Barcode example

Finding the relationship between bar patterns and a decimal subscript will be used to illustrate knowledge discovery through animation. The assumptions made are that the AI has access to image samples and that the memory beliefs of basic math and software primitives exist, as do the fundamental instantiation and grading abilities previously outlined.

Human cognition evolved to integrate 3d objects, environments and behaviors into a knowledge hierarchy - not 2d symbolic abstractions. It takes a great deal of effort and training for a human mind to so contort itself as to be able attempt these classes of problems. But with persistence, the help of external tools like pen, paper, calculator and computer, together with a little academic 'coercion' -and we are sometimes rewarded with results.

The method of discovery does not need to be infallible or super efficient, it just needs to have a statistical chance of success in finding connections and thus guide knowledge formation within the time allocated. The higher goal, as always, is to discover meaning through finding memory connections, joining means with ends and reducing mystery. In this case, the means is a barcode image, the ends - a decimal subscript number. A simulator deals primarily with object shapes and forms. Apart from drawing upon prior memorized beliefs in the form of animated scripts or static image relationships, there are fundamental 'instructions' operating on those forms:

And grading machinery based on:

These processes are fast, automatic and operate in layers through reversible animated pipelined scripts. Humans use pen and paper to 'fix' parts of these flows to create order and permanence out of these somewhat chaotic streams. This helps construct an external framework to guide the process. AI will have the ability to do this internally by way of 'persistent' simulation layers. ^11-

Each process is essentially dumb and automatic, but as a whole, and connected to sufficient source material and memory support, new connections can usually be found and integrated into memory. Dead end simulations will fade away and if grading progress stalls, higher level processes will kick in - overall goal re-appraisal; seek more real world data through the modalities or widen the internal associative memory search.

Applying instantiation to the global barcode image would yield six classes of abstract objects; two rectangle shapes and four numeric digit shapes. Language attachment to the object instances would connect as thick and thin bars and the four digits as a number.

At the 'ends' part of the problem, we have a number 1234. Memory references will recall a belief that numbers have 'an equivalence to' binary 1's and 0's. The first script trial might show an ascii equivalence yielding 8 bits per character. Thus an image of 1234 transforms to 32 digits. A second script layer might show each separate digit converted to a simple binary count. The third has the whole decimal number, 1234 represented by a binary count. Of the three scripts, simple pattern recognition would grade binary expansion as the closest match between means and ends. Further sample barcode image trials would confirm the link. Memory formations of the newly discovered script sequences would follow, including mutual pointers between the existing precursor knowledge records of decimal to binary equivalence etc. (Which incidentally, would reinforce the familiarity and trust in those prior beliefs)

Now, when presented with similar barcode images, the scene will be recognized and will draw from memory links to the newly formed animation scripts and an intimate familiarity with the scene will ensue due to these very same memory references, together with the emotional confidence that comes from recognition and understanding. The fundamental simulator operations used in this example of discovery were:

Scene instantiation - to shape primitives
Language tagging - from memory recognition of images/forms
Prior memory associations - decimal to binary equivalence (as animation or belief)
Object substitutions - bar shapes to thick / thin or to 1's and 0's
Image comparisons - the bit patterns

The process of decoding the barcode will not be understood in some isolated abstract way, but within the known framework of reality through intimate linkages with existing memory records; all being a part of a world knowledge and environment map. If a barcode is now presented with no number or vice versa, the simulation can play the script in forward or reverse to discover the missing parts through simulation to final substitution of bar patterns or decimal digits.