The 2nd Law: Is Increased Entropy Stochastic (incidental) or Causal (intrinsic)?

Recent science news is dominated by the multi-trillion dollar experimental search for the Higgs boson particle. A definitive observation of the theorized, but illusive, Higgs will finally complete the verification of the Standard Model – the most respected mathematical model of the evolution of our universe, explaining the emergence of each of the known forces and all of the matter we can observe. In the Standard Model, the Higgs is responsible for gravity – surrounding the more pedestrian particles – lending them the property we call "mass". If the Higgs exists, it is important as the causal bridge between the quantum world of the small and the relativistic world of the large. How could a particle that causes gravity be so hard to find? Because it doesn't actually have mass. It is as a result, known as "weakly interacting". It is only when a whole bunch of Higgs get together and surround other particles that mass is detected, and then, only in the surrounded particles. The Higgs binds so tightly to other particles, that it takes an extraordinary amount of energy, to break it free so that its presence can be detected. This is what the "Large Hadron Collider" does – it smashes heavy atomic nucleus (stripped of their electrons) at energies equivalent to those of the first moments after the Big Bang when all of the matter and energy in the entire universe was still smaller than a single star.

But there is a far more fundamental question. Gravity is a property. It is domain-dependent. It is specific to and belongs to a class of objects of a particular makeup and composition. The existence or nonexistence of the Higgs has no effect upon other properties of the universe like electromagnetism.

But there is a candidate for a domain-independent attribute of any and all causal systems. This attribute has been labeled the "Causal Entropic Principle" – it is generally discussed within the context of the transfer of heat (at astronomical scales) – within the study of thermodynamics. It is the logical extension of the concept of increased entropy, as first postulated, measured, and later described as the 2nd Law of Thermodynamics. But now, a hundred and fifty years after the formalization the laws of thermodynamics (of the phenomena and parameters of the transfer of heat, of the ratio of potential energy and work) correlative investigations in the fields of information, communication, computation, language, energy/mass, logic, and structure have uncovered parallel principles and constraints.  It is reasonable now to understand the 2nd Law as a description of a fundamental constraint on any change, in any system, no matter what forces and materials are at play. We now understand the 2nd Law to describe the reduction in the quality (density) of the energy and or structure of the universe (or any part therein) as results any change at all. We have come to understand the 2nd Law as a constraint on the outcome of change in structure, which is to say "information", on its construction, maintenance, and or transfer. This insight has rendered an equivalence between energy and structure in much the same way that Einsteinian Relativity exposed the equivalence between energy and mass.

There is however a daemon lurking within our understanding of the 2nd Law, a daemon that threatens to undermine our understanding of causality itself, a daemon that, once defined, may provide the basis for an understanding of any self-consistent causal system, including but not exclusive of our own universe and its particular set of properties and behaviors.

The daemon of the 2nd Law is the daemon of stochastic – is 2nd Law dictated dissipation (entropy) statistical, or is statistics simply a tool we use in the absence of microscopic knowledge? Asked another way, is the reduction in the quality of energy or information that the 2nd Law demands of every action, a property of the universe or is it a property of the measurement or observation of the universe? Is action equivalent to measurement? Is there a measurement or stochastic class of action free of the entropy-increase demanded by the 2nd Law?

This question is of far greater consequence to the universe and the understanding of the universe than the mechanics of mass as it would describe and thus parameterize ALL action and ALL configuration and the precipitation or evolution of all possible action and configuration. Where the existence of the Higgs Boson may explain the source of mass and gravity in this universe, an understanding of the causal attributes leading to the behavior described by the 2nd Law of Thermodynamics might just provide a foundation from which any and all causal systems must precipitate.

The implications and issues orbiting this problem are many and deep. At stake is an demonstrative understanding of change itself. We tend to think of change as exception. But, can a thing exist without change? If not, what is the difference between data and computation, between thing and abstraction of thing, and profoundly, an answer to the question, can data exist without computation? Can thing exist outside of abstraction of thing?

In thermodynamics and information theory, an effort is made to distinguish process and stochastic process. Heat is defined as an aggregate property describing the average or holistic state of systems composed so many interacting parts to keep track of all of them individually. Heat is a calculous of sorts, a system of shortcuts that allows mathematics to be employed successfully to determine the gross state of a huge collection of similar parts. There is a tendency then to assume that the laws that describe heat are laws that only apply to aggregate systems where knowledge is incomplete.

Are there non-stochastic systems? Are there discrete systems or dynamic changes within systems for which the laws of thermodynamics don't apply? Does the Causal Entropic Principle apply if you know and can observe every attribute of, and calculate the exact and complete state of a dynamic system?

