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    nmw 19:04:09 on 2016/05/27 Permalink
    Tags: , , artificial languages, , , cognitive, , , , emerge, , human intelligence, , , , , , , , , , , , , , , , , , traing set, traing sets, , , , ,   

    Literacy and Machine Readability: Some First Attempts at a Derivation of the Primary Implications for Rational Media 

    Online, websites are accessed exclusively via machine-readable text. Specifically, the character set prescribed by ICANN, IANA, and similar regulatory organizations consists of the 26 characters of the latin alphabet, the „hyphen“ character and the 10 arabic numbers (i.e. The symbols / zyphers 0-9). Several years ago, there was a move to accommodate other language character sets (this movement is generally referred to as „Internationalized Domain Names“ [IDN]), but in reality this accommodation is nothing more than an algorithm which translates writing using such „international“ symbols into strings from the regular latin character set, and to used reserved spaces from the enormous set of strings managed by ICANN for such „international“ strings. In reality, there is no way to register a string directly using such „international“ characters. Another rarely mentioned tidbit is that this obviously means that the set of IDN strings that can be registered is vastly smaller than strings exclusively using the standardized character set approved for direct registration.

    All of that is probably much more than you wanted to know. The „long story short“ is that all domain names are machine readable (note, however, that – as far as I know – no search engine available today on the world-wide-web uses algorithms to translate IDN domain name strings into their intended „international“ character strings). All of the web works exclusively via this approved character set (even the so-called „dotted decimals“ – the numbers which refer to individual computers [the „servers“] – are named exclusively using arabic numerals, though in reality are based on groups of bits: each number represents a „byte“-sized group of 8 bits… in other words: it could be translated into a character set of 256 characters. In the past several years, there has also been a movement to extend the number of strings available to accommodate more computers from 4 bytes (commonly referred to as Ipv4 or „IP version 4“) to 6 bytes (commonly referred to as Ipv6 or „IP version 6“), thereby accommodating 256 x 256 = 65536 as many computers as before. Note, however, that each computer can accommodate many websites / domains, and the number of domain names available excedes the number of computers available by many orders of magnitude (coincidentally, the number of domain names available in each top level domain [TLD] is approximately 1 x 10^100 – in the decimal system, that’s a one with one hundred zeros, also known as 1 Googol).

    Again: Very much more than you wanted to know. 😉

    The English language has a much smaller number of words – a very large and extensive dictionary might have something like 100,000 entries. With variants such as plural forms or conjugated verb forms, that will still probably amount to far less than a million possible strings – in other words: about 94 orders of magnitude less than the number of strings available as domain names. What is more, most people you might meet on the street probably use only a couple thousand words in their daily use of „common“ language. Beyond that, the will use even fewer than that when they use the web to search for information (for example: instead of searching for „sofa“ directly, they may very well first search for something more general like „furniture“).

    What does „machine readable“ mean? It means a machine can take in data and process it algorithmicly to produce a result – you might call the result „information“. For example: There is a hope that machines will someday be able to process strings – or even groups of strings, such as this sentence – and be able to thereby derive („grok“ or „understand“) the meaning. This hope is a dream that has already existed for decades, but the successes so far have been extremely limited. As I wrote over a decade ago (in my first „Wisdom of the Language“ essay), it seems rather clear that languages change faster than machines will ever be able to understand them. Indeed, this is almost tautologically true, because machines (and so-called „artificial intelligence“) require training sets in order to learn (and such training sets from so-called „natural language“ must be expressions from the past – and not even just from the past, but also approved by speakers of the language, i.e. „literate“ people). So-called „pattern recognition“ – a crucial concept in the AI field – is always recognizing patterns which have been previously defined by humans. You cannot train a machine to do anything without a human trainer, who designs a plan (i.e., an algorithmic set of instructions) which flow from to human intelligence.

