All I can get on the second paper is the abstract and a bit of the intro. The rest is behind a paywall.

You wrote, "When you talk about I^2*R expression for dissipation, you are thinking about wires and charges moving through those wires. So that expression will not hold for spin based architectures." It's Joule heating, as in this article:

http://poplab.stanford.edu/pdfs/Grosse-GrapheneContactSJEM-nnano11.pdf

And, you wrote about "entropy":

"I want to point out again that I use the dissipation bound as a (good) approximation of the actual dissipation in the processes that we are interested in. The dissipation bound expression as entropy \delta S and mutual information terms I."

But the abstract talks about "energy dissipation":

****

Abstract:

A hierarchical methodology for the determination of fundamental lower bounds on energy dissipation in nanoprocessors is described. The methodology aims to bridge computational description of nanoprocessors at the instruction-set-architecture level to their physical description at the level of dynamical laws and entropic inequalities. The ultimate objective is hierarchical sets of energy dissipation bounds for nanoprocessors that have the character and predictive force of thermodynamic laws and can be used to understand and evaluate the ultimate performance limits and resource requirements of future nanocomputing systems. The methodology is applied to a simple processor to demonstrate instruction- and architecture-level dissipation analyses.

...I. Introduction

Heat dissipation threatens to limit performance gains achievable from post-CMOS nanocomputing technologies, regardless of future success in nanofabrication. Simple analyses suggest that the component of dissipation resulting solely from logical irreversibility, inherent in most computing paradigms, may be sufficient to challenge heat removal capabilities at the circuit densities and computational throughputs that will be required to supersede ultimate CMOS.1

For 1010devices/cm3 each switching at 1013sтИ'1 and dissipating at the Landauer limit EminтЙИkBT have Pdiss=414 W/cm2 at T=300K Comprehensive lower bounds on this fundamental component of heat dissipation, if obtainable for specified nanocomputer implementations in concrete nanocomputing paradigms, will thus be useful for determination of the ultimate performance capabilities of nanocomputing systems under various assumptions regarding circuit density and heat removal capabilities.

****

Are you saying the heat dissipation in post-CMOS devices is just due to spin, and none of that heat is due to Joule heating?

Best Regards,

Lee

Natesh, thank you for your reply! You wrote:

"Also with respect to the fish example, the fish is a system that can act on its environment and thus its behavior is a tradeoff between 'exploration vs exploitation' under this idea and would not just be a form of predictive learning that we would see in a system that cannot act on its environment."

Is what you wrote, above, about probability-learning foraging fish implied by the definitions of terms in your hypothesis? That is, do the definitions of the terms in your hypothesis (like "open," "constraints," etc.) imply what you have written, above?

Your hypothesis: "Open physical systems with constraints on their finite complexity, that dissipate minimally when driven by external fields, will necessarily exhibit learning and inference dynamics."

Or, is more than this required to understand your hypothesis-- more than just the above statement of your hypothesis together with definitions of the terms used in your hypothesis?

Dear Ganesh,

I wish you all the best with your in depth analysis of how intentions govern reality. I welcome you to read there are no goals as such in which I propose that consciousness is the fundamental basis of existence and that intent is the only true content of reality. Also that we can quantify consciousness using Riemann sphere and achieve artificial consciousness as per the article Representation of qdits on Riemann Sphere. I saw that you are also arriving at study of consciousness in physical systems in the conclusion of your essay. Also please see all the diagrams I have attached in my essay.

Love,

I.

Dear Ganesh,

Thank you for the good essay on "Intension is Physical"

You are observations are excellent, like... "fundamental limits on energy efficiency in new computing paradigms using physical information theory as part of my dissertation"

I have some questions here, you mean to say for every external input, our Brain predicts and takes a correction. It wont be acting on directly by its own self....

Probably if we make energy efficient computer, it will become super intelligent, Probably we may require some software also....

Even though my essay (Distances, Locations, Ages and Reproduction of Galaxies in our Dynamic Universe) is not related to Brain functions, It is on COSMOLOGY....

