some musette de cour from the eighteenth century. it might seem impossible, but the drones are over 2 meters long: their length is folded into that seemingly tiny drone sticking out (much like modern shuttle pipes).

we’re slowly broadening our sights, and abandoning the tenuous details of the small things and moved on to bigger things. by now if you accepted my premises, we can begin to see a landscape where minds are not only available for human beings, but also to all kinds of matter in motion: molecules, cells, organisms, animals, plants, individuals and collectives. it might not seem clear at first, but this idea of mind includes and predicts that two individual minds can be seen, when communicating between themselves, as a bigger mind with a bigger mindspace than each of the individual ones. obviously, it’s not this simple as we saw before, but it’s interesting to see it this way. minds are arbitrary boundaries around moving information.

think about a simple every day item, like a cellphone. it is impossible for a single human being to fully understand how it is done and built. if we analyze this problem using the tools of the model i provided, we would see that the information required to build a cellphone is bigger than the capacity of a single mind, but not of a collective mind with enough abstractions. this means that certain material endeavors are impossible for the individual to understand, but possible for a collective to understand. we will dive into collectives soon.

but before we discuss collective information, i’d like to describe the principle of information inertia. if we accepted the principles that information is the specific physical arrangement of things, it is natural to imagine that to change this arrangement we must do work. the bigger the change, the bigger the amount of work. this means that if a specific arrangement exists, it will tend to stay that way. it also means that if an arrangement can transmit its information to another arrangement, the minimum amount of change possible is a copy of itself. from this follows that if we follow the principle of minimum energy, we also follow the principle of least originality. everything about basic thermodynamics says that complexity shouldn’t exist, and whenever it does, it will tend to preserve its structure versus complicating itself.

it is no surprise, therefore, that in this framework, we can extract the understanding of why things copy themselves (genes, cells, animals, ideas) in a way that tends to preserve their own information. it is not that they have a natural selfish tendency. in my opinion, it is simply that the laws of nature are rigged to make originality very hard, and laziness very easy. the laziest activity for a replicator is to replicate itself. to evolve will only happen under pressure to do so, otherwise, the tendency will be to stabilize. note that i extracted this only from the initial premises. with it, we can see evolution and reproduction in a simpler, more general light. there is no need to define what a thing is to obtain these predictions. that evolution will happen at every level where information is exchanged, and that it will happen against a natural tendency to inertia. i suspect that laws like the laws of genetics, memetics and so on will keep popping up in odd fields where this wouldn’t be expected, and that this pattern will be referred with a new word, but will be essentially the same process: the information inertia guaranteeing the replication, and the non random possible distribution of information in our world as the non random selection.

i won’t dig deeper since this seems like a concept that is simple enough as it is. it predicts that it is more likely to see a copy than a difference, the principle of least surprise. this hints at the idea that surprises are valuable events in nature, and in essence, against its very basic rules (though they allow for them).

more gaita sanabresa. previously we summarized the last few months of theoretical development. now i will begin exploring some of the consequences of the model i described. note that i am not claiming truth, i am merely describing the framework behind every argument i make. so let’s first explore what the consequences of accepting that minds are a generative space, or at least have equivalent properties.

the more you know, less you generalize.

knowledge, in a generative space, is quantified not by its expanded values, but by how many dimensions are present in the mindspace (the mindspace is the space made of all base concepts we have in our mind). for example, consider bagpipes. one individual only knows about scottish bagpipes. in his mindspace, the “bagpipe” line is every kind of scottish bagpipe: 3 drones, a mixolydian chanter and kilts. whenever anyone talks to him about bagpipes, they will imagine scottish music, the army, kilts and so on. this is the space expanded from the limited knowledge of a single type of pipes. now consider someone who has been exposed to more kinds of bagpipe. whenever someone speaks about bagpipes, this other individual won’t be able to imagine it without more information on what kind of bagpipes are we talking about. in his mindspace, it is insufficient to say “bagpipes” because he knows that “bagpipes” is a broad term applied to several different types of pipes, each with its set of drones (or no drone even), one or more chanters, and dozens if not hundreds of different musical modes and tonalities. the resolution to speak about pipes on the second individual is higher than the first one, but this also means that the second individual has a bigger bagpipe mindspace. he can expand the reality of bagpipes in more dimensions than the scottish one. but this means that to classify a new set of pipes, the first case will just say “scottish bagpipes” because that’s all he knows, versus the second that will say “that seems like the tunisian mezoued but with an eastern european drone”. less generalized, more refined, imply more knowledge. he projected a new set of pipes onto a big mindspace of many different pipes.

