25 Oct Chaos and Order, Fractals and Language Power

Fractals may appear chaotic when viewed from a distance, but they exhibit recognizable patterns or mirrored structures when viewed up close. So, too, there is a distance we humans must travel from the chaotic structure of a thought to the regular structure of a meaningful dialog made of symbols in the form of audible words and body language.

Fractals mimic the complexity of natural phenomena because they are built on the mathematics of self-similarity with minor variations. Yet fractals are meaningless beyond demonstrating the beauty of mathematics. Do meaningful things possess the same apparently chaotic properties that resolve into order? How can we analyze the chaos and order of communication (and cognition) to find out what is needed to deliver understanding? Decompose the problem into its core elements and model how they interact with each other on a distributed basis, then reassemble the pieces in such a way as to achieve similar results.

Distributed Components Of Understanding

One of the reasons existing efforts at automated language understanding have yielded unsatisfactory results, is that they have focused on some of the core components, such as syntax and semantics, without modeling or accommodating other core components such as context. Imagine the fractal shown here with only the parts with one or two of the colors.

The affect of cutting out pieces would be remarkably different, and probably not as appealing or evocative, but you would still get the idea. With language, I think it is the same, but different: some words and phrases, in isolation, are evocative enough to deliver a level of understanding independently. But the power of human language comes through best when you get all the components. Furthermore, I believe there is a minimum set of language components – a threshold beneath which it is impossible for a human or a machine to derive the intent of the speaker or writer. As you might guess from the name of this blog, I think that the component of context is above that threshold: without it, the likelihood of correctly understanding meaning drops precipitously in many cases.

Both cognition and language understanding are complex with chaotic components, like fractals, that resolve when they all come together into usable bundles.

Understanding Context Cross-Reference
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Section 7 #6

Table of Context

The coming together into usable bundles is where the power lies. The distributed components of language, words, symbols, syntax, semantics, morphology, body language and context, in isolation are interesting but weak. The power of these elements combined creates understanding. Understanding is a very powerful force in the shaping of our society. Mutual understanding is the basis of mutual prosperity. The opposite is powerful too – but most often in a destructive way. Because the brain activity that binds these symbolic elements together consists of a convergence of distributed electrical impulses, I would like to draw a parallel.

From Impulse To Intent

From the myriad electrical signals created through sensory perception, especially seeing and hearing, to the corresponding activity in the coordinating centers and reasoning centers of the brain, somehow we are able to sort it all out and derive “intent”. Some describe consciousness as a “bubbling up” where, amidst all the noise and confusion, meaningful stimuli win the competition and rise to the level of an impression that prompts attention and consideration. From a very young age, we learn that acting on these impressions leads to favorable outcomes and we get better and better at sorting through the noise and intentionally attending to that which is most likely to produce outcomes that make us happy.

For animals and other organisms lower on the chain, the brain activity comes together in similar ways, and they have the innate capacity to follow the things they interpret as desirable, such as aromas, that lead them to happiness. Some advanced animals, and possibly plants, demonstrate the ability to communicate with others in their proximity to achieve some level of mutual understanding. But these capabilities are usually limited to the moment and the gratification of immediate desires. What they don’t have is the expansive power of human language to generate long-term happiness and prosperity.

The coming-together power of language, then, extends the coming-together of electrical impulses in the brain to produce understanding: a basis for intentional action. The power of understanding, added to the power of decision, has long-term impact in the happiness and prosperity of the individual. Add communication to the mix, and this power of prosperity and happiness extends beyond the individual to the entire proximal population of individuals who share the same symbol set (language). The proximity constraint transcends geography through long distance communication. So humans can easily communicate to collaborate for the greater good anywhere in our solar system. And who knows how far telepathic communication, such as prayer, may extend.

In human language, what are the components that come together? I named some components of language above: words, symbols (such as phonetics and orthography), syntax, semantics, morphology, body language and context (pragmatics). I think there are more than these, but this is a great starting place. Because it is the coming together process that is of most interest, we care as much about the junctions or boundaries between these components, structures or strata of language as we care about the components individually.

It is often daunting to analyze the individual components of a complex distributed system. We may say that the “butterfly wings displacing air molecules in China may affect the formation of a tropical storm in Bermuda,” but tracing the impact from beginning to end is impossible. I think that even with advances in brain imaging, we are also far away from being able to map the pathways of electrical impulses in the brain from the incoming stimuli to understanding intent. In some respects, we may be closer to being able to model and replicate the final steps of this process in which humans analyze the symbols in context, and synthesize an understanding of intent.

When I began designing my approach to natural language understanding, using a massive distributed ontology of context-based knowledge, I had only a vague inkling of the power of distributed system modeling. Now that I look back at my findings I see a converging path between my study of the way brain activity leads to the ability to reason about input stimuli, and the way advanced reasoning processes tie together all the components of language to enable us to understand one another. In retrospect, this harmonic convergence between language and cognition is, in itself, a key to modeling processes that will enable computers to mimic complex brain tasks despite the inherent structural differences between neurons and CPUs. I’ll explain the model further in upcoming posts.

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