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.

Building Pattern Matching Graphs

I talk a lot about the integral relationship between compression and intelligence.  Here are some simple methods.  We will talk of images but images are not special in any way (just easier to visualize).  Recognizing pattern in an image is easier if you can't see very well.

What?

Blur your eyes and you vastly reduce the information that has to be processed.  Garbage in, brilliance out!

Do this with every image you want to compare.  Make copies and blur them heavily.  Now compress their size down to a very small bitmap (say 10 by 10 pixels) using a pixel averaging algorithm.  Now convert each to grey scale.  Now increase the contrast (about, 150 percent).  Store them thus compressed.  Now compare each image to all of the rest: subtract the target image from the compared image. The result will be the delta between the two. Reduce this combined image to one pixel.  It will have a value somewhere between pure white (0) and pure black (256), representing the gross difference between the two images. Perform this comparison between your target image and all of the images in your data base. Rank and group them from most similar to least.

Now perform image averages of the top 10 percent matches. Build a graph that has all of the source images at the bottom, the next layer is the image averages you just made. Now perform the same comparison to the 10 percent that make up this new layer of averages, that will be your next layer. Repeat until your top layer contains two images. 

Once you have a graph like this, you can quickly find matching images by moving down the graph and making simple binary choices for the next best match. Very fast. If you also take the trouble to optimize your whole salience graph each time you add a new image, your filter should get smarter and smarter.

To increase the fidelity of your intelligence, simply compare individual regions of your image that were most salient in the hierarchical filtering that cascaded down to cause the match. This process can back-propagate up the match hierarchy to help refine salience in the filter graph. Same process works for text or sound or video or topology of any kind. If you have information, this process will find pattern in it. Lots of parameters to tweak. Work the parameters into your fitness or salience breading algorithm and you have a living breathing learning intelligence. Do it right and you shouldn't have to know which category your information originated from (video, sound, text, numbers, binary, etc.). Your system should find those categories automatically.

Remember that intelligence is a lossy compression problem. What to pay attention to, what to ignore. What to save, what to throw away. And finally, how to store your compressed patterns such that the graph that results says something real about the meta-paterns that exist natively in your source set. 

This whole approach has a history of course. Over the history of human scientific and practical thought many people have settled in on the idea that fast filtering is most efficient when it is initiated on a highly compressed pattern range. It is more efficient for instance to go right to the "J's" than to compare the word "joy" to every word in a dictionary or database. This efficiency is only available if your match set is highly structured (in this example, alphabetically ordered). One can do way way way better than alphabetically ordered lists of 3 million words. Lets say there are a million words in a dictionary. If one sets up a graph, an inverted pyramid, where each level where the level one has 2 "folders" and each folder is named for the last word in the subset of all words at that level divided into two groups. The first folder would reference all words from "A" to something like "Monolith" (and is named "Monolith") The second folder at that level contains all words alphabetically larger than "Monolith" (maybe starting with "Monolithic") and is named "Zyzer" (or what ever the last word is in the dictionary). Now, put two folders in each of these folders to make up the second tier of your sorting graph. At the second level you will have 4 folders. Do this again at the third level and you will have 8 folders each named for the last word in the graph referenced in the tiers of the graph above them. It will only take 20 levels to reference a million words, 24 levels for 15 million words. That represents a 6 order of magnitude savings over an unstructured sort. 

A cleaver administrative assistant working for Edward Hubble (or was it Wilson, I can't find the reference?) made punch cards of star positions from observational photo plates of the heavens and was able to perform fast searches for quickly moving stars by running knitting needles into the punch holes in a stack of cards.

Pens A and B found their way through all cards. Pen C hits the second card.

What matters, what is salient, is always that which is proximal in the correct context. What matters is what is near the object of focus at some specific point in time.

Lets go back to the image search I introduced earlier. As in the alphabetical word search just mentioned, what should matter isn't the search method (that is just a perk), but rather the association graph that is produced over the course of many searches. This structured graph represents a meta-pattern inherent in the source data set. If the source data is structurally non-random, its structure will encode part of its semantic content.  If this is the case, the data can be assumed to have been encoded according to a set of structural rules themselves encoding a grammar.

For each of these grammatical rule sets (chunking/combinatorial schemes) one should be able to represent content as a meta-pattern graph. One of the graphs representing a set of words might be pointers to the full lexicon graph. A second graph of the same source text might represent the ordered proximity of each word to its neighbors (remember the alphabetical meta-pattern graph simply represents the neighbors at the character chunk level).

What gets interesting of course are the meta-graphs that can be produced when these structured graphs are cross compressed. In human cognition these meta-graphs are called associative memory (experience) and are why we can quickly reference a memory when we see a color or our nose picks up a scent.