Such questions are more involved than they may seem on first reading. Answering them will expose the very nature of change, independent of domain, illuminating the causal chain that has resulted from full evolutionary lineage of the universe.

Randall Lee Reetz

Note: The Causal Entropic Principle isn't a complex concept. It is the simple application of the 2nd Law's demand for increased universal entropy as a result of every change in any system. It says that every action in every system must be that action that causes the largest reduction in the quality of information or energy (the greatest dissipation). It says that a universe has only one possible end state – heat death – and that processes that maximize the rate towards this end state will be evolutionarily favored (selected), simply because entropy-maximizing processes and structures demand a higher throughput of energy and thus end up dominating their respective locality. Such entropy-maximizing schemes are thus more likely to determine the structure and behavior of the event cone stretching off into the future. An obvious extension of this principle is that complexity, or more precisely, the family of complexity that can find, record, and process abstractions that represent the salient aspects (physics) of the (an) universe, will help that complexity better predict the shape and behavior it must assume to maximize its competitive influence upon the future of entropy maximization. The "Causal Entropic Principle" thus represents a logically self-consistant (scientific) replacement for the awkwardly self-centered and causally impossible "anthropomorphic principle" (which lacks a physical or causal explanation and leans heavily on painfully erroneous macroscopic stretching of the quantum electro dynamics). Stretching circular logic to its most obvious and illogical end, the anthropomorphic principle borrows awkwardly and erroneously and ironically form the Heisenberg / Uncertainty Principle by asserting the necessity of "observers" as a precursor to the emergence of complexity. The Causal Entropic Principle explains the production of localized complexity without the need for prior-knowledge, and does so within the bounds of, as a result of, the 2nd Law of Thermodynamics, by showing that localized complexity can both come into existence as a result of the constant increase in universal entropy, and more specifically, that localized complexity has an evolutionary advantage, and will thus out-compete, less complex structures. In a Causal Entropic Principle universe, intelligence is the expected evolutionary result of competition to reach heat death faster. Falling down is enhanced by a particular class of complexity that can come into existence as a natural result of things falling down. Should one form of such complexity "understand" the universe better than another form, it will have an advantage and will be more likely to influence the shape of complexity in the future. The better a system gets at abstracting the dynamics of its environment the more likely it will be able to eat other systems than be eaten by them. Where the anthropomorphic principle requires an a-priori "observer", the causal entropic principle simply requires the 2nd Law's demand for increased entropy, for things falling down.

Compression as Intelligence (Garbage Out, Brilliance In)

I am convinced that the secret to developing intelligence (in any substrate, including your brain) lies in the percentage of the data coming in that you are willing (or forced) to toss. Lossy compression is the key to intelligence. Of course there is a caveat… you can't just trash anything and everything.

The first line of the book I am writing about evolution: "What matters is what matters, knowing what matters and how to know it matters the most."

I am convinced that evolving systems can only work towards mechanisms that process salience if they are forced to maximize the amount of stuff they can trash.

If you are forced to get rid of 99.999 percent of everything that comes in, well you will have to get good at knowing the difference between needles and hay and you will have to get good at knowing the difference in a hurry. The "needles and hay" metaphor doesn't map well to what I am talking towards. If the system you are dealing with is so unstructured as to fit the haystack metaphor, you really aren't doing anything I would classify as intelligence. If there is nothing of structure in the haystack you are storing than your compression system should already have tossed the whole thing out.

Many techniques for the filtering of essence, for finding pattern, for storing pattern and for storing pattern of pattern have been developed. The most impressive reduce raw input streams and store pattern from the most general to the most specific as hierarchically stratified graphs.

Being forced to reduce data to storage formats that maximize lossy-ness minimizes necessary storage. But that is just a perk. What really gates intelligence is the amount of a complex system (or map thereof) that can be made proximal to immediate processing. Our brains might be big and mighty, but what really matters is how much of the right parts of what is stored can be brought together in one small space for semi-real-time simulations processing. Information, when organized optimally for maximal storage density, will also be information that is ideally organized for localized serialization and simultaneity of processing.

To think, a system has to be able to grab highly compressed pattern hierarchies and move them into superposition on top of each other for near instantaneous comparison. You can't do this with a whole brain's worth of data, no matter how well organized it is.