this is somewhat counter intuitive, as we tend to imagine that people that know laws of nature really know nature very well. in fact, i’m saying that knowing reality in one dimension only is actually restricting one’s knowledge of it. generalizing grows in the inverse proportion of the knowledge of the topic being generalized.

this only makes sense in a generative space. if, instead, we were dealing with brains and minds made of a quantified collection of independent concepts, assorted and fragmented, this would mean that the individual with the most pipe knowledge would be able to generalize the most because he had the biggest amount of information about pipes and a bigger vocabulary to express his generalization. what we see is the opposite: the richer the vocabulary, the more specific, therefore, less generalizing comment. this implies a mindspace that is generative and not simply a collection of information.

it is impossible to fully understand topics of which we have no prior knowledge of

consider that extra dimensions are added via new information that the system receives, like we saw above. for example, if we have the red and the green neuron as before, we cannot add the “yellow” until we see one and both our “bases” are active at the same time and “grow” a new abstraction for it (as i put before, another base or neuron). without this case, we will never encounter a case where both will be active. if we cannot “listen” to both red and green at the same time, each of them alone will call their data red and green respectively. it is only when they are put together via a higher level abstraction (a “yellow” neuron that collects replies from “red” and “green”) that we understand that in fact, both red and green are correct and together they can be identified as “yellow”.

the fact that structurally we are capable of seeing yellow (thanks to having green and red neurons) means nothing if we never observe a yellow data point. even though today we can induce yellow’s existence from the green and red, it comes from our prior knowledge that green and red together make yellow, which in turn comes from, you guessed it, the fact that we observed that red and green make a yellow. this apparently circular argument serves only as a proof that reality is the primary source of information, and that even induced effects use symbols that they themselves were extracted from reality. we are capable of recombining and expanding existing values (like the two colors we can detect), but we cannot conceive, imagine or even work with, concepts outside our observed dialect. we were born without any capacity to see infrared, so we couldn’t imagine it until an artifact (thermometer) could transform that information into one we can see (turned heat into the growing length of a mercury column). in this sense, our conceptual capacity is permanently limited by how much we can project into our own sensory system, and then induce from it its patterns.

an easier example to understand is being given a book about the life of bees in chinese when you don’t speak the language but are an expert on the life of bees. since you have no prior understanding of chinese, you cannot project its information onto your own mindspace. this means that even though this book is part of the reality you know via your subjective projection, it is impossible for you to access this new set of data because you possess no equivalence between your subjective symbols and the chinese symbols. you cannot understand the book, even though the subject is familiar to you, because it is represented in a mindspace that is orthogonal to yours, i.e., shares no single common base concept.

one of my favorite examples of this was a particularly fun IQ test i took online. everything was working out great, until i reached a basic mathematics for counting. for some reason, the script miscalculated my location and gave me numbers in hebrew. i couldn’t understand the numbers, so i failed the test below retarded level for mathematics, even though i had done the same type of tests before and had a normal score. the system assumed that i had a non-orthogonal mindspace on which they could project the test, and with it, assess my knowledge. in practice, it demonstrates that it is impossible to understand a test if we don’t understand the letters it is written on, implying that prior knowledge is required for any activity. by prior i don’t mean we’re born with it, i merely mean that it is present in the system when the event being analyzed occurs. i will deal more with this when we reach communication.

it causes generalizations that are powerful and false

if minds are generative spaces, then they can generate hypothetical observations beyond their original observation. for example, the basic understanding of quantity, i saw one stone, then two, then three, i can generalize and say i can count any amount of stones. this is a valid mathematical theory that fits very well with reality, but it is flawed because it induces it can go on forever: there is no reality check on the generalization. this means that we can imagine infinite rocks even though we have a finite supply of rocks (see our axioms). generalizing is only possible in a generative space. if we based our thoughts exclusively on data, it would be impossible to imagine beyond the three rocks we had seen before. induction is a side effect of a generative space, and must be fed back through reality to check the validity of certain inductions. this also means that mindspaces can contain infinite quantities, not because they can represent infinite quantities by enumerating them (1, 2, 3, …), but actually by just representing the base for that space: number x is between 1 and infinity. this requires only the base of numbers and capacity for computation. a lot of infinities have very low computational complexity. as i said above, mindspaces can generalize very well thanks to their reduction of reality to a few key projections.

