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.

Semantic Web and Social Web: Similarities and Differences !

Though semantic web and relational ontology has become a theoretical discussion among many pioneers of world wide web, it is yet to receive wider traction among the industrial use cases of internet. Yet the concept remains widely discussed in the academic circles and the research groups of enterprises focusing on online data distribution and search engines. However, semantic web as a paradigm has some differences from a social web where we can collaborate better and more meaningful.

Semantic web need to go way beyond the constructs and ambitions of autonomic computing if it should become a gateway to web socialization. Internet  should evolve to be more open web where we can create meanings and relationships within first degree of separation itself. I believe it is still very much skewed by the way search engines, resource identifiers, memory allocation architectures, data storage, binary approximation algorithms etc.

Social web is a world where society weaves a web of information around their social relationships and social use cases. It stems from the real web of information. Semantic web will be a subset of social web in this sense. If semantic web is dealing with information enterprise, social web is dealing with collaboration circles.Machines can become self aware and understand what is the cognizance of information that they process. But in social web, socialization will be the first phase and then semantic relationships will emerge. web sociology will depend on the the social coefficient that exist in various degrees of separation and the order of 'connectedness' in each collaboration circles.

The business dynamics of web sociology will depend on the market dynamics of software engineering economy as well. We cannot think of semantic or social web isolating it from the market ecosystem or software economic premises. In terms of social activities, what all are happening in web?

• Collaboration
• Conversation
• Construction
• Deconstruction
• Inspired connectivity
• Virtual sensitivity
• Cognitive mind mapping
• Symbolic learning
• Forgetting and temporary memory
• Recognition
• De-cognition
• Identification
• Crowd behavior
• Media affinity
• Informed passivity
• Social solitude
• Virtual sentiments 

This list goes on. Web sociology will need to come up with new frameworks where all these activities need to be aligned in the sociological perspective. Thus semantic web can generated meaningful association between machines and men. Semantic web can definitely write and read relationships with human beings. Socialization is something beyond communication. It is always an attempt to represent the social self and personal self to a wider audience. Only social web can aggregate and spread more meaningfulness, relationships and cognition in the collaboration circles. 

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.

Unwind the Mind : Data Dynamics of Natural Computers

Data Diary (5): Natural Computers

Information space continues to enrich my imagination to new manifolds. The recent news that a new start up named Aysadi has come up with a Topological approach to machine learning and Big Data analytics, is really really an interesting conjecture. It is said that DARPA, NSF and Stanford were involved in this research for a long time. I hope this will be a good trend where we will have an open approach to data science. This should be beyond the dependency on specific tools. 

This inspired me to go through some nuances of topological learning and created lots and lots of questions in my 'unstructured' mathematical understanding. The stress on various 'in variance' conditions in topological analysis makes me believe that we are far from the best approach. A comprehensive approach to a data problem should not be defining a boundary to its explorations and insights. Yet my comment remains largely naive as I am not an authority or trained in topology. 

Continuing from our previous post on information - cognition   conjecture, I have landed on a cyclical condition. With the advent and advance of cognitive computing and neuroscience, we are creating anew computing machines driven by human cognition. So we can state that cognition can control computation and therefore information too. On the other side of the coin, can information control cognition. In simple terms the answer is yes, a plain yes. If so, can we create a cyclical information - cognition cyclical machine ? This should be a machine where cognition initiates information processing and then information processing generates new re-cognition. 

When I try to rationalize this order, I believe this is happening in all our day to day lively transactions. Going on the same lines, how many machines can claim to do this natural computing cycle to maximum approximation to the real world. And what is the most effective model to observe the data flow in this cognition - information - re-cognition cycle. Knowledge ( Neural Signals, Thought Processes ) in ( Cognition ) - (Language, Semantics, Syntax) in ( Information ) - ( Semiotics, Visuals, Shapes, Numbers, Senses, Emotions ) in Re-cognition seems to be data dynamics. Natural computing demands more rigorous modelling for data dynamics. In pursuit of more natural thoughts ... 

+Gokul Alex 

PS: Content is Social. Social is Me