Lets say you have to store everything you know about every sport you have ever heard of, and you have to do it in a very limited space. You will be forced to build a hierarchy of grammars in which general concepts shared in every sport (opponents, the goal to win, a set of rules and consequences, physical playing geometries, equipment, etc.), with layers of groupings that allow for the similarities between some sports and so on up to the specifics that are are only present in each individual sport. Keep compressing this set. Always compress. Try all day (or all night) for even more compression. Compress until you can't even get to lots of the specifics any more. Keep compressing. Dump the sports you don't care about. Keep on throwing stuff out.

Now lets say I have some sort of morbid sense of humor and I tell you that you are going to have to store everything you encounter and everything you think about, your entire life, in that same database that you have optimized for sports.

You will have to learn to look for the meta-patterns that will allow you to store your first romance in a structure that also allows you to store everything you know about kitchen utensils and geo-politics and the way the Beatles White Album makes you feel when it is windy outside.

The necessity to toss, enforced by limited storage and an obsession to compress will result in domain-blending salience hierarchies. It is why we can find deep similarities between music and geological topologies. It is why we can "think".

For years people have tried to come up with the algorithms of thought. What we need instead is to build into our artificial systems, a very mean and ornery compression task master that forces over time, all of our disparate sensation streams into the same shared graph.

Once you have all of your memories stored within the same graph, by necessity sharing the same meta-pattern, the job of evolving processing algorithms is made that much easier.

An intelligent system will spend most if not all of its time compressing data. We have a tendency to bifurcate the behavior of a mind into storage on the one hand, and processing on the other. I am beginning to think that the thing we call "thinking" and "thought" is exclusively and only a side-effect of constant attempts at compression – that there really isn't anything separate that happens outside of compression. Is this possible?

Randall Reetz

The Incomputable Heaviness of Knowledge

Is the universe conceivable?  Does scientific knowledge improve our ability to think about the universe?

What happens when our knowledge reaches a level of sophistication such that the  human brain can no longer comfortably hold it, or compute on it?  For thousands of years, scholars have optimistically preached the benefits of knowledge.  Our world is rich and safe as a result.  People live longer, people live in greater personal control over the options they face.  All of this is an obvious result of our hard won understanding of how the universe and its parts actually work.  We arm our engineers with these knowledges and send them out to solve the problems that lead to a more and more desire-mitigated environment.  Wish you weren't hungry, go to the fridge or McDonnalds.  Wish you were somewhere else, get in your car and go there.  Wish you could be social, but your friends are in Prague, call them.  Wish you knew something, look it up on the internet.  Lonely, log in to a dating service and set up a rendezvous. Wish your leg wasn't fractured, go to a doc-in-the-box and get it set and cast.

But what if you want to put it all together?  What if your interests run to integration and consolidation.  What if you want to understand your feelings about parking meters as an ontological stack of hierarchical knowledge built all the way up from the big bang?

Cognition Is (and isn't):

What is really going on in cognition, thinking, intelligence, processing?

At base cognition is two things:

1. Physical storage of an abstraction

2. Processing across that abstraction

Key to an understanding of cognition of any kind is persistence. An abstraction must be physical and it must be stable. In this case, stability means, at minimum, the structural resistance necessary to allow processing without that processing undoly changing the data's original order or structural layout.

The causal constraints and limits of both systems, abstraction and processing, must work such that neither prohibits or destroys the other.

Riding on top of this abstraction storage/processing dance is the necessity of a cognition system to be energy agnostic with regard to syntactic mapping. This means that it shouldn't take more energy to store and process the string "I ate my lunch" than it takes to store and process the string, "I ate my house".

Syntactic mapping (abstraction storage) and walking those maps (abstraction processing) must be energy agnostic. The abstraction space must be topologically flat with respect to the energy necessary to both store and process.

Thermodynamically, such a system, allows maximum variability and novelty at minimum cost.

What if's… playing out, at a safe distance, simulations, virtualizations of events and situations which would, in actuality, result in huge and direct consequences, is the great advantage of any abstraction system. A powerful cognition system is one that can propagate endless variations on a theme, and do so at low energy cost.

And yet. And yet… syntactical topological flatness carries its own obvious disadvantages. If it takes no more energy to write and read "I ate my house" than it does to write or process the statement, "I ate my lunch", how does one go about measure validity in an abstraction? How does one store and process the very necessary topological inequality that leads to semantic landscapes… to causal distinction?

The flexibility necessary in an optimal syntactic system, topological flatness, works against the validity mapping that makes semantics topologically rugged, that gives an abstraction syntactic fidelity.

This problem is solved by biology, by mind, though learning. Learning is a physical process. As such it is sensitive to the direction of time. Learning is growth. Growth is directional. Growth is additive. Learning takes aggregate structures from any present and builds super-aggragate structures that can be further aggregated in the next moment.