it is very common to see these generalizations in our every day life. in fact, the whole capitalist exponential growth seems to be based on this generative idea that infinity is real. even if the richest man in the world makes all the money there is to make, the matter used to quantify his wealth will still be a limited number, limited by the amount of information that can fit physically in reality. there is no infinity on our planet, only the concept of infinity, that exists in brains and minds. whether infinity exists in general is a very interesting topic. i feel inclined to say this question, if asked locally, the answer is no. globally, i don’t know.

communication is possible, but only efficient with a balance between mutual information (shared base vectors) and information entropy between the systems

if mindspaces weren’t projective spaces, communication would merely be the transmission of information from one point to another (much like we saw for mindless observers), so it would be possible for me to read a chinese book. but what we see is that this is not the case. for mindful observers, it is a requirement that there is some kind of internal mindspace that receives the new data. like the example above of the chinese book, the information carried has a certain mutual information and a certain entropy connecting the sender and the receiver. the issue here is that if we are dealing with a projective space, if there is no mutual information, the projection will be zero, effectively causing no transfer whatsoever. this is very counter intuitive: the efficiency of communication between minds requires that they already share some prior information (this sharing is the mutual information between the two minds). while it is true that i could learn chinese, i would do so by growing a new section of my mindspace that guarantees a non-zero projection: it guarantees that i have some mutual information to read the book. if minds weren’t projective, learning would be just “feeding” the symbols directly. we know this is not the case, and that minds need to be trained to be able to grow that new chunk of mindspace.

this means that communication between minds is worse than communication between reality and observers. it requires that of both that they have some mutual information, which, by definition, is not necessary to transmit. this also means that communication will be the most efficient with the mind itself, i.e., we are best understood by ourselves, even if we are permanently contradictory. my opinion is that this explains why we gravitate towards minds we share something with. communication is more efficient, and it is less likely that we’ll have a misunderstanding or a conflict. it also explains why we have so much trouble dealing with conflicting data versus our prior beliefs: if something has high mutual information, we do not need to grow new sections of mindspace to accommodate the knowledge, we can just reinforce what we already have. a projective space is more efficient at transferring information it already knows, than at transferring information it doesn’t know. this is incredibly different from a mindless observer that indiscriminately gathers information.

now, why is it that a projective space makes evolutionary sense versus other spaces? because it can reduce the complexity of reality that has an immense amount of data to simple dimensions that it can understand, no matter what size of the data being fed. in fact, the simpler the brain, the more powerful the generalization. a mind that can only tell light from dark will have no problem dealing with light from a light bulb, a firefly, a supernova or a star. this is both incredibly powerful and incredibly lazy at the same time: the simpler the mind, the less resources it will need and still it will be capable of powerful generalizations about reality. a very nice trick indeed. for the mathematically inclined, i shall give a full mathematical formulation of this soon.

with this comes a sobering consequence for complex minds: they are valued not by their capacity for generalization, but for their capacity to exquisitely represent abstract realities. not for their capacity to know reality (since their skin knows more about reality than their brain), but instead for their capacity to imagine, conceive and expand their interpretation of reality beyond observation. it is not that we see reality, but that we use it so we can see beyond it, and with it, visit infinite impossible landscapes just by closing our eyes.

some ney anban from iran. note the double chanter.

i have a portable chromatic tuner i carry with me all the time. it came with some basic lead batteries, and they eventually ran out. so i modded it for solar energy. an interesting side effect is that since the current is very low, we can actually charge the lead batteries. while some batteries do explode or leak, most can be recharged slowly. this is a risk we can take, since my cells are 75mA only.

materials

1. any chromatic tuner that runs on batteries
2. solar cells (i have a bunch of 1V/75mA cells that came from recycled toys, check ebay for that kind of stuff)
3. a diode that can handle 75mA (almost any diode you find on any scrap electronics from the trash can)
4. wire, soldering iron, voltmeter and diode meter (if you don’t know which one is the plus and minus)

schematic and pictures

results

it works. you can see the led lighting up in the last pic. looks a bit ugly because the cells aren’t a perfect fit to the tuner’s size, plus i made a board out of newspaper and glue layers to hold it, making it look even worse. that’s about it. considering lead batteries do not have memory, they can be recharged indefinitely. this might sound a bit strange, but as long as they aren’t completely drained, they can be reused over and over again. the main advantage of lithium is that it can be completely drained and still work, plus energy density and weight, but there’s no particular reason not to use these as the chargeable cells in this case. also, the charge voltage is very near the operating voltage (3.3V versus 3V) which means it could work only with sunlight. however, it’s always better to have batteries between the two as they stabilize the voltage.

some dazkarieh this time. i love this song, they have a new album out.

in the previous series we saw how information can be stored and processed by physical entities without any elaborate structure. we also saw how the speed at which it is both processed and stored has increased as life became more complex. today, i will summarize again the whole series from the beginning, including our formulation for minds.

