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Posts Tagged ‘knowledge’

The edges of existence.

Everything is an edge – an edge of an edge – an edge of an edge of an edge. Existence is an infinite regress of edges encoding, decoding and recoding other infinite regressing edge networks. The explanations for the unexplained, even in their simplicity, are infinite regresses.

A dictionary is a book of words defining words. Where does a definition end?

Human language is a loose collection of rules to be excepted and exceptions to be ruled by effect. If a communication communicates it’s acceptable?

Sensory perceptions and the instruments of perception cannot be fully perceived. Are we to believe our eyes about our eyes?

Mathematics and its objects and relations are designed to perfectly articulate all that is the case and yet hiding with infinity are infinities and transcendentals that cannot be defined, systematically discovered, nor hardly described. (http://vihart.com/transcendental-darts/)

Our science modernized from the mystics (Kepler) and numerologists (Newton) and the faithful (Leibniz) strikes out, pathetically, against leaps of faith. This science likely has led to the heating of the planet via industry which now can only be reversed by more science?

Turing conceived computers to mirror the way humans thought – conceived when our collective knowledge of brains was rather small. Ironically, within a few lines of code computers (theoretical and physical) become nearly inscrutable in terms of what they might do. Are more inscrutable machines required to create and understand more inscrutable machines?

Currency is abstracted not just from physical objects but from any tangible value other than a sustained believe that this $ will be understood and honored by some anonymous entity beyond oneself. The beliefs sustained by what most label as “the dismal science” (economics) and its backer, the state.

The desired progress of all of the above can be summarized as “prediction”. If something is predictable it is controllable is the underlying point of most modern obsessions with science, technology and information. Even though our most precise and abstracted efforts have shown prediction, by in large, is impossible. Not just for complex systems of the natural world but the very simple mathematical objects we create. https://www.youtube.com/watch?v=sHYFJByddl8

Despite all the empirical evidence over hundreds of thousands of years and the theoretical proofs of the 20th century as a whole, our culture – primarily in the US but spreading elsewhere – simply refuses to give up control through prediction. It persists, likely, because we are limited beings in energy and time and need whatever perceived advantage we can get. Right? Seeming identification of a pattern reinforces that identification when paired with the perception of reward or advantage. That is learning itself is an edge of an edge of an edge and fully infinitely regressive to its own contradiction.

Prediction and learning and control are all about probability. For a prediction to be useful it must tell us something about the probability of conditions coming to be. For us to do something based on a prediction we must believe that prediction to be as accurate at least as much as the probability of events it predicts. That is, our beliefs should only be as strong as the probability predicted. Or so logic would suggest. However, probability itself turns out, with no surprise here, to be an infinite regress. Probability is really a statement about lack of information. (Sure some people argue that chance/randomness is implicit to existence while others say it’s an artifact of our limited perceptions. In either case our ability to say anything about the existence of things comes down to ignorance and the infinite regress of existence.)

This information remains forever out of reach. It is both at the heart of everything and is the edge of everything. We cannot know. We can only play with these edges, find more of the edges, recode edges into edges. Our struggles philosophically, scientifically, spiritually and educationally come down to this straightforward non-fact. Should we continue our answer and prediction seeking efforts in spite of their impossible hope? That is a personal question that each will have to answer over and over for themselves. For me, I will, not so I can be right or in control, but because I enjoy the edge want to live outside of control. I paint to paint, not because the painting says something about reality. “The good life” is proportional to the number of edges explored, clanged to, jumped from, thrown away, revisited, and combined.

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Who’s Free and Who Isn’t (this is about artists) aka who should we pay aka The Free and Anti Free

An N+1 article grapples with the basic question, “should an artist get paid?” https://nplusonemag.com/issue-20/the-intellectual-situation/the-free-and-the-antifree/ It’s a decent question – that is, we ought to ask it and keep asking it. This article and so many like it definitely don’t answer it. The article isn’t poor quality. It’s thought provoking in that way where you want to chase down a lot of things. I don’t want to pick apart the article right now as it gets into class warfare, lots of history, media critique and more. I want to attack the question head on without the backdrop of all this other stuff – which I think is mostly just baggage.

The most important point of all is sort of an anti-point in life and existence. That is, none of us, nothing (no thing), deserves anything. The title of the article introduces the concept of SHOULD which is really just a form of “deserves.” Human language will never do this point justice but there isn’t any universal SHOULD. There are proximal “SHOULDS.” In some cultures, in some relationships, in some situations X SHOULD HAPPEN.

So no, artists shouldn’t get paid.

In this proximal culture, this western world of award shows and museums and ad agencies and media companies, should artists get paid? maybe. If they want to get paid they figure out how. Should they be paid a priori because they are doing art? maybe.