At base, all of these storage and processing tricks depend on two things, storing data structures that allow fast matching, and getting rid of details that don't matter. In concert these two goals result in a self optimization towards maximal compression.

The map MUST be smaller than the territory or it isn't of any value.

It MUST hold ONLY those aspects of the territory that matter to the entity referencing them. The difference between photos and text: A photo-sensor in a digital camera doesn't know for human salience. It sees all points of the visual plane as equal. The memory chips upon which these color points are stored see all pixels as equal. So far, no compression, and no salience. Salience only appears at the level of where digital photos originate (who took them, where, and when). On the other hand, text is usually highly compressed from the very beginning. What a person writes about and how they write it always represents a very very very small subset of 

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

Computing: What Went Wrong

The year, 2010.  The state of computing?  Applications that build obfuscating document types that act as black boxes, hiding and separating information and intent.  File systems that store these documents largely blind of their content, of the context of their origination, and of the associations hidden in the meaning that binds them to the flow of the author's life and work.

There are but a finite set of ways that information needs to be associated.  Yet almost none of these association types are supported by our computers.  If you don't know before hand that everything you want to do will fit into the format of a linear text document, or the grid of a spread sheet, or the fields and records of a data base, well you might as well not even start.  If you want some information in one of your documents to reference information in another, well you had better be content with copying and pasting (live links are forbidden in all but the most expensive (and self-restrictive) application "suites".

Why is this true?  Why don't we yet have computers that can compute?  Mainly because the application layer is the WRONG place for the association of information.  The file system is the RIGHT place.  Information must be related and associated in a strata far below the application layer.  The only authority that should be granted to applications is user affordance – how humans are helped through the assignment, understanding, and management of the associations within their life's data.

To do this, the data (file) management layer needs to be beefed up (and the application layer slimmed down).  Where today's file systems only know a document by its wrapper (name, enclosing folder, parent application, document type, size, date, and on-disc storage address), a true data model layer would "understand" and dictate the structure of all of the ways information is related both within and between "documents".  In fact, in a data-model driven architecture, documents become arbitrary "collections", "instances", and "presentations" specific to the context of presentation or use.  The underlying data and associations between data from which documents are derived remain intact, separate, and agnostic of the documents that serve to reference, blend, display, and associate.

In the proposed data-model driven architecture, applications don't define information association, they must call on the data model to ask which types of associations are allowed and how these associations dictate data type, grammatical hierarchies, and chunking.  Applications build documents not as strings of binary, but from pointers into the information content stored according to the meta-archetectural rules of the master data model.  A document becomes instead an instance of assembled data and data associations (either frozen or live)… more like the "edit decision lists" that video and music editing professionals use to assemble linear streams of media from multiple sources.

Today's computational model's awkward emphasis on application authority and autonomy promotes an informational ecosystem that promotes informational islands dictated by the whims of application developers.

[to be continued]

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

Biology Is Too Slow!

Humans are pumping a lot of energy around. When it comes to energy we don't mess around. We like our energy highly concentrated. We dig it up, refine it, convert it, and pump it through wires or pipes or the air like there is no tomorrow.

Nature is adaptive. Right? Nature finds a way. Right? So where are the animals and plants that suckle upon high power lines, that find their adaptive way into fuel tanks and batteries? Surely they could. Surely the same nature that goes gaga around mid ocean heat vents and can learn to metabolize the worst toxins we can throw into ponds... that good old adaptive nature should find a way to co-evolve with 50 thousand volt transmission lines.

And there are other (new) tits for nature to suckle. I fully expect our air to become less and less transparent to radio transmissions. If we can build devices that can grab radio energy right out of the air.… surely airborne molds and other microorganisms can do so. Are they? Doesn't look like it. What weird life forms would be best suited to radio-metabolism? Plants grab photons in the visible (radiation) band. Photosynthesis (in plants) is a respiratory affair - requiring oxygen and nitrogen for the primary reactions, but they also rely on heavy and rigid structural support to get up into the air where they can maximize their surface interface and solar exposure. Actually, when you think about it, a plant would be more efficient if it spent no energy fighting gravity, and instead laid flat on the surface of the land. Plants must only grow into the air to compete away from shade the shade of other plants and to increase respiration surface area.

Anyway, and this is a bit of an aside, but would there be a way for lighter than air super-colonies of single celled animals to maximize access to radio energy without the need for the heavy structure and vascular transport terrestrial plants employ? Maybe the radio scenario is ludicrous. Surely there is lots of background microwave energy constantly streaming by. Surely radio waves have been around as long as biology has been around. If radio was a good source of energy, nature would have already found a way. Maybe big bang radiation doesn't pack much of a wallop. Is it possible that communication intended radio is more energetic? More localized. Easier to exploit. I can imagine some type of group-dynamic in which individual floating animals or proto-animals learn to orient themselves such that they become a reflective parabola or fresnel lens concentrating radio energy to a focal point where other animals absorb the energy in some sort of symbiotic bio-community. Many other scenarios are conceivable.