1. reality R is a set of N discrete things repeated M times
2. the distribution of these things is non-random (i.e., it has patterns)
3. this distribution is not static, it can change thanks to free energy, leading to specific distributions that can be exceptionally complex (e.g., minds)
4. the development of complexity comes simply from random fluctuations and non random possibilities for existence (e.g., anti matter particles still form, but they are annihilated by reality (see 2.), hence the evolutionary trait present in all layers of reality)
5. these specific arrangements versus the general ones can be quantified using arbitrary boundaries, they are arbitrary because there are no boundaries in reality
6. minds represent a specific subset of R, whose distribution is non random, which is a subjective representation of the whole reality. this means minds can be conceived depending on the boundary: draw one around a planet, an animal, a cell, an atom, and you can define its observations and its thoughts
7. complex minds made of neurons represent not only observations themselves, but their structure allows for thoughts to generate hypothetical observations, i.e., by virtue of their hierarchical structure, they can generalize patterns so much that impossible distributions can exist as thoughts. this means that even fantasy is real, because it exists physically as a distribution of things (in this case for example, the electrical signals and wiring of the person with the idea).

note that it is not said whether all of the constituents of reality are observable by us, human beings, i.e., of the N things reality is made of, i make no claim on how many, and what type, are they made of. this means that if someone wants to use this model to justify spirituality, they can do so, all they have to do is say there is a “spirit particle”. and though i accept it, as i said before, the quest is for the simplest generative space with minimum distortion versus reality, and the more symbols are added that do not improve fitting, but instead distort it, the worse. but in essence, this is an objective formulation of a subjectivist model. it sounds completely ridiculous, but that’s exactly what it is. it harmonizes both subjectivity and objectivity in a way in which both are complementary, not opposing views.

regarding the minds as generative spaces, i will provide a simplistic formulation on how to look at complex brains and see them at work, and will provide predictions and tests that can be done to verify this model.

as we saw first, we are dealing with a limited and discrete set of symbols to work with. i postulate that brains create multi-dimensional spaces on which they project their sensory signals (sensory signals are not observations, they are interpretations, thoughts done over classification of input). the best way to imagine this is to imagine a two dimensional brain (two neuron brain). one of its neurons can classify red, and another can classify green. if we shine red, the red neuron lights up. if we shine green, the green neuron lights up. but if we shine yellow, both light up. now, each neuron “knows” only one color, but combined they can represent colors that alone they couldn’t represent. this is typical of many dimensional systems. two lines, one dimensional, once put perpendicular to each other can now represent all points in a plane, even though each one of them has only one dimension, together they’ve expanded “each other’s” representation of reality. red could not see yellow without green, and vice versa.

this means, and this is mostly, as i said, a very broad generalization i am making, that a single neuron is a base vector of that multi-dimensional space. so these two color neurons can represent yellow because when combined together they create a base vector for a 2 dimensional space versus two 1 dimensional spaces. obviously this view is a bit influenced by neural networks (which actually estimate an n-dimensional polygon and hyperplanes). but i take a simpler approach: i’m thinking just in terms of axis, projections and expansions.

each neuron is a base vector, and together they create the “brain” (a base matrix). the imaginable space of a “brain” is, therefore, the space expanded from these base vectors. note that it might be that not all neurons are independent of each other, that would be expectable, so this actually overestimates the capacity of this system. the main point is that i’m saying we are dealing with generative complexity, not actual complexity: the ideas that flow are consequence of the possibilities of representation, and this representation is a consequence of a reducible set of base “structures”.

i will develop on this soon, with more concise definitions, and discuss this whole block of ideas in terms of their predictable consequences in understanding the interaction of living things.

a “repasseado”, one of the many portuguese folk dances. this one is particularly fun to dance to. playing is one of the last makers of mirandese pipes, master célio pires.