With axioms like... No Isotropy; No Homogeneity; No Space-time continuum; Non-uniform density of matter(Universe is lumpy); No singularities; No collisions between bodies; No Blackholes; No warm holes; No Bigbang; No repulsion between distant Galaxies; Non-empty Universe; No imaginary or negative time axis; No imaginary X, Y, Z axes; No differential and Integral Equations mathematically; No General Relativity and Model does not reduce to General Relativity on any condition; No Creation of matter like Bigbang or steady-state models; No many mini Bigbangs; No Missing Mass; No Dark matter; No Dark energy; No Bigbang generated CMB detected; No Multi-verses etc.

Dynamic Universe Model gave many results otherwise difficult to explain

Have a look at my essay on Dynamic Universe Model and its blog also...

http://vaksdynamicuniversemodel.blogspot.in/

Best wishes................

=snp. gupta

Your work is highly mathematical. Although my math skills are not good enough to understand the details, I think it is a tremendous work. I would love to see your work on consciousness, qualia and meaning, which you hinted at in the essay, primarily because I had considered those areas well-nigh impervious to mathematics. But I am pretty sure you will talk even of those areas using math. Rather impressive, I must say.

I had wanted to evaluate my work on whether it satisfied the mathematical theorems of Ashby and Conant. I am speaking here of the Law of Requisite Variety (Ashby) and the Good Regulator Theorem (Conant). But I could not because my math skills are not good enough for the job. My guess is you would want to evaluate your work as well on its alignment with the basics of those works. Ashby's work is considered a classic in understanding the functioning of all systems, but on glancing through his works, it is clear that he did his work primarily with the human brain in mind.

Natesh,

Great essay, well informed and organised analysis of a very interesting hypothesis.

I also like most and agree with much, certainly with the addage that; 'our aims and goals are shaped by our history', and the importance of; efficiency, neuronal avalanches, branching parameters, critical regions and that a 'hierarchical predictive coding' model is possible.

I'm not yet convinced that minimal dissipation itself is a precondition of learning and 'inference dynamics' and didn't feel you proved that. Do you not think it might be more a ubiquitous characteristic than a driver - in a similar way to 'sum over paths'? If 3 lifeguards head off to save a drowning girl 100m down the beach, one heads straight for her, one to the shore point opposite her position, and one at the right angle to allow for swimming slower than running; Of course the 3rd gets there with least energy, but I feel that underlying that may be a still greater and more useful truth and meaning.

Also might the error/feedback mechanism be better described as iterative value judgement comparisons. Perhaps no 'right or wrong' just different outcomes, which we can't value till compared with previous runs.

Say the first 'run' of consequences is imaginative drawn from input history. Say Do I want a PhD Y/N? gives an 'AIM' (Y1). We run a scenario to imagine it and implications. We then keep running it as more data comes in. Subsequent lower level/consequential Y/N decisions are the same, taking Y1 into the loop, an on hierarchically. If it turns out not to be as envisaged, or we win millions and want to be a playboy instead, we change the aim to N and form new ones.

May it be that you're a little too enamored by the formula and suggest conclusions from those rather than the deeper meaning they're abstracted from.

i.e. Might Lifeguard 3 have said 'how do I get there fastest' rather than '..by using least energy'? (or just done that due to it's successful outcome in past re-runs of the scenario).

Lastly on 'Agency', which I see as a semantic 'shroud'. If all subsequent Y/N decisions in a cascade are simply consequential on the first Y/N decision, and the branches lead to a mechanical motor neuron or chemical response, all repeated in iterative loops, might the concept 'agency' not fade away with most of the mystery?

All 'leading edge' questions I think and thank you for a brilliant essay leading to them. Top mark coming.

Best

Peter

Hi, Ganesh, I respond here to a question that you asked in my page:

"I will have to ponder over the idea of ascribing goals to any entropy reduction in a system. I am wondering if that is too narrow a definition. After all, a (conscious) observer should be capable of ascribing a system as performing a computation (and hence the goal of performing that computation,) even with no entropy change(?)"

If the computation is invertible, then the output is equal to the input, except for a change of names. I believe that computations are interesting only when they are non-invertible. But perhaps I am missing something...