    There was a very trendy movement which was quite popular several years ago that led to the view that data might self-organize, that trends might „emerge from the data“ without needing the nuissance of consulting costly humans, and this movement eventually led to what is now commonly hyped as „big data“. All of this hype about „emergence“ is hogwash. If you don’t know what I mean when I say „hogwash“, then please look it up in a dictionary. 😉

     
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    nmw 17:23:31 on 2014/11/02 Permalink
    Tags: , cognitive, , , , , , , , map, mapping, maps, , metaphors, , , , , , , , , , , , territory, topological, topology,   

    Topological Maps: Don’t Even Go There — unless, perhaps, you wish to mention the exceptional case in which the map *IS* the territory 

    One of my friends since many years (who has taught me a lot about the way people might think or perceive stories, digest experiences, come to understand their life, etc.), namely Jean Russell, has posted some interesting remarks about the metaphors she uses (at times, I guess) to understand “the Internet”:

    So much of our experience of computers and the internet in the last 50 years has been disruptive. People didn’t know they wanted it, didn’t know what it was or what it did. And when one introduces such things, we use metaphors to bridge from the familiar to the new. Your domain is like your home. Your Home Page. Email is like mail but sent over the computer.

    And along with these metaphors come a set of protocols and expectations. If I buy a domain as a home, then I don’t expect other people to have control there. I am responsible for keeping it tidy and inviting other people there. I can get a prefab home or make one myself.

    And these are all really helpful ways of using metaphors to help a new disruptive innovation gain traction in the world.

    However, if we want to BE disruptive in our innovation, we want to look for a different kind of metaphor. Websites are not just like homes, they have some features that homes do not and lack some features that homes have. If we use models of the familiar in creating our innovations, we aren’t likely to be very disruptive at all.

    For many years now, I have attempted to drum home the distinction between domains (addresses, web sites, virtual land, etc.) and websites (the HTML and similar “content” built up on top of the virtual property [note that I view all of the content -- whether it is considered "artificial language" or "natural language" -- as content; some people consider parts of this content -- e.g. HTML coding, especially "metatags" and such -- to be something other than content... I'm not exactly sure what, but they apparently consider it to be special in some way]).

    Yet whether land or property or building or whatever virtual real estate analogy, all of the above do not draw attention to one of the most noteworthy differences between domains and “real world real estate”: When it comes to information, the map may in fact actually be the territory!

    Think about it: When you think of an elephant, do you think of the elephant as an astronaut? Or perhaps climbing the Empire State Building? I would say that before having read those two suggestions, you probably hadn’t thought of elephants that way. You might have thought of elephants standing, eating, sleeping, … — but probably not writing computer programs. Your experiences of elephants have probably included things your brain associates with such concepts as “stand”, “eat”, “sleep”, etc. and when you think of an elephant, you may very well be inclined to also think of such topics. Perhaps thinking of “eating” might even motivate you to get up and get something to eat (see also “Words as Puzzle Pieces“).

    Words describe elements of relationships. We cannot think about sleeping without thinking of whatever thing that is sleeping. Some things sleep while standing; other things sleep while lying down on mattresses, with pillows, in beds. The things which your mind conjures up with any particular word may very well have more to do with the way your mind works than it has to with anything in the “real world”. Much like you may associate a certain fragrance with an early childhood experience which might somehow be linked to that scent, you may also associate concepts with each other based on how your cognitive map has stored linked or related concepts.

    Perhaps one of the great challenges for creating information retrieval systems that are able to “disrupt” the status quo is how to make it easy for people to distinguish between information sources that are about “cooking food” (versus e.g. “cooking the books”) and information sources that are about “buying food” (versus e.g. “buying a video game”). “Food” by itself is only a beginning: It is but one piece of a puzzle that needs to match up with other concepts. Bringing these concepts together to build a story from these elements will probably involve building complex networks that are not necessarily related by “real world” proximity. Thinking about information as if it had something to do with the way paper books have traditionally been stored on shelves is a sure-fire way to miss the boat.

     
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