What ‘light loving moths’ forgot to tell you about ‘experience design’

‘Light loving moths’ and the world of design?, why this weird comparison! Yes, you may be thinking for a while. As we know there is a common belief that moths, flies, beetles are attracted towards light and that leads them to their unfortunate miseries at the candle light. When we understand that it is by their sheer inability to understand the circular light emitting behavior of the artificial light, these little insects fall prey to the candle fire and other hazards caused by artificial sources of light. Those insects are actually in search of the moonlight that has a constant angle of radiation. 

This occurrence in nature often reminds me about the hypes of nurturing and manufacturing customer experience design. Primarily design has two layers; one physical and second ideological. When physical layer of design act as the necessary act to optimize the functional and material aspects of the subject matter or the object manifestation, ideological layer act as the force that can create a demand or customer appeal beyond its actual capacities. Hence when we make all sort of fuzz about design, design thinking, truth in design we should understand the layer in which we are applying our design efforts and logic.

Big Data is not born in a Day !

Adieu to an Algorithmic Age::

Intelligence every where! Sensors every where! Data seems to be liberated from all the corners of universe. One may wonder, where was all these exabytes and petabytes of data hidden. Or is this internet universe that spawn monsters of data from nowhere? 

Absolutely not. Big Data is not born in a day ! This rich collection of data that we see accumulating in minutest measures of seconds was nothing but encapsulated in the abstractions of an algorithmic age. 

A Critique of Classical Computational Models:

In the scientific computing community, there is an emerging realization that we are moving ahead of an algorithmic age to an age of intelligence and adaptation. We emulate more and more the natural cognition. Data structures have grown beyond the linkages and contextual affinities of algorithms. We may look back for a while. What were we doing with information spaces all these while? Data structures in various information spaces where conditioned by a logic that could approximate a mathematical behavior.

In a way , Boolean logic is largely a mathematical behavior or an operational approximation for the convenience of calculation. Mathematical behavior can be modeled by approximations. Thus we created concept machines based on Boolean logic.Mathematical behavior or for that matter any behavioral logic is just the reflection of a larger set of conditions. If we look from a higher abstraction, we were trying to fix the multidimensional data structures into a partial projection of Boolean logic. Thus my argument is that Boolean logic was insufficient to capture the computational complexities of data streams.

Data Structures and Dialectical Logic

Then what is inherent in data structures: in reality, do they exist at all? I am not an exponent enough to lay down my arguments in mathematical formats. However, I may try in terms of dialectical logic here. Data as such is a representation of a physical entity or a cognitive process. It may have its own primary structure or it may be derived or dependent on a much more relatively invariant structure.  This integral between representational mathematical format ( symbol ) and the corresponding primary or secondary structure constitute a data structure. This visualization is in terms of dialectical logic. 

In the measures of physics, we may use the metrics of space time in many models. But when the data streams are dependent or realizations of energy structures the scenario becomes more interesting. The high energy physics and the future smart sensors may become the sources of such #energy structures. As the data explosions gains momentum, we may need inner eyes to capture the innate structures behind the 'Big Data' ecosystem.

Some Early thoughts on the Boundaries of Circuit Computing

Computational machines, their digital senses are hovering are all around us. We make decisions, we experience and negotiate, we travel and triangulate through their prowess. In other words, we as a collective of social knowledge embed intelligence and knowledge into their circuitry and digital logic. Their arises my question; how is this digital logic grounded in the larger set of mathematical logic. Is this digital logic a linear crystallization of natural computing algorithms? 

As we reach the boundaries of traditional algorithms of natural data set, the emerging frontiers of decision sciences are appearing as the phantom ghosts from nowhere. And at times we think they are coming from nowhere in the past and we call them data explosion and sensor revolution. Where has internet hidden all these Phantoms from the past.

Recently I read that mathematical logic has a lot of formalism derived from the brilliant approaches of the 20th century mathematician Hilbert, who tried to consolidate many of the mathematical problems under the ambit of a unified theory of mathematical theory. Thus we need to understand the formalism and whether their were some limitations on the approaches in the problem solving approaches of Hilbert methodology. 

Why all this question now? It is because of the very reason that the theoretical computational models need to take a new turn as we are seeing cognitive computing as the future approach to the decision making algorithms of future needs. When we shift the gears from circuit computing to cognitive computing, there must be a realization of the underlying mathematical logic and its complexity inherent. Our investigations must begin at the very root of the mathematical logic which derived its powers from the formalist approach. 

+Gokul Alex