Intention is Physical by Natesh Ganesh

Dear Natesh

this is an impressive piece of work.

I have not had time to try to work through the details, but for me two things are important. First, you have set a context of predictive estimation. That is a key issue, and I agree whole-heartedly. So there has to be a structure that underlies the existence of this function, and the kay issue is where this structure came from. That cannot be via non-equlibrium thermodynamics alone.

Second, you say Agency is the capacity of a system/entity/agent/organism to act on it's environment. Is is the Moon an agent n that respect? (after all it causes tides on the Earth). "We will define sense of agency (SA) as the pre-reflective subjective awareness that one is initiating, executing, and controlling one's own volitional actions in the world." That is already assuming key elements of psychology that do not arise in any simple way from physics.

I will try to reflect more on what you have written in due course. Your principle may well be important at the physical level, when the rest of the context is given.

Best wishes

George

Natesh Ganesh asks, "Can minimal dissipation alone be a sufficient condition for learning?"

Two biological systems might fit this description.

First, in Charles Gallistel's book The Organization of Learning, Chapter 11 tells of foraging experiments. Consider fish (are neurons like fish?). Given probabilistic feeding stations, the school divides itself proportionally-- which from the perspective of game theory looks like a Nash equilibrium: no single fish can increase its payoff by leaving one station to feed at another. In this situation, no food energy is "dissipated" because every scrap is eaten. So according to the hypothesis, learning must be involved. Indeed, if this were a probability learning experiment with just one fish and food energy given per play of the game, we would see probability learning (also as in Chapter 11). In probability learning food is "dissipated." Perhaps because of fear of leaving one group and joining another, the visiting behavior that's seen in probability learning is attenuated when the school forages as above; and then no food energy is dissipated.

Second, in Goranson and Cardier's A two-sorted logic for structurally modeling systems, there is a review of apoptosis (programmed cell death, which according to the paper happens in the human body about a million times a second). In the olfactory system, apoptosis of neurons amounts to learning new smells, which involves consciousness.

In Barwise and Seligman's Information Flow: The Logic of Distributed Systems, the example of a flashlight is given. The system-- a flashlight in this case-- supports different "local logics" and therefore different languages between which there may be perfect or imperfect translations. The language in this essay could be one such language. But there are others, as the example of the flashlight shows (including the specification language that specifies the purpose of the flashlight). Goranson describes software by which many different languages, local logics, or situations like this can be organized.

Hi Natesh,

I think this essay is fantastic and basically completely correct. I love that you took the time to make explicit connections between the Landauer limit, which many biochemical processes ahve been shown to assymptotically approach, and the importance of a predictor circuit and feedback between sensing and acting, and you even bring in the flcutuation theorems at the end in discussing the problem of assigning probabilitites to brain states, I think it's wonderful and very informed with regard to current research in stat mech, neuroscience, and machine learning. You have the diversity of background required to address this question which is at the intersection of so many fields.

I hope you might take the time to peruse my submission, entitled "A sign without meaning." I took a very different approach and went with an equation-free text in the hopes of being as accessible as possible, but I think you'll find that we agree on a great number of issues, and I'm glad that the question is being addressed from multiple perspectives but with the right foundation in statistical mechanics.

Best of luck to you in the competition I think you wrote a hell of an essay!

--Joe Brisendine

Quite interesting, Natesh. The emergence of intention, purpose, goals, and learning are automatically achieved with England's restructuring and replication thesis as dissipation occurs -- but humanly done with purpose and goals? Your emphasis on computer modeling seems to blur the distinction between the human and machine but that is probably my failure to view it after one quick read.

Impressive study.

Jim

Natesh - You did a good job on your essay and I like how you've been able to incorporate math, which was suggested in the essay rules. Well done! I gave you a good community rating which I hope helps to give your essay the visibility/rating it deserves.

Natesh, I can't find an equation that defines "dissipation" in the essay. Is it in the references? I can find "the lower bound on dissipation in this system as it undergoes a state transition..." But that seems specific to finite state machines, which are not equivalent in power to Turing machines. Is it just "delta E"? where E is energy lost from something like a thermodynamic engine?

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