after the previous definitions, we can move on to bigger structures easily, since the principle is the same. a brain is included in the previous definitions, but it is different in a very important aspect: its feedback loop for thought is done by both reality and its own internal processes: brains (and complex living things) can do work on themselves effectively changing their own information. this has the advantage of not taking hundreds of trials and errors through evolution to produce meaningful responses to environmental pressures. note that DNA can actually act on itself by making proteins that inhibit or change its own structure. overall, this process is more like a spaghetti of loops mixing internal structure and work and external structure and work. the difference i’m focusing on is both how long it takes to cause information to change (the thought ‘clock’), and how much information can be stored (the ‘capacity’). the boundary of “internal” and “external” is artificial, and can be drawn arbitrarily. i tend to prefer a boundary between the part that does work and the part that just provides energy for the work. the first is all things in “motion”, the latter is just the fabric in which they stand.

now, we saw previously that it is not sufficient to account for the information represented, it is also necessary to account for the information reading machine, e.g., knowing a DNA sequence is meaningless without the appropriate cell machinery to process it and an environment that triggers its responses. this additional information should be assumed included in everything i describe as “information”. as i said, it is not sufficient to know the words, it is necessary to know what the words mean. since we generalized the definition of mind and thought, a cell + DNA is actually a small mind with very simple thoughts. its non-random response to the environment signals some sense of understanding of the real world: too hot, too cold, too moist, too dry, stressed, peaceful, all these chemical signals that bathe it are interpreted and processed according to some internal representation of reality. this representation is the chemical structure of the cell itself in its whole: it must include all the parts necessary for the accurate observation and change of reality’s constituents.

the same holds true for a brain of any kind with a big difference. a cell might go through reproduction to change its DNA, but a brain (a brain is a mind made of connected cells we call neurons) can do so on the fly by rewiring itself. a brain, thanks to the capacities of its neurons, can change its observations and its actions easily without necessarily going through the reproductive process (the actions are changed thanks to its relative power over its supporting systems). this allows for it to be a contained self-reorganizing system. but what defines the fitness of this organization is, obviously, reality. the brains that are better at reorganizing themselves to fit an optimal response to reality are favored, and the ones that don’t, aren’t. so it’s plausible to expect that brains are good at observing reality efficiently, otherwise they wouldn’t survive. more on this later.

this representation of information (which we analyzed previously) is quantifiable, like every other before it. but now we have to deal with a self-modifying observation, instead of a passive one (like our planet analogy). a planet has no choice on what information it will store: it is just a consequence of its surroundings. but a mind, through self-modification, can create a subset of information from all the information it gets. both are subjective observers (their observation is a subset of reality), yet the brain can take that subset and change it. this change, sometimes called reflection or learning, can exist in simple and elaborate brains. we like to think that only humans reflect on what they are doing, but i argue that reflection and learning are a consequence of active self-modification.

the reality observed by brains is distorted not only by its location (like the location of a planet), but also by its structural responses and modifications. this means that brains are worse than passive observers at representing reality. this might seem counter intuitive, but as we said before, we may be able to imagine other planets, but this is a long way away from what a dweller of that planet would observe. even a pebble on its surface will know more about the history of the planet than all our telescopes combined. this should be a humbling perspective above all. we must understand that our observations as consequence of our brains are distorted both by being exposed to a limited subset of data and a filtered, biased alteration of the said data. the wrinkles of our skin, the spots and wounds are a much better catalog of what happens to us than our interpretations of what happened to us.