Why the waffle?

Really this question is bigger than art and artists. Does anyone deserve to get paid? should ___________ get paid? No. No, no category of people, persons, trades, classes SHOULD get paid. Do they? yes. Yes, some people get paid. Some categories of people with categories of skills get paid.

This distinction matters because there are FAR MORE starving manual laborers than starving artists. There are FAR MORE starving students than artists. There are a lot of starving people on the planet. There are lots of species of animals in very bad shape. The subclass of artists that are humans that are part of species on the earth is hardly unique.

what is art? that’s a relevant question at this point. It doesn’t matter. what is science? what is math? what is teaching? what is philanthropy? what is a job? what is work? Should WE Get Paid?

what do you do? Why do you think you should get paid? If you are a teacher you most likely have decided that you’re doing a good for society. If you are a social worker, same. If you are a business person you probably think you are “adding value” to the company or marketplace. Most likely you think you should get paid because those around you and those before you who did vaguely similar things think you should get paid and those people often “hire” those similar to them.

and then there’s art (and its cousins…) Art is the stuff that changes perceptions. Art is the concepts and ideas and forms and tools that don’t make sense. Pay only comes from a marketplace. Art is anti-market. It has an anti-market if it has anything at all.

So n+1 and those that worry about these sorts of things you can stop wondering if someone will pay or if capitalism or whatever category of being we’re talking about will assign commodity-based currency quantification to something that seeks, at its core, to never be defined.

Art and artists should forever seek ill definition, that’s the fucking point. If there is a point at all.

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Previously I made this set of statements:

Computation irreducibility, the principal (unproven), suggests the best we are going to be able to do to understand EVERYTHING is just to keep computing and observing. Everything is unfolding in front of us and it’s “ahead” of us in ways that aren’t compressible. This suggests, to me, that our best source of figuring things out is to CREATE. Let things evolve and because we created them we understand exactly what went into them and after we’re dead we will have machines we made that can also understand what went into them.

This is a rather bulky ambiguous idea without putting some details behind it. What I am suggesting is that the endless zoological approach to observing and categorizing “the natural world” isn’t going to reveal path forward on many of the lingering big questions. For instance, there’s only so far back into the Big Bang we can look. A less costly effort is what is happening at LHC, where fundamental interactions are being “created” experimentally. Or in the case of the origin of life, there’s only so much mining the clues of earth and exoplanets we can do. A likely more fruitful in our lifetime approach will be to create life – in a lab, with computers and by shipping genetic and biomass out into space. And so on.

This logic carries on in the pure abstraction layers too. Computational complexity studies is about creating ever new complex systems to then go observe the properties and behaviors. Mathematics has always been this way… we extend mathematics by “creating” all sorts of new structures, first we did this geometrically, then logically/axiomatically, and now computationally. (I could probably argue successfully that these are equivalent)

All that said, we cannot abandon observation of the world around us. We lack the universal scale to create all that is around us. And we are very far from exhausting all the knowledge that can come from observation of what exists right now. The approaches of observation and creation go hand in hand, and for the most important questions it’s required to do both to be anywhere close to certain we’re on the right path to what might actually be going on. The reality is, our ability to know is quite limited. We will always lack some level of detail. Constant revision of the observational record and the attempt to recreate or create new things we see often reveals little, but critical details we miss in our initial assessments.

Examples that come to mind are Bertrand Russell’s and Whitehead’s attempt to fully articulate all of mathematics in Principia Mathematics. Godel undid that one rather handedly with his incompleteness theorem. More dramatic examples from history include the destruction of the idea of a earth centered universe, the spacetime curvature revelations of Einstein and Minkoski, and, of course, evolutionary genetics unraveling of a whole host of long standing theories.

In all those examples there’s a dance between observation and creation. Of course it’s way too clean to maintain there’s a clear distinction between observing the natural world and creating something new. Really these are not different activities. It’s just a matter of perspective on how where we’re honing our questions. The overall logical perspective I hold is that everything is a search through the space of possibilities. “Creation” is really just a repackaging of patterns. I tend to maintain it as a different observational approach rather than lump it in because something happens to people when they think they are creating – they are more open to different possibilities. When we think we are purely observing we are more inclined to associate what we observe with previously observed phenomenon. When we “create” we’ve already primed ourselves to look for “new.”

It is a combination of the likely reality of computational irreducibility and the psychological effect of “creating” and seeing things in a new light that I so strongly suggest “creating” more if we want to ask better questions, debunk false answers and increase our knowledge.

 

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There’s a great deal of confusion about what is meant by the concept “computational knowledge.”