I saw your essay as soon as it came out, I was impressed, but did not follow all the details. Today I gave it a second look, and I am still impressed, above all, because this strikes me as an original contribution, which I found only very rarely in this forum. Moreover, within the neural networks theory, I've had enough gradient-descent learning rules that come out of the blue, your proposal is so much physical. I confess I must still give it more proper thought - or perhaps, find the time to do the calculations myself - because I intend to take these ideas very seriously. I hope you publish this work as a paper soon, this essay contest does not seem to be the best environment. The work is probably a bit too technical given the contest rules, the length is too constrained, and the audience can be better targeted. I hope that you will consider presenting these ideas in the computational neuroscience audience. They may not have your same physical-computational background, but they will be surely interested in the conceptual result.

Congratulations!

inés.

Hi, Ganesh, I am afraid I do not understand. "the observer will claim that this physical evolution with zero entropy change has achieved the goal of being an AND gate." Why do you say that the evolution has no entropy change, if the observer has made the association A -> 0, and B,C,D -> 1? This association is entropy-reducing, isn't it? I wait for your reply before elaborating more.

Great to know you are on the way to publish! Your essay is new raw material, so the natural evolution is: get it published. As a neuroscientist, I was more surprised by the learning part of your essay, than by the criticality one, but mind you, I am not truly mainstream, so just take it as one opinion out of many. To me, the learning part is thought provoking, I have the impression that new paradigms, and new understanding may come out of that. The criticality claim seems to be everywhere, but I do not gain much from it, apart from classifying the process as critical. Anyway, surely I am missing something...

best!

ines.

Hi Natesh,

sorry about the names! I am a slave of rhymes, and tend to map together all what sounds similar. Actually it's even worse, I also cluster faces together. By all means, I must learn to represent information more injectively...

Yes, sure, as I see it, it may well happen in the brain of the observer. There are many possible settings, which I discuss below. But in all settings, ascribing agency (as I understand it) requires an entropy-reducing mapping. If the mapping is not injective, it may still be interesting or useful for the observer, and he or she may still make a valuable acquisition in their life by learning the mapping. But I can hardly relate this operation with perceiving a system that seeks to achieve a "goal". For me, a goal is something that tends to be reached irrespective of factors that tend do interfere with its accomplishment. That is why I require the non-injectivity: the putative obstacles are a collection of input conditions, that are supposed to be overcome by the goal-seeking agent.

Now for the variable settings.

One option is: I am the observer, and I receive information of the behavior of some external system (say, a replicating DNA molecule outside me), and by defining the system under study in some specific way, I conclude that there is some goal-oriented behavior in the process.

Another option is: I am given some information which I represent in my head, and I perform a certain computation with that information, for example, the AND gate you mention. The computation happens inside my head. But I also have observers inside my head, that monitor what other parts of my head are doing. For one such observer, what the other end of the brain is doing, acts as "external" (in all computations except for self-awareness, which is the last part of my essay). One such observer can assign agency (if it wants to!) to the computation, and conclude that the AND gate (inside my head!) "tends" to reduce the numbers 1, 2, 3 to the digit 0 (or whichever implementation we choose). A weird goal for an agent, but why not.

All I am claiming is: goal-directed behavior does not exist without an observer that tends to see things in a very particular way: as non-injective mappings. I am not claiming that this is the only thing that can be learned by a plastic system (one-to-one mappings can also be learned). I am not claiming that the only thing that can be done with a non-injective mapping is to arrogate it with agency and goals. There are many more things happening in the world that may be good to learn, as well as in the brain of the observer. And there are many more computations to do, other arrogating agency. My only point is: if we see a goal (inside or outside us), then we have trained ourselves to interpret the system in the right manner for the goal to emerge. The goal is not intrinsic to nature, it is a way of being seen.

Not much, just one tiny point. Or perhaps, just a definition. If you look through the essays, there are as many definitions of "goal", "agent" and "intention" as authors...

> my main contribution is tying it all together in a physical sense

Yes, and that is precisely why it is so great! And perhaps you are right, within your framework, what so far has been presented as a mere description of the critical brain, now can be seen as the natural consequence when certain physical conditions are assumed. I do appreciate that.

Anyhow, time to rest! buenas noches!

inés.