Are plants learning to seed near highways to take advantage of air movement and carbon dioxide? There are a million ways in which human activity effects environments in ways that provide energy and stability clines. Surely life is reacting in step.

The pace of culture is so much faster than most organisms can genetically respond. The smallest organisms with the shortest life spans that have the greatest populations spread over the largest geographies are the organisms most likely to take advantage of our frenetic environmental messings.

Are they? Is anyone paying attention?

What is computing?

This is the most important question of our time… yet so rarely asked. Computing technology increasingly shapes every aspect of human behavior, culture, resource use, health, commerce, and governance. A passive stance on the question that effects all other questions is increasingly dangerous to the future of all humans, of life, of evolution itself.

In the 60's we created NASA, an elaborately funded research program to uncover the knowledge and develop the technology to "go to the moon". Yet one would be hard pressed to justify the cost to society of contraptions that do nothing more than take a few people to a near-by rock… almost nothing of the NASA program can be used outside of the narrow focus of getting a few tens of miles off the surface of Earth (at tens of millions of dollars per pound).

Ironically, and inadvertently, the practical mathematics, programming, and computational techniques developed and honed by NASA in the pursuit of its expensive and arguably impractical goals may be the only pertinent contribution to show for the tens of trillions of dollars spend on this ill-concieved and irrational "research" program.

Talk about putting the cart before the horse… akin to building a global library system and book binding before developing a written language.

We are surrounded by lifeless rocks. We didn't need to send a few Air-force test pilots to the moon to figure that out. The practical scope of our chemically propelled rockets hardly avails us to the nearest little frozen or boiling neighbor planets in this corner of this one little Solar System. Ever attempt a phone conversation with 40 min. gaps between utterances?

The interesting stuff in this Universe (at least the small corner we have access to) is right here on our little Earth. It is us… and more than that, it is not so much what we have done, but what we will do and how what we will do effects what other future things will do because we set them into motion. That is our job. In a very real way, we are the first things that understand the job description despite the fact that it has always been there and has always been the same. This understanding should give us a leg up on the process. Should.

There are two kinds of knowledge: the first, historical, the second, developmental. When we go somewhere, we do nothing more than uncover that which already is. Compare this to development, where we create things that never were. In this universe, if there was a force that was prescient in creating one star or planet, that same force must have been prescient in the creation of Earth. We don't have to go to Mars to find the forces that created Earth. And we certainly don't need to send humans over there even if we do want intimate knowledge of a place like Mars.

At any rate, computing is a universal process. Computing is agnostic to domain. You can compute about particle physics and you can compute about knitting. Computing is an abstraction processing medium. Computing is what brains do. Computing is not restricted to the category that is biological minds. Learning how to compute is learning how to discover. The goal becomes the unknown… becomes un-prejudiced developmental discovery. The machinery of pattern matching… of salience… of the perception of essence across domains.

I am obsessed with this biggest "why" of computing. I don't think the computational "why" can be separated from the biggest "why" of existence in general... of evolution… of the march of complexity.

The convergence of thermodynamics (the way action effects energy dissipation) and information science (the relative probabilities of structure and the cost of access, processing and transference) guide my approach to these questions. Least energy laws dictate the evolution of all systems. Computing is evolution. Abstraction systems allow prediction. Prediction grants advantage. Advantage influences the topology of the future. The better a system gets at accurately abstracting it's environment, the more it will influence the future of abstraction systems. Computing is the mechanics of evolution... always has been. Are we designing computing to this understanding of the methodology of complexity handling?

Lets suppose we gave the scientists at NASA a choice. We ask them, "What technology represents a greater potential towards the eventual understanding and even physical exploration of the Universe, rocket engines or computers?", What would be the rational and obvious answer? If we ever hope to get any real distance in this universe it won't be by burning liquid oxygen and kerosene. Most things in this universe are millions of years away even at the speed of light. Rocket engines hardly move at all when compared with even the too-slow speed of light. Getting anywhere in this universe will demand tunneling beneath the restrictions that are space and time… no rocket engine will ever do that for us. I am not an advocate for space exploration, but if I was, I would be pushing computation over rocket propulsion.

It is time to advocate a culture wide push towards the advancement of an ever-expanding understanding of computing. To the extent we succeed, all of the future will be defined by and fueled by our discoveries. If we choose instead to spend our limited and most expensive money towards rockets we had better hope the universe can be understood through the understanding of explosions and destruction and spending long periods of time floating in space. Come on people! Think!

[ more to come… ]