now, there is something very interesting about brains made of neurons, which in my opinion is quite surprising but makes perfect sense. it is impossible to represent reality in a few billion neurons accurately. but, as we saw in our information analysis of abstraction, it is possible to represent increasing quantities of information by creating abstractions. an abstraction is no more than a subset of common information extracted from a large data set. like in my earliest examples, consider that we have the data set {a,b,a,b,a,b}. we can abstract it by saying it is 3 times {a,b}. note that this is not the same as knowing the entire set: it is merely creating an internal observation that has minimal distortion versus the real thing, saving resources, assuming its elements are interchangeable. it is possible, therefore, to represent billions of tons of steel as a mix of a concept of quantity and a concept of steel. note that you won’t be able to point out atom number x in the bulk of steel (therefore your data will not be fully accurate), yet you have a general sense of what it’s made of. this process of pattern recognition and abstraction makes brains naturally scientific. i’m going to get in trouble because of this one i know it. but in this lies a physical flaw in what is abstraction: all the elements of the set abstracted as the same category are considered to be interchangeable and the same, i.e., and iron atom can be swapped by another one with no trouble. while this might turn out to be true for some cases (like chemical elements), it is completely false for other cases (like people). yet our brains are capable of extrapolating data with equal power regardless of the category used. this is not because our brains have elaborate internal representations of both iron atoms and people. it is because the neurons themselves make no distinction between a pattern that is a person and a pattern that is iron. the fact that we can generalize does not come from richness of observation and understanding, instead, comes from a deep lack of understanding of things. this understanding of the differences between things in reality is something that is fine tuned by our interaction with it. to a young child, a puppet is just as alive as a human being: its brain generalized both as living. it is only after much training and learning that these two can be split between a dead puppet and a living being. and what makes the split is the child’s understanding that a puppet can’t move on its own. but the reason why the puppet can’t move on its own comes from the outside world: reality, through the laws of physics, provides that information permanently in every observation. its brain will then, if it’s not deceiving itself, correct its observations to generate a better observation of reality. i hope this is clear because this is essential to understand what will come next.

why is it naturally scientific (actually, i should say naturally philosophic)? let’s go through the process of a mind growing. the mind starts with mind M as it inherited from its ancestors (DNA, cells, etc). this mind M has both observations and actions (as i said before, they are both physical things). if M is faced with a new pattern p and observes and acts on it adequately, then M will prevail. if not, either M corrects itself into a fitter M’ or it will perish. this requires that M is permanently observing reality and testing its internal representation against reality. this is similar to the scientific method: bad ideas are discarded because they don’t work, but whether they work or not is verified by reality. i already provided a definition of the scientific method previously, but now it should become clearer. brains calculate a generative space through abstraction: 3 times {a,b} is a generative space because it can “create” via work a set bigger than itself. abstraction allows us to change the 3 into 4 or 5 and imagine new {a,b} worlds that don’t exist. this is a natural consequence of abstraction. the algorithm used to create the perfect fitting between the abstractions of reality and the observations of reality is the scientific method. its error is the measurement of scientific progress. let’s do a simple example to clarify.

consider miniverse R = {a,b,a,b,a,c}. two competing abstractions exist in minds M1 and M2. M1 = 3 times {a,b} and M2 = 3 times {a}, 2 times {b}, 1 time {c}. which is the one that fits the data best? we generate the extrapolated reality of each, RM1 = {a,b,a,b,a,b} and RM2 = {a,a,a,b,b,c}, and compare them with reality R. we can see that the first mind miscalculated one observation (b instead of c), so it has an error of ~17%. the second calculated all observations correctly. the best choice is, therefore, M2. my formulation of the scientific method is the search for the optimal abstraction whose generated observation space fits reality with 0 error. it is impossible, but an interesting goal. any animal whose understanding of reality is fitter than another will have an advantage over others, and therefore, will thrive. being scientific is no more than being very good at following our survival instinct.

the main point i’m getting at is, however, not how reality as a feedback is key to understanding minds, but actually, how minds themselves represent reality as generative spaces instead of static observations. as we saw above this makes sense if our observation resources are limited to a small quantity of data. it is easier to store the generative space of reality and generate it than to store reality itself. much like my example. but there is a problem. if you accepted my premise that brains store mostly generative spaces instead of real observations, then this means brains can generate realities that do not exist. the existence of brains creates extrapolated realities that do not fit reality. for example, i can imagine an extrapolated crocoduck from my abstractions of living beings, ducks and crocodiles. but this generated observation, when tested, is proven to be false as an existing animal. my abstractions of living beings, ducks and crocodiles are good scientific evidence tested by reality, yet i can generate equally easily completely invalid ones. how can we tell them apart? as i said, for survival reasons, animals with a good sense of “real” should survive better than animals without it. but if survival is out of the picture, then there is no way of stopping fantasy (non-scientific knowledge) from becoming the center actor of a brain’s activity. this hints at the subject of the coming posts. note that even a false idea resides in reality, so it is real only as far as the molecules sustaining its concepts are real. once extrapolated and tested and proved false, it should remain as a hypothetical tale and a proof of the insufficiency of generative spaces.

so far, these seemingly elaborate concepts have been given without any apparent goal. from here on i will begin to build on these concepts and analyze bigger and bigger systems. it’s been very boring, but this sets up the foundation on which i will work, so that ambiguity is removed.