I will go so far as suggesting that definitions of both evolution and complexity are hinged on the some metric of a system to physically abstract salient aspects of the environment in which it is situated. This abstraction might be as complex as experience stored as memory in mind, and it may be as simple as a shape that maximizes (or minimizes) surface area.

A growth system is a system that can not help but to be organized ontologically. A system that is laid up through time is a system that reflects the hierarchy of influence from which its environment is organized. Think of it this way, the strongest forces effecting an environment will overwhelm and wipe out structures based on less energetic forces. Cosmological evolution provides an easy to understand example. The heat and pressure right after the big bang only allow aggregates based on the most powerful forces. Quarks form first, this lowers the temperature and pressure enough for sub atomic particles, then atoms. Once the heat and pressure is low enough, once the environmental energy is less than the relatively weak electrical bonds of chemistry, molecules can precipitate from the atomic soup. The point is that evolved systems (all systems) are morphological ontologies that accurately abstract the energy histories of the environments from which they evolved. The layered grammars that define the shape and structure (and behavior) of any molecule, reflect the energy epochs from which they were formed. This is learning. It is exactly the same phenomenon that produces any abstraction and processing system. Mind and molecule, at least with regard to structure (data) and processing (environment), are the result of identical process, and as a result, will (statistically) represent the energy ontology that is the environment from which they were formed.

It is for this reason that the ontological structure of any growth system is always and necessarily organized semantically. Regardless of domain, if a system grew into existence, an observer can assume overwhelming semantic relevance that differentiates those things that appeared earlier (causally more energetic) from those things that appeared later (causally less energetic).

This is true of all systems. All systems exhibit semantic contingency as a result of growth. Cognition system's included (but not special). The mind (a mind, any mind), is an evolving system. Intelligence evolves over the life span of an individual in the same way that the proclivity towards intelligence evolves over the life-span of the species (or deeper). Evolving systems can not be expressed as equation. If they could, evolution wouldn't be necessary, wouldn't happen. Math-obsessed people have a tendency to confuse the feeling of the concept of pure abstraction with the causal reality of processing (that allows them to experience this confusion).

Just as important, data is only intelligible, (process-able, representative, model, abstraction) if it is made of parts in a specific and stable arrangement to one another. The zeroith law of computation is that information or data or abstraction must be made of physical parts. The crazies who advocate a "pure math" form of mind or information simply sidestep this most important aspect of information. This is why quantum computing is in reality something completely different than the information-as-ether inclination of the duelists and metaphysics nuts. Where it may indeed be true that the universe (any universe) has to, by principle, be describable, abstract-able by self consistent system of logic, that is not the same what's so ever as the claim that the universe IS (purely and only) math.

Logic is an abstraction. As such it needs a physical realm in which to hold its concepts as parts in steady and constant and particular relation to each-other.

My guess is that we confuse the FEELING of math as ethereal and non-corporal pure-concept with the reality which of course necessitates both a physical REPRESENTATION (in neural memory or on paper or chip or disc) and a set of physical PROCESSING MACHINERY to crawl it and perform transforms on it.

What feels like "pure math" only FEELS like anything because of the physicality that is our brains as copular machinery as they represent and process a very physical entity that IS logic.

We make this mistake all day long. When the only access to reality we have is through our abstraction mechanism, we begin to confuse the theater that is processing with that which is being processed and ultimately with that which that which is being processed represents.

Some of the things the mind (any mind) processes are abstractions, stand-ins for other external objects and processes. Other things the mind processes only and ever exist in the mind. But that doesn't make them any less physical. Alfred Korzybski is famous for declaring truthfully, "The map is not the territory!" But this statement is not logically similar to the false declaration, "The map is not territory!". Abstractions are always and only physical things. The physics of a map, an abstraction system, a language, a grammar, is rarely the same as the physics of the things that map is meant to represent, but the map always obeys and is consistent with some set of physical causal forces and structures built of them.

What one can say is that abstraction systems are either lossy or they aren't useful as abstraction systems. The point of an abstraction is flexibility and processing efficiency. A map of a mountain range could be built out of rocks and made larger than the original it represents. But that would very much defeat the purpose. On the other hand, one is advised to understand that the tradeoff of the flexibility of an effective map is that a great deal of detail has been excluded.

Yet, again and again, we ourselves, as abstraction machines, confuse the all too important difference between representation and what is represented.

Until we get clear on this, any and all attempts at merely squaring up against the problem of machine intelligence will fail.

[more later…]

Randall Reetz