Stephen Wolfram put out a nice blog post on the question for computable knowledge.  In the beginning he loosely defines the concept:

So what do I mean by “computable knowledge”? There’s pure knowledge—in a sense just facts we know. And then there’s computable knowledge. Things we can work out—compute—somehow. Somehow we have to organize—systematize—knowledge to the point that we can build on it—compute from it. And we have to know methods and models for the world that let us do that computation.

Knowledge

Trying to define it any more rigorously than above is somewhat dubious.  Let’s dissect the concept a bit to see why.  Here we’ll discuss knowledge without getting too philosophical.  Knowledge is concepts we have found to be true and that we somewhat understand the context, use and function – facts, “laws” of nature, physical constants.  Just recording those facts without understanding context, use, and function would be pretty worthless – a bit like listening to a language you’ve never heard before.  It’s essentially just data.

In that frame of reference, not everything is “knowledge” much less computational knowledge.  How to define what is and isn’t knowledge… well, it’s contextual in many cases and gets into a far bigger discussion of epistemology and all that jive.  A good discussion to have, for sure, but will muddy this one.

Computation

What I suspect is more challenging for folks is the idea of “computational” knowledge.  That’s knowledge we can work out – generate, in a sense, from other things we already know or assume (pure knowledge – axioms, physical constants…).  Computation is a very broad concept that refers to far more than “computer” programs.  Plants, People, Planets, the Universe computes – all these things take information in (input) one form (energy, matter) and converts it to other forms (output).  And yes, calculators and computers compute… and those objects are made from things (silicon, copper, plastic…) that you don’t normally think of as “computational”… but when configured appropriately they make a “computer”.   Now to get things to compute particular things they need instructions – (we need to systemitize… or program it).  Sometimes these programs are open ended (or appear to be!).  Sometimes they are very specific and closed.  Again, here don’t think of a program as something written in Java.  DNA is an instruction set, so are various other chemical structures, and arithmetic, and employee handbooks… basically anything that can tell something else how to use/do something with input.  Some programs, like DNA, can generate themselves.  these are very useful programs.  The point is… you transform input to some output.  That’s computation put in a very basic, non technical way.  It becomes knowledge when the output  has an understandable context, use and function.

Categorizing what is computational knowledge and what is not can be a tricky task.  Yet for a big chunk of knowledge it’s very clear.

Implications and Uses

The follow on question once this is grokked — What’s computational knowledge good for?

The value end result, the computed knowledge, is determined by its use.  However, the method of computing knowledge is valuable because in many cases it is much more efficient (faster and cheaper) than waiting around for the “discovery” of the knowledge by other methods.  For example, you can run through millions of structure designs using formal computational methods very quickly versus trying to architect / design / test those structures by more traditional means.  The same could be said for computing rewarding financial portfolios, AdWords campaigns, optimal restaurant locations, logo designs and so on.  Also, computational generation of knowledge sometimes surfaces knowledge that may otherwise never have been found with other methods (many drugs are now designed computationally, for example).

Web Search

These concepts and methods have implications in a variety of disciplines.   The first major one is the idea of “web search”.  The continuing challenge of web search is making sense of the corpus of web pages, data snippets and streams of info put out every day.  A typical search engine must hunt through this VERY BIG corpus to answer a query.  This is an extremely efficient method for many search tasks – especially when the fidelity of the answer is not such a big deal.  It’s a less efficient method when the search is really a very small needle in a big haystack and/or when precision and accuracy are imperative to the overall task.  Side note: Web search may not have been designed with that in mind… however, users come more and more to expect a web search to really answer a query – often users mistake the fact that it is the landing page, the page that was indexed that is doing the answering of a query.  Computational Knowledge can very quickly compute answers to very detailed queries.  A web search completely breaks down when the user query is about something never before published to the web.  There are more of these queries than you might think!  In fact, an infinite number of them!

Experimentation

Another important implication is that computational knowledge is a method for experimentation and research.  Because it is generative activity one can unearth new patterns, new laws, new relationships, new questions, new views….  This is a very big deal.  (not that this has been possible before now… of course, computation and knowledge are not new!  the universe has been doing it for ~14 billion years.  now we coherent and tangible systems to make it easier and more useful to use formal computation for more and more tasks).

P.S.

There are a great many challenges, unsolved issues and potentially negative aspects of computational knowledge.  Formal computation systems by no means are the most efficient, most elegant, most fun ways to do some things.  My FAVORITE example and what I want to propose one day as the evolution of the Turing Test is HUMOR.  Computers and formal computation suck at humor.  And I do believe that humor can be generated formally.  It’s just really really really hard to figure this out.  So for now, it’s still just easier and more efficient to get a laugh by hitting a wiffle-ball at your dad and putting it on YouTube.

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and more where that came from here.

and even more to investigate. (not Bronowski)

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